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Featured
12 min read
The One Model Team
In 2015, The New York Times reported that among S&P 1500 companies, there were more CEOs named John than there were women. That stat struck a nerve—and since then, similar analyses have shown that Davids have also outnumbered women in top leadership roles across the Fortune 500. The comparison was cheeky. But it wasn’t just a quirky stat—it was shorthand for a deeper truth: diversity in leadership was nowhere near where it should be. Fast forward to today, and while the numbers have improved, they still make the case: only 10.4% of Fortune 500 CEOs are women. That’s progress—but not parity. The real issue isn’t about names. It’s about systems and the secrets they contain: -Why do some candidates never make it past the first interview? -Why does representation stall at certain levels, even when promotion rates are equal? These aren’t just philosophical questions—they’re People Analytics challenges. Turning Awareness into Action Headlines grab attention, but numbers drive change. The companies making real progress on DEI aren’t just tracking how many women they’ve promoted. They’re building systems that let them see exactly where representation drops off—and why. They’re setting KPIs that go beyond compliance. They’re giving HR and DEI teams tools to act on the data. (Vogue Business) For instance, Neiman Marcus Group (NMG) has implemented a strategy called "bias interruption," which addresses systemic biases in hiring, engagement, and retention. This approach utilizes data-driven practices to fix biases in hiring, employee engagement, and retention by understanding how certain practices deter candidates and employees of color. As a result, NMG surpassed its 2025 goal of having over 21% racial and ethnic diversity in leadership roles, achieving 21.4% in 2023. The new target is 28% by 2030. (Vogue Business) This example illustrates that DEI isn’t just about ideals. It’s about data. And if you're not measuring what matters, you're just guessing. DEI Data in Action: One Model + Company X At One Model, we’ve seen firsthand how organizations can go from intention to impact by turning diversity into something measurable—and actionable. Consider a Fortune 100 financial services organization we'll call Company X. When they partnered with One Model in 2016, they weren’t just looking for reporting tools—they were looking for visibility, accountability, and a way to actually move the needle on workforce diversity. Here’s what that looked like: Rescuing and Rebuilding Data Company X was transitioning from SAP to Workday and had a goldmine of workforce survey data at risk of getting lost in the shuffle. Using One Model, they recovered and normalized that legacy data across multiple systems, enabling the team to report from a single, cohesive source. This meant they didn’t have to redeploy a massive survey—and their DEI metrics didn’t reset to zero. Spotting the Gaps Others Miss As the team reviewed self-ID data, they found that a significant number of employees had skipped over racial/ethnic identification entirely. This wasn’t just a reporting glitch—it was a missing chunk of reality. With One Model, they were able to isolate this group and launch internal processes to close the gap. Within months, they had identified 95% of those employees—restoring accuracy to their diversity picture. Building Real KPIs Around Hiring Equity They took it a step further by setting department-level DEI hiring targets. Teams that met or exceeded their goals were studied—their processes, outreach, and interview practices documented and shared org-wide to replicate success. The result? DEI stopped being “a nice idea” and became part of performance strategy. Turning Interview Stage Data into Promotion Equity One of the most revealing insights came from interview stage analytics. Although promotions were evenly split—55% of internal promotions were women—managerial roles were still male-dominated. Digging deeper, they discovered that female candidates were only making it to the final interview stage 50% of the time. That bottleneck became a turning point. Company X created a new KPI: ensure that 80% of female managerial candidates reach the final interview round. They began testing different interventions—changes to panel composition, structured scoring rubrics, recruiter training—and tracked all of it in One Model to measure what actually worked. Tracking and Monitoring Changes Company X wanted more visibility into why females had a lesser presence in managerial roles within the organization. While, male to female promotions were equal. (This past year, 32 people were promoted. 55% of promotions (16 people) were women), there were significantly more males than females in managerial roles. Upon reviewing the data, they learned that out of the company’s requisitions, females applicants only made it to certain stages within the interview process (namely, an in-person interview) 50% of the time. Half the time, the only applicants that made it to a particular stage were male. They determined a hypothesis surrounding a particular KPI - that if more females made it to this particular stage, the odds were higher that more females would fill these roles. Company X set a goal that they wanted a female candidate make it to a manager interview stage 80% of the time. They are testing different methods on how best to achieve this, and with One Model's help, they are able to measure the effectiveness of those methods. By providing this visibility, One Model’s platform is currently helping them monitor their progress towards this goal, and allows them to see the affect - the direct impact on numbers of M/F managers in real-time. Company X is one of the many companies that has embraced the importance of diversity in workforce planning. We’re proud to be a part of the solution helping them meet their goals. Metrics That Move the Needle More companies are now following this lead—using People Analytics to ask better questions and close more meaningful gaps. Some of the key DEI metrics organizations are tracking today include: Representation Metrics (By role, level, department, gender, ethnicity, veteran status, IWD) Recruitment Funnel Drop-off Points Interview Progression by Demographic Promotion and Pay Equity Analytics Training Penetration Rates by Group Culture & Climate Sentiment Scores Exit Reasons by Demographic Because if you're not tracking it, you're not fixing it. Data is Where DEI Gets Real So yes—once upon a time, there were more CEOs named David and John than there were women CEOs and probably still are. And while that point made headlines, it didn’t change the numbers. Data did. DEI needs more than good intentions. It needs visibility. Targets. Measurement. Feedback loops. And the right platform to make all of that possible. If you're building a more inclusive workforce—and you want the data to back it up—One Model can help. Want to see what better data can do for you? Take a look at One Model. About One Model One Model pioneered people data orchestration and flexible predictive models that empower large and rapidly growing companies to unlock transformative insights and data-driven workforce strategies. Built to reduce technical burdens for data scientists, engineers, and HR leaders alike, our platform is the most flexible and secure solution available today. We are committed to ethical data practices, ensuring unmatched security, privacy, and transparency, providing confidence in every decision powered by One Model.
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Featured
10 min read
Stacia Damron
Back in 2016, Harvard Business Review reported that 58% of people said they trusted strangers more than their own boss. Fast forward to 2024, and trust in leadership hasn't improved. According to PwC’s latest Trust Survey, 70% of employees say they don't trust their company’s leaders to follow through on commitments. Meanwhile, DoorDash handles over 7 million deliveries a day, and Airbnb hosts millions of guests in strangers’ homes. People are putting their dinner, their safety, and their vacations in the hands of people they’ve never met—but trust inside the workplace? Still in short supply. Workplace Trust Building trust in the workplace promotes confidence in a company's future. When workplace trust is strong, employees tend to work more effectively, engage with their teams, and contribute to greater overall productivity. You could say trust is both a cause and a result of company culture. Every day, we make decisions—consciously or not—based on how much we trust one another. And with each decision, we either build trust or chip away at it. So, where did workplace trust go? How can companies and managers go beyond surface-level efforts to rebuild it? And how are People Analytics professionals measuring, tracking, and improving employee satisfaction in a meaningful way? This article doesn’t claim to have a silver bullet—but here are some solid KPIs worth keeping on your radar: Absenteeism Rate Employees who are present, on-time, and hitting their goals and deadlines are going to be more engaged, satisfied employees. Those who aren’t … well, they might not be singing the company's praises. Monitoring absenteeism and cross referencing with other KPIs is a good place to start. Employee Turnover Rate While it's a common belief that employees leave organizations primarily for better pay, recent data suggests a more nuanced picture. According to a 2024 report by iCIMS, among employees planning to job hunt, the top reason cited was a higher salary. However, it's essential to recognize that compensation is just one piece of the puzzle. Factors such as career advancement opportunities, work-life balance, and management quality also play crucial roles in employee retention. Is the company conducting exit surveys? Tracking why employees are leaving is vital, in addition to measuring additional metrics such as turnaround under specific managers, departments, or within specific minority groups. Is there a pattern in turnover? Perhaps a specific department, manager, or trigger event is responsible? Do you have predictive models that can help you internalize your data and answer the big questions? Employee Net Promoter Score The Employee Net Promoter Score (eNPS) measures the likelihood of whether an employee would be willing to recommend your company as a great place to work and whether they would recommend the products or services your company creates. Employee Connect reported in 2023 that 59% of employees wouldn’t recommend theirs. If you haven't yet started, track your eNPS. Then you can filter the data through a platform where you can see patterns and trends that could have affected the results. Quick note: You can measure, track, and monitor trust-related trends across your organization using One Model’s People Analytics platform. Our tools help you uncover correlations between trust and key workforce metrics—like engagement, retention, and performance—so you can take data-driven action where it matters most. Training When your car runs out of gas, you don’t abandon it—you refill the tank and keep going. Your team deserves the same consideration. According to the 2024 Training Industry Report, companies spent an average of $774 per learner in 2024, with variations based on company size. While these figures may seem modest, they primarily account for direct training expenses and may not include indirect costs such as lost productivity or administrative expenses. Employees who opt into company-sponsored training—or are supported in pursuing outside education—tend to be more engaged in their roles, feel valued, and stay loyal. They’re also more likely to become promoters of your workplace, boosting your employee Net Promoter Score (eNPS) and fueling growth through word-of-mouth in their networks. Building Trust in the Workplace with Predictive Workforce Data Trust, engagement, and performance can all be tracked and improved—if you know where to look. With the right tools, companies can use their workforce data not just to react to trends, but to anticipate them. If you're serious about strengthening company trust, it’s time to move beyond simple reporting and start using predictive modeling. These models help you surface hidden factors that erode workplace trust by: Predicting Attrition Identify which employees are likely to leave within the next 6–12 months, based on signals like manager changes, internal application outcomes, commute times, training engagement, and more. Strengthen company trust by addressing underlying issues before they escalate. Spotting Manager Risk See the warning signs that can quietly erode workplace trust under certain leaders or departments. Models can uncover red flags that deserve a closer look. Optimizing Hiring Strategies Which sourcing channels lead to high-performing hires? Are you bringing in the right people—or just the most available? Predictive hiring models can validate your recruiting efforts and help improve quality of hire. How One Model Helps One Model gives you the platform to bring these models to life. It integrates data from any source, cleans and normalizes it, and powers your analytics with clear, visual tools. Whether you're using out-of-the-box predictive models or working with our team on something custom, you’ll have the infrastructure to build, scale, and evolve your analytics over time. And when your data changes? Adding new sources or systems doesn’t mean starting over. One Model is built to flex with your ecosystem—no messy rebuilds required. So if you're ready to move beyond dashboards and start asking bigger questions—like how to build company trust and drive long-term success—we're ready to help you answer them. Explore more at onemodel.co, or reach out with questions. We love solving data problems. Request a demo
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Featured
4 min read
Steve Hall
In our previous blog on problem-solving with One Model, we explored how analytics platforms must go beyond standard reports to uncover deeper insights. Building on that foundation, this post examines why adaptability is crucial for organizations facing complex, evolving workforce challenges. Many organizations rely on pre-configured People Analytics solutions, but these often fall short when unique challenges arise. Every business has its own workforce dynamics, and rigid tools can’t always provide the insights needed for complex, real-world decisions. That’s why adaptability is the game-changer in People Analytics. Why Adaptability Matters in People Analytics Workforce data isn’t one-size-fits-all. Organizations face evolving questions that demand flexibility in how data is collected, structured, and analyzed. A standardized analytics platform might answer common HR questions, but when deeper exploration is needed—such as uncovering hidden turnover patterns or understanding engagement shifts over time—rigid systems become a barrier. The Problem with Pre-Built Analytics Many platforms impose a fixed structure on users, restricting how they ingest, model, and visualize data. While this simplifies reporting for standard KPIs, it becomes a roadblock when leaders need to analyze specific, nuanced issues. For example, a company may want to examine how compensation changes impact voluntary turnover in specific regions over the past three years. If their platform only provides a broad turnover metric, they’re left with surface-level insights that don’t capture the full picture. This lack of flexibility can lead to oversimplified conclusions, which is a risk businesses can’t afford. Breaking Free with Granular Data Control One Model takes a different approach. Instead of forcing users into pre-built reports, it provides the building blocks of workforce data—think of them like Lego bricks that users can combine in any way necessary. This means companies can analyze workforce trends on their own terms. Instead of being limited to pre-set dashboards, they can explore trends like: How internal mobility connects to performance and engagement The impact of hybrid work on long-term retention Whether specific hiring channels lead to higher-quality hires By enabling teams to create custom models and visualizations, One Model helps businesses get to the real insights behind their data and not just the ones predefined by software limitations. The Freedom to Solve Real-World Problems Having full control over data isn’t just about customization. It’s about ensuring analytics can evolve alongside shifting business needs. The ability to refine and explore data dynamically allows leaders to move beyond predefined assumptions and uncover insights that drive more precise decision-making. This adaptability is essential because: Every business problem is unique: Organizations need tools that can handle their challenges, not just generic ones. New questions emerge constantly: Analytics should evolve as business needs change, without waiting for software updates. Decisions require precision: When making decisions about people, generalized reports aren’t enough. The Future of People Analytics: More Control, More Impact The best People Analytics platforms don’t dictate how organizations should think about their workforce—they empower them to explore, analyze, and act with confidence. One Model ensures that businesses aren’t just consuming analytics. Instead, they’re shaping their insights to fit their specific needs. True adaptability in data modeling and visualization gives organizations the power to own their workforce strategy, rather than relying on out-of-the-box assumptions. Analytics should work for you, not the other way around. And that’s what One Model delivers. Want to learn more?
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Featured
15 min read
The One Model Team
Transcript: Hi, everyone. Let’s dive right in. Today, we’re going to talk about unlocking the power of people data platforms—what that means, how to access your data, and how to connect with it in meaningful ways to drive insights across the workforce. Introduction I’m Richard, and for those I haven’t met, it’s great to meet you. A bit about me: I started my career in nonprofits—shout out to others here with a nonprofit background! From there, I moved into HR, focusing on workforce planning at GE Capital, followed by roles at Citibank. Eventually, I discovered my passion for People Analytics, which shaped the trajectory of my career. I’ve had the privilege of working on People Analytics teams at companies like Facebook, Uber, Nike, and Argo AI. Each experience taught me something new about building scalable teams and leveraging technology to solve big challenges. For example, at Facebook (when it was still Facebook), our People Analytics team grew from 15 to 150 people. It was an exhilarating time, but not every organization can afford that kind of scale. So, when I moved to Uber, the focus was on how to scale smarter—how to build products and platforms instead of large teams. At Nike, I also helped build data foundations and worked closely with data engineering teams to develop a more robust HR data hub. When I moved to Argo AI, I worked across HR tech, People Analytics, and project management. I was heavily involved with Workday and began exploring One Model, which shaped my approach to building scalable analytics solutions. Fast forward to today: I’m now VP of People Analytics Strategy at One Model, where I get to connect with hundreds of People Analytics teams annually. This has given me a unique perspective on what’s working, what’s not, and where we’re all heading. Why is People Analytics So Hard? Let’s start with a key question I ask every time I talk about People Analytics: Why is this so hard? People Analytics as we know it is still relatively new. The modern function emerged maybe 15 years ago, and while it’s evolved a lot since then, we’re still figuring it out. There are so many names, frameworks, and definitions out there—it’s confusing for everyone. If you’re struggling to make sense of this within your organization, know that you’re not alone. From Facebook to smaller companies, everyone finds this hard. Defining People Analytics When I talk about People Analytics, I use three definitions: Community People Analytics is a community of practitioners working to create better workplaces through data. If there’s one thing you take away today, it’s this: there is a thriving People Analytics community out there. It’s full of nerdy, passionate people who love this topic. If you’re curious about data or looking to use it more effectively in your organization, find these people—they’re everywhere and eager to connect. The Act The act of People Analytics is simply using data to support workforce decisions. This isn’t just an HR responsibility—everyone in the organization, from managers to the CEO, makes workforce decisions. They should all be using data to do it. The Function The People Analytics function is the team within HR that supports this work. They build systems, provide guardrails, and help the organization use data effectively. The Invisible Work of People Analytics Leaders One challenge for People Analytics leaders is that they’re often hired for one job but end up doing another. Their job descriptions focus on descriptive, predictive, and prescriptive analytics. But once they start, they realize IT and HR haven’t spoken in years. They’re stuck cleaning data, navigating politics, and trying to get access to systems—none of which were in the job description. This invisible work is critical but goes unrecognized. If you have a People Analytics leader, send them a note and let them know you see and appreciate their effort. The "Skipped Step" in HR: People Data Here’s where the real problem lies: HR skipped a step. We went from strategy to operations to technology without fully addressing people data—the process of extracting, cleaning, and organizing data into a comprehensive HR data hub. Analytics teams are left backtracking to fix foundational issues before they can deliver insights. This skipped step creates pain for everyone. And it’s becoming even more critical now that HR data systems are feeding not just analytics but also Generative AI. The Little Red Hen Moment This brings us to what I like to call the "Little Red Hen Moment." You might remember the story: the Little Red Hen finds some corn and asks the other farm animals, "Who will help me plant the corn?" They all say no. So she plants it herself. Later, she asks, "Who will help me harvest the corn?" Again, no one helps. She does it herself. Then she bakes the bread and asks, "Who will help me eat the bread?" And, of course, suddenly everyone wants in. This is exactly what happens with People Data in many organizations. HR leaders ask, “Who will help us build the business case for People Analytics?” The data engineering team says, "Not I," because they’re busy maintaining data pipelines for sales. The IT team says, "Not I," because they’re focused on streamlining the vendor landscape. The enterprise analytics team says, "Not I," because they’re prioritizing metrics for finance. So HR is left to plant the corn, harvest it, and bake the bread on its own. We pull together data manually, build foundational systems, and lay the groundwork for analytics and insights—all while trying to establish a sustainable workforce data supply chain. But once those insights are ready—once the bread is out of the oven—everyone shows up to eat. The same teams that didn’t prioritize People Data suddenly want the insights it produces. They’re eager to see workforce metrics, predictive models, or generative AI results, but they don’t recognize the effort it took to get there. This isn’t just an HR problem; it’s a structural issue. HR has been underinvested in and systemically held back. Other business functions—like finance, marketing, and operations—have robust platforms and strong executive support. HR deserves the same level of investment to drive business outcomes. The message here is simple: it’s time for HR to demand a seat at the table and take HR data ownership People Data and build a robust HR data hub to succeed.. We need to make the case for why this work matters—not just for HR, but for the entire organization. The Framework for People Data Platforms Let’s talk about People Data platforms—which are essentially the foundation for a workforce data supply chain. A platform has two key components: The Data Foundation This is where data is extracted, modeled, and organized. It’s the backbone of everything, including generative AI. The Application Layer This is where data is visualized, analyzed, and put to use. At One Model, we’ve developed a framework that covers every stage: extract, model, store, analyze, and deliver. Each step has detailed requirements, and we provide tools to help organizations navigate them effectively. Dive deeper into the 5 Steps to Get Data Extraction Right. Conclusion People Analytics is hard, but the opportunities are enormous. By investing in People Data platforms and supporting our teams, we can create better workplaces and drive smarter decisions. Q&A Q1: During the modeling phase, are you prioritizing data? Is all of it being stored, or are you storing it in multiple places? Are you saying, “This is the most useful for dashboards,” and keeping other data as a backup in case it’s needed for KPIs later? What does that look like? A: That’s a really good question. Here’s how it typically works: You have data that sits in your core HR tools, the data you extract from those tools, and the modeled data you use for analytics. Along the way, you need to maintain copies—raw files, for example—for audits. But it’s the modeled data that should be driving your business decisions. What often happens in HR is that we’re told, “Just pick what you need,” because we aren’t given the resources to extract and store everything. This is one of the things One Model addresses—we create a single, unified data model where all your data is combined and accessible in one spot. This approach is becoming the norm for mature People Analytics teams. They no longer accept being limited to a single report from Workday or any other system. Instead, they demand full access and make sure their data is modeled and ready for any use case. And this is important because features in your data can play into your models in surprising ways. For instance, data from internal communications platforms like FirstUp can be remarkably effective for attrition prediction, but it’s often difficult to get access to that data. Q2: So, you’re doing predictive modeling as well. Can you use the same scripts or frameworks and apply them to different datasets? A: That’s a great question. Another key point to understand here is the difference between data extracts for reporting and data extracts for data science. For example, Workday provides daily snapshots of data. That works fine for reporting, but for predictive modeling, you need data over time. HR data is especially time-sensitive—more so than in many other functions—because of how events like transfers, exits, and tenure affect workforce insights. You can’t have someone transferring after they’ve already quit. The sequence of events really matters. This is where taking raw file snapshots and turning them into analytical feeds becomes critical. The ability to extract data for machine learning and predictive modeling is fundamentally different from extracting data for reporting. It’s something HR teams need to be aware of and push their IT teams to support because I’ve seen too many teams pressured into settling for reporting-level extracts, and it’s just not enough. Q3: When working with highly customized platforms like Workday or your ATS system, you often can’t—or don’t—make changes. For example, adding regrettable versus non-regrettable turnover as a data point can require defining those terms and assigning someone to audit that information. What advice do you have for making the business case for these changes? A: That’s an excellent question. Two things come to mind. Bring the stakeholder’s pain with you Let’s say you have a stakeholder downstream who’s really feeling the pain from a lack of data, like not knowing whether turnover is regrettable or not. Often, HR tries to solve this issue internally, on behalf of the stakeholder, by negotiating changes with upstream teams like HR tech. The problem is, the tech team doesn’t feel that pain firsthand, so they don’t prioritize the change. Instead, bring the stakeholder along with you to these discussions. Let them articulate their challenge directly to the HR tech team. When the tech team sees how their choices—or lack of action—are impacting the business, they’re more likely to respond. Create a new umbrella function The other solution I’ve seen gaining traction is hiring a leader to oversee People Analytics, HR technology, HR strategy, and operations as a single function. We call this the “workforce systems leader.” About 40 Fortune 50 companies have already started building roles like this. This umbrella leader can help navigate the politics and make tough decisions more efficiently. For instance, they can prevent unnecessary internal friction, like the head of People Analytics clashing with the head of HR tech. Instead, this leader would coordinate those efforts to drive progress forward. Q4: How do you recommend building a relationship with IT so they understand HR’s needs without seeing it as interference with data governance? A: That’s a fantastic question. I’ll give you two approaches—one "nice" and one a little more assertive. The nice way A lot of times, IT leaders (and finance leaders too) don’t fully understand HR’s technical needs. But they do understand their role as people leaders. So, start by framing the conversation in terms they’ll relate to. For example, you might say, “You’re leading a 400-person organization. Do you have visibility into what’s happening in your own team? Do you know where your pain points are?” This can help them see how better data access benefits not just HR but also their own leadership. The assertive way Here’s the reality: Other functions, like IT or finance, have no problem saying "no" to HR. But when they need something—like hiring 50 new project managers—they come to us, and we almost always say "yes." HR is often the ultimate team player, taking on more than its share of the load. While that’s great in theory, it can sometimes weaken our bargaining position. To build a stronger relationship with IT, we need to be more assertive about our needs. For example, we might say, “If you want to continue working the way you are, we’re going to need support from you. Let’s come to the table and figure this out together.” In short, it’s about clear communication, mutual accountability, and, sometimes, standing our ground to get what we need. Thanks, everyone! Ready for a deeper dive? Download Achieving People Analytics Maturity with a People Data Platform today for more insights on maturing your workforce data for actionable insights.
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Featured
14 min read
Phil Schrader
As a people analytics leader, you’re going to be confronted with some not-so-simple, horribly open-ended questions: “Hey, so what do you want to measure?” Where should we start?” or… “What HR dashboards should we build?” Perhaps these words have been uttered by a well-intentioned business analyst from IT, peering at you from behind a laptop, eager to get your items added into an upcoming sprint before all their resources get tied up with something else. What do you say? Something really gnarly and fancy that shows your analytic savvy? Something that focuses on a key issue confronting the organization? Something basic? Fear not. In this blog post, we’ll walk you through eight essential people analytics dashboards. You should be able to get the HR data for all of these from your core HRIS or HCM solution, even if they’re in different modules and you have to combine it into one dataset. The key performance indicators (KPIs) in these views will give you the highest impact: Headcount Metrics Dashboard Span of Control Dashboard Employee Turnover Dashboard or Attrition Dashboard Talent Flow Dashboard Career Growth / Promotions Dashboard Diversity (DE&I) Dashboard Employee Tenure Dashboard …see below… 1: Headcount Metrics Dashboard Headcount metrics are the foundation of people analytics. Headcount speaks volumes. Trend it over time, break it out by key groupings, and you are well on your way to doing great people analytics. Here’s an initial view that captures the basics. Here’s what’s included in this dashboard so you can get a handle on headcount. In the upper right, you’ve got what I call the “walking around the number”. It’s not anything that will help you make an informed decision on anything. But this is the stat that you would feel embarrassed if someone asked you and you didn’t know off the top of your head. Here it’s the total number of employees as of the current point in time. (EOP is shorthand for End of Period. Be precise in how you define things. More on this at the end.) Next, you’ll want to see the headcount trended over time. Here we have a monthly trend paired with the same period last year. Boom. Now you can see how things are changing and how they compare with the previous year. Also, these two visuals are a great test run for your existing reporting and analytics capabilities. In the bottom right, here you have headcount broken out by org unit (or business unit, or supervisory org for you Workday types). Here you want not only the total counts but ideally a stacked column view so you can see the proportion of contractors, part-time, co-op, or other employment types. Different orgs might get their work done in different ways. You should know the differences. Finally, a map view of headcount by geography. It’s not a basic visual, but it has certainly become essential. Things happen in the world. You need to know where your workforce is so you can quickly estimate the impact and plan support. In just the past two years, employees have been impacted by wildfires, heat domes, political unrest, blizzards, cold snaps, flooding, and, of course, COVID. Geo maps have officially gone from fancy visual to essential view. 2: Span of Control Dashboard I’m going to change things up a bit by elevating the span of control to the second slot on this list. Don’t worry. We dive into attrition and representation later in the article. As a people leader, you’ve got to maintain some perspective on how efficiently your workforce gets work done. There are many ways to do this. You could calculate the total cost of your workforce. You could align those costs against revenue over time. By all means, do that. But this list is also there to help you get started. With just the data from your core HCM / HRIS system, your team should be able to show you the span of control and organizational layers. These metrics always remind me of stepping on a scale. If your span of control is ticking down, you’re getting less lean. If you’re adding more layers, your internal coordination costs are going up. There could be good reasons for this– but there sure as heck can be bad reasons for this. Here you’ll find your key Span of Control Metrics, your trend over time, and your layers and org units visualized. The real killer metric – if you’ve got the stomach for it – is a simple list of the number of managers in your organization that have only one or two direct reports. Use these views to keep your talent management processes grounded in business reality. If your existing team/technology can’t produce these views then shift them back. 3: Employee Turnover Dashboard or Attrition Dashboard Ok, we can’t go any further without employee turnover. Attrition if you’re feeling fancy. Turnover is the strongest signal you get from your workforce. Someone worked here and– for one reason or another– it didn’t work out. Changing jobs and firing an employee are both major events. Your workforce is telling you something and you need to listen to help you with employee retention. Here’s a basic view to get you started. Again, get your rolling 12-month termination rate up at the top and trend it out with the previous year for context. Below that, you see a breakout of voluntary and involuntary termination rates. Then, you can see breakouts by business unit, location, and org tenure groupings. Now with a glance, you can see how turnover rates are changing, where they are high, and whether it’s you or the employee forcing the change. Learn more how to calculate the cost of turnover. 4: Talent Flow Dashboard Once you’ve got a turnover view squared away, you can move into broader views of talent movement within your organization. Here’s a high-level talent flow view to get started. It leads off with a consolidated view of hires, terms, and net hires trend over time. I love this view because it lends itself to discussions of churn and the cost of turnover. The top area (green) shows external hires. The bottom (red) shows exits/terminations. The dark bars show the difference: net hires. The big question. How much of that time and money that you put into recruiting is just to replace the people who leave the company? A great variation on this view is to limit it to women or underrepresented groups. Are you working hard to attract these demographics, only to have them leave because they don’t find the organization to be a fit for them? We’ll get to more workforce representation views below. Next to the Net Hire Trend, you can mix in a growth metric and a helpful breakout by “business unit, so you can keep an eye on what segments of the organization are growing/shrinking. Are they the ones you expect? Later when you bring in data from other systems like learning, this view can be a place to collaborate with the learning team to answer questions like: Are you adding more employees, when you could be upskilling? Finally, get a solid crosstab view of promotions or movements. This will help you optimize talent development and answer questions like: Do people move from function to function? If so, what are the common paths? What paths don’t exist? Should they? 5: Career Growth / Promotions Dashboard After you get the big picture on movements, dig into promotions. In my mind, the movement and span of control views are about what the organization is experiencing. Promotions put you more in the mind of your employees and what career opportunities look like in your organization. I’ve added two of our key metrics to the top of this one. What’s the rate at which people get promoted and how long is the typical wait for promotion? Once you know the typical (average or median is fine) wait time, keep your ears out for high potential / high performers who have run past that mark. They’re probably keeping a rough estimate of that metric in their minds as well. Below that are two breakout views. The first one - “Manager Hires vs. Promotions to Manager” - is meant to look at a key milestone in career growth. I’ve used promotion to manager, but you might have unique ones. Then for each business unit, I’ve compared the number of promotions into that key group with the number of outside hires in that group. Are you growing your own leaders (or another key group)? If not, why? Filling out the bottom row is the “Termination Rate and Headcount by Time since Last Promotion” view. Look for two things here: 1) Do people leave if they don’t get promoted? 2) Do people leave right after they get promoted? 6: Diversity (DE&I) Dashboard It’s past time we brought in views of the diversity, equity and inclusion (DE&I) in your workforce. Many of the views in the dashboard below are split out versions of the metrics introduced above. Above is a sample diversity dashboard using male / female breakouts. Use this as a template for other representation breakouts including ethnicity, gender identity, age, etc. Any of these views could be modified to incorporate multiple, rather than just two, groupings. The top bar shows activity differentials over time. Hires are done simply as counts. Do you hire more men than women? Are promotions and terminations handed as rates to monitor for disproportionate outcomes?, i.e. are men promoted more often than women? The second row shows representation by key grouping in stacked horizontal bars. I like organizational layer and salary band to show if high career outcomes are disproportionate. I’d recommend the inclusion of tenure as well, however. If your organization had a history of disproportionate staffing, you will get a clue in this view. That could explain why today’s initiatives have not yet balanced out outcomes in level or pay. Or differences in tenure might be explained by differences in termination rates, depicted directly above in this view. This is a multifaceted issue. 7: Employee Tenure Dashboard Confession. I love tenure. I’ve come of age in my career amid data telling me that I’ll work for something like 11 companies before I retire. And, to be honest, I’ve done my share of career hopping. But it turns out that when you stick around somewhere, you learn things. You make connections with your co-workers. Employee tenure represents the accumulation of invaluable knowledge and connections that help you measure the value of your human capital. Next to average tenure, this dashboard shows the total accumulated workforce tenure in years. While not exactly a “walking around number,” you can use this to impress your fellow leaders into thinking about your workforce like the treasured asset it is. “Hey, our team has x millennia of accumulated experience!” Rounding out this view is a sorted view of positions or job titles with lots of accumulated experience as well as a stacked trend over time to see how tenure groupings are changing. 8: Dashboard Definitions and Details This final section is not a specific dashboard suggestion. Rather, it’s intended as a sobering reminder that none of the dashboards above will make an impact in your organization if you can’t explain your logic and build trust in your data. I like to build little glossary style views right into the dashboards I create. For example, at the bottom of our standard attrition storyboards, I’ve added breakouts showing which termination reason codes are included as voluntary and which are involuntary. Next to my glossary, I’ve created a table that breaks out the subcomponents of turnover rate, such as total headcount and days in period. I like to include at least one leap year for a bit of showmanship. “Look, I’ve even accounted for the fact that 2020 had 366 days, so back off.” Ready To Learn More? Get All Your Questions Answered One-on-one. Finally, if your security models and technology support it, drill to detail. This is the number one, all-time champion feature of people analytics. Click on headcount, terminations, whatever and see the actual people included in the data. Bonus points for adding the definition and “bread crumb trail” for metrics that build off of other metrics. Below is a view of how we do that in One Model. If you’d like to see these people analytics dashboards in action or learn more about people analytics software for your organization, reach out to us!
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The One Model Team
One Model was founded around a goal of helping teams tell data-informed stories that lead to brilliant, data-driven talent decisions. By leveraging data and story, we can help teams communicate a deeper understanding of the tangible benefits of diversity, equity, and inclusion initiatives, and how they contribute to the success of a business. Data-informed stories can be a powerful tool for uncovering how the work environment is impacting our employees. Through data, we can demonstrate the positive impact of treating people well, and how this can drive business success. Let’s walk through one of the “classic stories” we hear in HR and people analytics for a fictional organisation called Innovative Enterprise. We’ll start with the story, introduce the data, and then apply One Model’s data-informed storytelling framework to the story to show how our platform easily weaves the narratives together. This is a common story that HR teams are asked to tell around employee experience and the impact that a positive work environment can have on the overall business. Story alone: Within Innovative Enterprise, while we have a diverse workforce, this diversity is yet to permeate our leadership effectively. Our leadership team, although competent and committed, does not fully represent the diverse perspectives present within our broader team. This lack of representation in leadership could potentially influence our culture and engagement levels. Data alone: Internal data at Innovative Enterprise shows that while 49% of our workforce identifies as ethnically diverse, only 15% of our leadership does. Recent industry studies that the people analytics team analysed indicate that organisations with diverse leadership teams outperform those without by 35% in terms of innovation and creativity. Moreover, organisations that boast diverse leadership report a 25% higher employee satisfaction score compared to companies with less diverse leadership teams. Data story: At Innovative Enterprise, the lack of diversity in our leadership team becomes evident. Our internal data reveals that while our workforce is 49% ethnically diverse, only 15% of our leadership reflects this diversity. It's clear we're falling short, and this is a challenge that we share with many organisations across our industry. However, industry data provides a clear directive: organisations with diverse leadership teams are more innovative and creative by 35%. They also report a 25% higher employee satisfaction score, indicating a more engaged and motivated workforce. This compelling combination of our internal situation and broader industry data paints a powerful argument for enhancing diversity, equity, and inclusion at the leadership level. The data provides clear guidance — it's time for us to take action. Ready to learn more This example from Innovative Enterprise demonstrates the power of data-informed storytelling in HR. For more impactful stories and detailed analysis, download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organisation tell compelling, data-driven stories.
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The One Model Team
If you're preparing for people analytics, there’s a lot to do before you hire that first data scientist. To build the right foundation for success, there are five important steps you should follow that don’t even involve data, insights, or statistics. Following these steps will help you establish and support an efficient and impactful people analytics practice at your organisation. 1. Find your why Understanding why you're pursuing people analytics is vital to your journey. This not only means identifying the specific business needs that would benefit from a better understanding, deeper insights, or more precise analysis of your workforce, but also exploring the underlying reasons behind those needs. You could start by asking questions like: What are the biggest challenges or pain points we're facing as an organisation? What are the key areas where we could improve our workforce, and how would we measure success? What are the most critical business decisions we need to make, and what do I need to know to help us make them more effectively? What are the specific gaps in our knowledge that we need to fill in order to make better decisions? Without taking the time to find the why for your organisation, you risk getting lost or going off course before you even begin. By finding your why early and holding onto it through the process, this will keep you focused throughout your people analytics journey. 2. Look upstream When starting your people analytics journey, it’s important to remember that the data you’ve generated is only as good as your processes and technology. There’s a flow we like to think about from process to tech to data to analytics. When people analytics teams run into challenges, there’s likely an upstream challenge in one of these steps to address. Begin by examining your processes. Technology is only as good as the process it’s automating, so if your processes are poorly designed and documented, your technology is unlikely to be implemented correctly. Technology should reflect how you want your business to run. If it doesn’t, you’ll likely end up with incomplete or incorrect data flowing out of the technology — making it difficult or impossible for people analytics teams to create value.This is not to say “don’t start on people analytics until the rest is done”. People analytics teams can absolutely provide great value, and some of the best teams out there are scrappy with what they have on hand. This is more of an acknowledgement of the flow and a callout that if you want long-term success of your people analytics team and to unlock that next level of value, you’ll have to address these upstream challenges. A strong people analytics leader will also be able to help you identify and navigate these challenges upstream. So begin by ensuring that your processes are well-designed and documented. Next, double check on your technology implementation and ensure that it matches your processes. Finally, check in on the data. The data ultimately doesn’t lie, so it will tell you if the processes and tech are clean. Doing so will ensure that data flows smoothly and accurately from the technology preparing you for analytics. 3. Address data management Another early focus for starting down the path of people analytics is data management. Without data, there’s not much for people analytics teams to do. It’s the oil to the people analytics engine. We’ve seen a number of teams get started, but then plateau around a lack of good data. At times the resources to fix data problems sit outside of HR, which makes it all the more important to navigate and commit that resource request up front when pursuing people analytics. Making sure your data is accessible is critical, but raw data extraction is also only the beginning. A robust workforce-specific data model, proper data architecture blending your different systems data, and HR-led workforce data privacy and workforce data governance are also part of your people analytics foundation. This may require marshalling what are typically scarce internal resources, capabilities, and priorities from IT or data engineering teams to ensure that your data is clean, systematically organised, and readily analysable. Or you can save those internal resources by working with people analytics platforms like One Model. We were founded to make this upstream challenge easier. We provide named data engineering resources, have experience developing business-specific workforce data models, and provide the data foundation that people analytics teams need to thrive. If you skip this step, you may experience the following problems: Missing data: Without the right data management structure in place, you may find it difficult to extract the data you need for a given project. This can lead to incomplete or incorrect data and difficult analysis. Slow data: Improper data management can leave you with only monthly (or quarterly!) snapshots and that pace just doesn’t reflect how fast your business moves — let alone back-dated changes, which are frequently found in HR. Inability to build predictive models: Data management is critical to building predictive models. To develop predictive models, you need to extract data in a very specific way (e.g. time-stamped changes). It’ll be difficult or even impossible to build accurate and effective models without this proper data management. By addressing data management early on in your people analytics journey, you can avoid these symptoms and ensure that your people analytics initiatives are successful. To learn more, here are five tips for getting HR data extraction right. 4. Set the tone Setting the tone at the top is crucial for demonstrating that data-driven decision making is the way forward. This involves garnering support from your organisation's senior leaders, as well as regular reminders, activities, and actions from the CHRO or HR head. If you’re in a leadership position, setting the standard that data is required for new projects and investment decisions goes a long way. Cultivating a data-minded culture will trickle down from the top, setting a precedent for the entire organisation. Without this high-level endorsement and sustained backing, making significant strides in people analytics can prove challenging. 5. Find help Consider engaging with a seasoned people analytics leader either full-time or as a consultant to spearhead your people analytics initiatives and education within your function. Experienced people analytics leaders, with their unique combination of data analysis skills, HR orientation, ethical understanding, and team management expertise, can provide invaluable guidance. They’ll work to ensure alignment between your analytics efforts and broader business objectives. Remember to also tap into the people analytics community. This strong and enthusiastic network can provide invaluable support. Engage with professionals on LinkedIn, ask questions, and use the expertise of vendors in the space. The team here at One Model is always willing to connect and assist at every stage of your people analytics journey. New to people analytics or ready to enhance your existing program? Either way, our eBook People Analytics 101 covers everything you need to know about establishing a strong people analytics foundation for smarter HR strategies and meaningful change across your organisation.
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What should your first people analytics project be? Many teams start with employee attrition because it has clear outcomes, it has a direct impact on the company, and attrition data is already in your HRIS. So to understand the five steps you need to follow during people analytics projects, let’s walk through an example of how Penelope, a fictional people analytics practitioner, might approach an employee attrition project, from start to finish. Step 1: Define the problem The first step in any people analytics project is to define the problem you want to solve. In this case, the problem is employee attrition. Specifically, we want to understand why employees are leaving the company and what we can do to reduce attrition. "As an HRBP, I noticed a trend of high employee turnover in the company. I began to investigate why employees were leaving and how we could reduce this trend. My goal was to identify the underlying causes of this issue and develop a plan to address it," says Penelope. Step 2: Gather the data The next step is to gather the data you need to analyse the problem. In this case, you'll need data on the employees who have left and their reasons for leaving (if available). This data can often be found in your HRIS, as well as employee surveys or exit interviews. "To gather the necessary information, I dove into the company's HRIS system, as well as employee surveys and exit interviews. I collected data on employee demographics, job history, performance metrics, and reasons for leaving. I made sure to gather as much relevant information as possible to ensure a comprehensive analysis," shares Penelope. Step 3: Analyse the data Once you have the data, it's time to analyse it. There are a variety of statistical methods you can use to analyse attrition data, including survival analysis, logistic regression, and decision trees. But you can also start with descriptive methods. Your choice of method will depend on the nature of your data and the questions you want to answer, and you don’t always need advanced methods. "I took a look at attrition trends across each of the major groups within the company. Using descriptive statistics, I found that some teams were experiencing higher attrition than others within similar business units. I wanted to identify why the attrition rate was high, so I looked for factors that were strongly correlated with attrition," notes Penelope. Step 4: Tell the story After analysing the data, it's time to tell the data story. This is where data visualisation and data storytelling come in. You'll want to create charts, graphs, and other visualisations that help you communicate your findings to stakeholders. You'll also want to craft a narrative that ties the data together and explains what it means for the company. "Using the results from the data analysis, I created charts, graphs, and other visualisations that I could use to communicate my findings to stakeholders. I crafted a narrative that brought my business knowledge into the story and explained the factors contributing to the high attrition rate and the steps we could take to address it. I presented the data and narrative to the company's leadership team," explains Penelope. Step 5: Implement solutions Finally, it's time to implement solutions based on your findings. This might involve changes to HR policies, changes to compensation structures, or changes to management practices. Whatever the solution, it should be informed by the data you've gathered and analysed. "Based on the data and narrative, I recommended changes to HR policies, compensation structures, and management practices. I presented the recommendations to the company's leadership team and worked with them to implement the changes. Over time, we saw a decrease in the attrition rate and an increase in employee satisfaction," says Penelope. Overall, attrition is a great starting point for any people analytics team. It's a universal problem that every company faces, and the data is often readily available. By analysing attrition data, you can gain valuable insights into your workforce and make data-driven decisions that improve retention and reduce turnover. New to people analytics or ready to enhance your existing program? Either way, our eBook People Analytics 101 covers everything you need to know about establishing a strong people analytics foundation for smarter HR strategies and meaningful change across your organisation.
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Richard Rosenow
For HR teams, especially new people analytics leaders, a hidden danger lurks in the shadows that can significantly hinder success: It's your Workforce data architecture. This danger comes from a substantial blind spot for organizations between HR and IT, making it difficult for new people analytics leaders to be successful and for HR teams to effectively leverage data. Understanding and bridging this gap is essential for unlocking the full potential of workforce analytics, which is increasingly vital in today's data-driven business environment. Troubleshooting the People Analytics Ecosystem When an HR organization embarks on a journey into people analytics, or when a new people analytics leader establishes a team, one of the first tasks is understanding and assessing the HR data landscape. Alongside stakeholder meetings, team assessments, and making the case for the necessary tech stack, evaluating the data infrastructure is crucial within the first 90 days. Initially, teams might try to manage with available system reports and surveys. Those teams end up relying heavily on manual data wrangling, which in turn brings human error, bias, and friction into processes and often involves complex and messy spreadsheets. Maintenance of those manual systems will eventually hold the team back. This approach carries significant risks, as it is prone to errors and inefficiencies. Moreover, if the key person managing these processes goes on leave or resigns, the entire operation could fall apart, leaving the team holding the bag on an impenetrable data model. When it comes to advanced analytics, the main trouble HR finds itself in at these companies is that people analytics teams can’t run on raw data from system reports alone. In order to reach beyond reporting and into analytics, people analytics teams require architected data, which means raw data must be converted into usable metrics and dimensions. An investment in data architecture forms the bedrock upon which advanced analytics and insightful decision-making are built. Access to clean, well-architected data is essential for the success of People Analytics teams. Bridging the “Invisible Gap” in Building a Solid People Analytics Data Infrastructure So the people analytics leader starts their journey: What data do I have, what data do I need, and what technologies produce data across our workforce ecosystem (HRIS, ATS, Survey, etc). Who do I have that can help me? But they quickly face a two-fold political problem: 1. On the HR side Their leaders and peers on the HR leadership team may be just starting to get familiar with analytics, and data architecture is a step beyond that comfort zone. HR education generally doesn’t cover data engineering and, to give that some credit, why should it? Most HR leaders will not need to engage in data architecture conversations. And to that point, most people analytics professionals are even downstream from these conversations or have to learn it on the job, too. There are very few, if any, courses for HR professionals on the nuances of workforce data architecture. Additionally, data engineering for analytics is a unique need specifically and almost entirely for people analytics within an HR team. People analytics regularly centralizes and handles this work on behalf of their HRLT peers, which inadvertently hides this work – and the pain of this work – from their peers. 2. On the IT side One might assume that IT or central data teams could provide the necessary support for people analytics leaders. While this is true in some organizations, the reality often is that IT and enterprise engineering teams, despite their data expertise, lack understanding of the unique nuances of slow-changing dimensions of workforce data and HR processes. That’s why we provide resources to help, like this 5 Tips for Getting Data Extraction Right blogpost. Additionally, IT teams are frequently overwhelmed with demands from various departments such as product, marketing, sales, and finance, making it challenging to prioritize HR-related data projects. The other hard truth is that we are still in a political reality where teams outside of HR don't readily recognize the value or prioritize this work, as we illustrate in The Little Red HR Team: A modern retelling of a timeless classic. So the people analytics leader, who needs workforce data to be extracted, architected, and modeled to do their assigned job, now has a problem. Why this gap is even more detrimental if you’re moving toward AI in HR Navigating Data Architecture Hurdles Securing buy-in, resources, and priority for data architecture work can be challenging, especially when it's often hidden from key teams and not part of the typical job description. Historically, people analytics leaders have faced two main options, each with its drawbacks: 1. Educate and influence campaign To get this work done, the people analytics leader embarks on an extended period of education for both HR and IT to explain what's going on and why they need to spend time, resources, and priority building an analytical data warehouse, not just reports from the core HRIS. This is thankless work trying to upskill and educate teams who do not want to know or need to know about this area to do their day jobs. These campaigns are long journeys. 2. Just get it done The people analytics leader advances into this “invisible work” by themselves or with the team they have, and just tries to get it done. People analytics leaders take the work on, upskilling in data engineering and doing the best they can. This results in a “good enough” but ultimately shaky foundation. And while that’s happening, people analytics has to wait until you have data to work with. So you put your head down and get work done. Unfortunately, when it's done or “good enough,” – and this is the hardest part – no one else will notice. The first option means you lose the critical window of time when new leaders need to show effectiveness. But the second option means you lose visibility and guarantee a long term problem with maintenance. Potentially even more dangerous for a new leader. With both options, success is far from guaranteed. Both HR and IT teams just want you to get your work done; they don't necessarily want to learn about why their current setup of technology is not working. There's good news, though. The vendor landscape supporting people analytics has been evolving to meet this need. How One Model Helps Move the World Forward One Model is uniquely designed to address the 'invisible work' of data engineering and data architecture in people analytics. This was clearly demonstrated in our work with Elastic, a leading tech company. The One Model platform enabled Elastic to streamline their data processes and significantly enhance their people analytics capabilities. Read more about our partnership with Elastic. Data Orchestration One Model stands alone when it comes to the levels of support we offer for data architecture. Our data orchestration layer is One Model’s crown jewel within the product suite. One Model seamlessly extracts, transforms, and loads your data into a secure, tailored data model within our People Data Cloud (effectively, a sophisticated data warehouse built specifically for your people data). The automation we establish ensures that your data is consistently updated daily without the need for manual intervention. By eliminating the need for manual data loads or loading files yourself, we provide a reliable and efficient solution for maintaining up-to-date, high-quality data for analytics. This was a game-changer for Elastic, enabling them to maintain accurate data without the burden of manual updates. Additionally, our platform features direct connectors that go beyond mere extraction of raw files or reports, providing fully modeled data ready for analytics. Whether dealing with flat files or complex data sources, our system integrates and unifies data into a cohesive analytical model, which updates daily, and streamlines your access to data. Data Engineering Support And you're not going at this alone or upskilling your team with additional expensive training to make this happen. One of the biggest reasons I chose One Model when I was a buyer in people analytics was that One Model provided data engineering support as part of the subscription. Named resources support your team, but above and beyond that support, the One Model platform and One Model team members maintain the data pipelines. No more calling IT teams that don't prioritize HR and no need to hire unique and expensive resources for data engineering. With One Model, you will have a partner you can call who not only picks up the phone, but who cares about your success in this “invisible” space. In Elastic's experience, this support allowed their team to focus on strategic analytics rather than getting bogged down in the technical details of data engineering. Seamless Integration from Connection to Dashboard Most importantly, this orchestration happens quickly and securely. You don't have to spend months or years trying to unlock your data. We can extract and create a tailored data model for your HRIS rapidly – from connection to dashboard! This quick implementation enabled Elastic to quickly transition to leveraging high-quality analytics, accelerating their time to value, and enhancing their overall data strategy. One Model stands as the global leader in our space, uniquely positioned among people analytics providers as the premier partner for data architecture. Don’t feel like you have to navigate the complexities of data architecture alone. Partner with One Model and leverage our expertise to unlock the full potential of your workforce data. Reach out to us today to see how we can transform your data management and analytics capabilities. Glossary of Terms When exploring the complexities of data architecture and engineering, it's helpful to familiarize yourself with key terms frequently encountered in this field. The following glossary provides a concise overview of essential concepts and terminology: Data architecture: The overarching strategy, rules, and principles governing the collection, organization, transformation, and storage of data in a specific environment. Raw data: Unprocessed digital information extracted from a technology, often in a format that's difficult to understand without processing. Data integration: The process of combining data from different sources and providing users with a unified view of these data; sometimes referred to as ETL, which is to Extract data from a source, Transform it to fit your needs, and Load it into the end system. At this point, it becomes an analytical data model (see #6). Trending data: Data showing changes and patterns over a specified period of time, often used to predict future events or behaviors. Data warehousing: A large store of data collected from a wide range of sources within a company and used to guide management decisions. Analytical data model: A set of interconnected tables (or fact tables) ready for use in analytics. (a.k.a. a Galaxy schema) Unified data model: A framework that unifies multiple data types from different sources into a consistent and universally accessible format. Download a resource for your IT team that helps explain why they should care about people analytics. Why Tech Leaders Prefer One Model's People Analytics Platform Download today
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The One Model Team
A well-crafted data-informed story can effectively influence decision-making, foster understanding, and drive meaningful change within the organization. A data-informed story blends the art of storytelling with data-driven insights, creating a compelling narrative that resonates with the audience and inspires action. Here's a framework for how to develop data-informed HR stories: Business Objective: Setting the Stage Every compelling data-informed story begins with a clear business objective. It's essential to know what you want to convey and the actions you want to inspire from your audience. Defining your objective gives direction to your story, shaping its structure and maintaining its focus. A well-articulated objective ensures your story remains purposeful and impactful, driving the narrative towards your desired outcome. “Using people data to get to a scientific insight is only half the battle. If you can't step back and crisply describe your findings in terms of business impact, you quickly lose the room, lower credibility, and break trust with business leaders.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Evidence: The Backbone of Your Story Your story's credibility stems from its findings: both data evidence and the story context. Data Evidence: Collect and analyse data pertinent to your objective. This data acts as the backbone of your story, supporting your narrative and revealing valuable trends, patterns, and insights. It's the facts and figures that make your story believable and persuasive, reinforcing your arguments and enhancing your story's validity. Story Context: Context adds depth to your data, making it meaningful and relevant. Explain why your data matters, its relation to broader organisational objectives, and its direct impact on your audience. This context helps your audience comprehend the data's significance, allowing them to connect the dots between raw data and its implications. Visualization: Bringing Your Data to Life Visualising your data helps to clarify and accentuate your key messages. Rather than presenting raw data or lists, craft clear and engaging visual representations of your data. This could involve charts, infographics, or diagrams, which enable your audience to quickly grasp the information and easily identify the patterns or trends you're emphasising. Narrative: The Art of Engaging Your Audience Narrative is the act of weaving together data and insights into a compelling story that resonates with the audience and inspires action. By using an engaging narrative, relatable examples and analogies, and emotional appeal, HR professionals can effectively communicate the human impact of organisational decisions and drive meaningful change. “To infuse more storytelling into People Analytics, understand the business and people context, use narrative techniques and visualisations to present data engagingly, and go beyond data by exploring the human factors driving it. Enhancing storytelling in this field can significantly boost its impact on business outcomes.” — Tony Truong, Vice President of People Strategy and Operations, Roku Engaging Narrative: To captivate your audience, weave your data and insights into a compelling narrative. Ensure your story flows logically, featuring a beginning, middle, and end, each part reinforcing the key message you wish to convey. Relatable Examples and Analogies: Examples and analogies act as bridges between complex data and familiar concepts. By relating your data to real-life scenarios or recognisable concepts, you make it more accessible and understandable for your audience, making your story more relatable and engaging. Emotional Appeal: The magic of storytelling lies in its ability to evoke emotions. Incorporate elements that resonate with your audience on an emotional level. This could involve personal anecdotes, inspiring stories, or connections between the data and the organisation's values and goals. “People Analytics insights have an easier path to landing as a compelling story if quantitative findings are combined with qualitative findings. Pulling anecdotes from HR and non-HR leaders, managers, and employees in your business lines is a validating and powerful storytelling device.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Interactivity: A Living, Breathing Story Data stories are not static monologues but dynamic dialogues. Build your stories in a way that allows you to be prepared for follow-up questions and additional requests. Consider building your data stories in platforms where you can treat them as living documents, flexible and adaptive, fostering interactivity and ongoing engagement. This approach will enrich your narrative, keeping it relevant and resonant over time. Action: The Impetus for Change The goal of any data-informed story is to inspire action. Conclude your story with a clear call to action, outlining what steps you want your audience to take based on the insights presented. This crucial step ensures your story doesn’t merely inform but also drives engagement, leading to tangible change. Ready to learn more? Download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organization tell compelling, data-driven stories.
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Matthew Wilton
People data is the lifeblood that fuels insights and drives strategic decisions. Yet, for many leaders, extracting meaningful data from complex systems like Workday can be a daunting task. One Model's Workday Connector is designed to turn this challenge into an opportunity, providing a powerful solution that stands out in the crowded market. Here's why it’s a game-changer for technical people analytics leaders. The One Model Advantage: Beyond Brute Force At its core, our Workday API Connector is built on a deep understanding of the intricacies of Workday. Unlike competitors who might rely on inefficient methods—such as pulling data for every employee every day—One Model has developed a sophisticated approach that is both clever and efficient. Intelligent Data Retrieval With a brute force approach querying a year's worth of data for a single employee requires 365 requests to the Workday API. For a 1,000 employee company this means to get a full year's data will require 365,000 API requests. Workday’s API returns data in large, complex XML files and API requests can take seconds to receive a response. For the 1,000 employee company, even if the API responds to every request in 1 second, it will take over 4 days to pull all the data from the Workday API. This brute force method does not scale and is not practical, especially for larger enterprises. Our solution? We focus on significant data change points, intelligently identifying the moments when meaningful changes occur in an employee's record. This approach not only reduces the volume of data processed but also ensures that we capture the most critical updates. The Self-Healing Data Model: Scalability and Accuracy One Model’s unique self-healing data model is a standout feature that ensures accuracy and consistency in your analytics. Here's how it works: Intelligent Identification: By leveraging our deep understanding of the nuances of data locations and changes, our connector identifies and extracts only the necessary data points. This minimizes the load on Workday’s API and speeds up the data retrieval process. Error Detection and Correction: Our system automatically detects discrepancies and back-dated changes, correcting them without manual intervention. This self-healing capability ensures that your data remains up to date and accurate, even if historical changes are made. Dynamic Processing: The connector dynamically adapts to changes in the Workday API, ensuring continuous, reliable data extraction without interruption. Comprehensive Data Support: From Raw Workday Data to Analytical Models One Model goes beyond mere data extraction. We transform raw data into analytical models, providing actionable insights rather than just raw numbers. Our approach integrates custom fields and user-defined reports, ensuring that even the unique aspects of your data are captured and analyzed. Integration with Custom Reports For those unique data points that aren't covered by standard API calls, One Model supports the integration of custom reports. Customers can create custom reports in Workday, which our connector then pulls and integrates into the overall data model. This flexibility means that no piece of data is left behind, giving you a comprehensive view of your workforce. Unmatched Support and Stability Our Workday Connector isn't just a tool; it's a platform-based service. Through the platform, we offer continuous monitoring, maintenance, and support to ensure your data pipeline remains robust and reliable. Beyond the platform, our team is on hand to address any issues, making sure that your focus remains on deriving insights, not on troubleshooting data pipelines. Handling Workday Data Updates with Ease Workday’s frequent updates can pose challenges, but One Model’s connector is designed to handle these seamlessly. By using versioned API endpoints and dynamic data processing, we ensure that changes in Workday’s data model do not disrupt your analytics operations. Why Choose One Model? In a market where many solutions promise easy data extraction but fall short on delivering comprehensive, scalable, and accurate data models, One Model’s Workday Connector stands out. Here’s why: Scalability: Efficient data retrieval methods that scale with your organization. Accuracy: Self-healing models that ensure data integrity. Flexibility: Integration of custom reports and fields. Support: Continuous maintenance and monitoring from a dedicated team. We have many customers and current prospects that have come to us to solve their challenges in accessing, obtaining, and maintaining a historic data load from Workday. With our Workday Connector, you get more than a Workday data export – you get it in a form that drives meaningful, actionable insights. Unlock the full potential of your people data with One Model. Connect with us today or download our Workday People Analytics guide to learn more about our connection to Workday and how it can transform your analytics capabilities.
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6 min read
Dennis Behrman
Anyone who analyzes data knows there's always a need to drill into reports to answer the questions that pop up. One Model is the only people analytics platform that allows you to drill into literally anything and everything, as if your siloed enterprise data sources were a single source of truth. Working with Metrics, Dimensions, and Time Explore is a powerful tool designed to help you perform powerful ad-hoc analysis on your One Model storyboards, reports, and visualizations. Here's a quick look at how the Explore tool works. Select Your Metrics Metrics are quantifiable measures used to understand the results or outcomes that you observe in your business. The Explore tool presents the entire list of metrics available to you based on your organization's metrics library and the access permission associated with your user profile and your group/team membership. You can add and remove metrics by dragging and dropping them from your metrics library to your metrics selection fields. Your metrics library contains all of the direct and derived values that are used to tell the stories hidden within your data. To learn more about how metrics are established in your One Model instance, check out this article or this video. (You may need to login with your One Model account to view Help Center content.) Pick Your Dimensions Dimensions are attributes or categories by which data can be grouped. Dimensions organize data into meaningful sections for comparing. For example, in a turnover report, dimensions could include rank, business unit, performance rating, and so on. To sub-group your data even further, you might want to add pivoted dimensions, which help you compare groups by more than one attribute. One Model's Explore tool allows you to drag and drop any number of dimensions into your report to see an analytical picture with more detail. Mind Your Time Model Time modeling is perhaps the trickiest and most important activity that happens on the One Model platform. Since time is a constant, your data analysis depends on the most comprehensive coverage of observable and measurable events for analyzing data over different periods. In theory, time subdivides infinitely. But in practice, most analysts and decision makers prefer to view time within a standard set of available lenses such as days, months, quarters, and years. But since months, quarters, and years can have different numbers of days within them, it is critical to getting time right to understand your business in the most accurate way possible. It's important for these cumulative measures to "add up" or "sum to the right number" when aggregated (or drilled through) at scale. It's equally important for data about events to be captured at various time intervals. For example, a group of employees who are currently high-performing rock stars may inform a decision today about high performers. But in reality, many of those rock stars may have been groupies in the past. One Model has no peer when it comes to the most effective application of time series analysis. Here's why. I created the Sankey diagram below with fake data to show a point. Observe how none of the more than 4000 high performers at the end of 2021 remained high performers at the end of 2023. So any analysis conducted in 2024 that uses the pool of 2023's high performers to infer multi-year trends would be an incomplete and possibly flawed analysis of the company's high performers. Most other approaches don't account for the question of "how it looked" in the past. Explore Explore's Unrivaled Speed to Insight Your organization needs the most accurate and current information to make the most informed talent decisions. The Explore tool is one of many keys to telling the stories within your data. Approachable & Intuitive One Model's Explore tool features a professional-class user interface designed to cater to both casual and highly technical users. This balanced design ensures that casual users can easily navigate and utilize the tool without feeling overwhelmed, while technical users have access to advanced functionalities and customization options. The interface’s adaptability fosters a productive environment for all users, enabling them to swiftly uncover insights and make data-driven decisions. Consistent Metrics Definitions Paired with Flexible Dimensional Pivots The Explore tool ensures the consistent application of organization-wide metrics definitions and offers the flexible application of dimensions, enabling users to tailor analyses to their specific needs. By presenting a cohesive and accurate picture of organizational data, the Explore tool enables faster and more reliable insights, accelerating the overall time-to-insight. Better, Faster Insights to More Decision-Makers Around Your Organization One Model's Explore tool excels in its ability to deploy sound insights to any team or decision maker within an enterprise. By seamlessly integrating with various data sources and offering robust reporting features, the tool ensures that actionable insights are readily accessible to all relevant stakeholders. No other people analytics platform drives more data-driven decision-making better than One Model, thanks to tools like Explore, which empower organizations to make informed decisions quickly and efficiently. Essential Questions to Ask When Selecting an AI-Powered HR Tool Learn the right questions to ask to make the right decisions as you explore incorporating AI in HR.
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7 min read
Gina Calvert
RedThread Research has identified 7 skills verification methods that range from simple to more complex. In Part 1 of this 2-part job skills assessment series, we dive into the 4 simplest and most common job skills assessments. In Part 2, we examine 3 complex forms of skills verification that lean heavily on benchmarks and data. RedThread members may access the full report authored by Heather Gilmartin and Dani Johnson. As the prevalence of skills-based recruiting grows, HR leaders are beginning to grapple with how to verify skills in order to ensure their data is accurate. They’re discovering that evaluating job skills is more complex than merely defining roles and hoping to find perfect matches. Decision-makers must weigh a variety of factors to determine the most suitable verification approach for their needs. You’re likely using some of these tactics to authenticate skills, but which are right for each role? And when should you level up to new ones? 1. Self-Assessment If you’re looking for simple ways to verify skills, having employees and applicants affirm their own expertise is the second most common approach, according to RedThread. This is most typically seen in job applications, employee resumes, and interviews. But just because it’s popular doesn’t mean it’s effective. While widely used, self-assessments can be unreliable. Discrepancies can occur for several reasons, including poor self-awareness, overconfidence, unintentional "self-presentation" bias, or, more seriously, candidate fraud. Many studies support the notion that people are notoriously inaccurate in subjective evaluation compared to objective measurements. Additionally, RedThread notes that this approach lacks specificity of the level of skills and doesn’t contribute to the company’s skills data set. That’s not to say there’s no place for worker self-reviews. As long as leaders recognize the limitations and risk, self-assessments can be a good, low-cost first step in identifying top talent early on. Giving potential employees an opportunity to showcase their abilities and skills contributes to a better hiring experience. 2. Performance Feedback / Informal Observation In this verification type, an observer validates skills through an informal set of standards using various modes of feedback and reviews. According to RedThread’s report, 37% of surveyed organizations use performance feedback in their skills verification processes - the single most-used method by a wide margin. This is possibly because even before adopting a skills-based recruiting strategy, performance feedback was already being used. These evaluations offer valuable insights into an employee's understanding and reveal any knowledge gaps by reflecting their overall performance over time or within a specific project. This approach contrasts with formal assessments, which isolate feedback to a single, often stressful event or test. One significant downside to note in this type of job skills assessment is that the observer’s feedback can be subjective and influenced by personal biases. 3. Formal Observation The key difference between formal and informal observation is that formal observation employs a specific framework to assess employee skills. A formal, structured set of standards empowers managers to develop the ability to hold difficult conversations. It enables the clear identification of areas of improvement, and it provides a foundation for coaching and knowledge transfer that helps improve performance levels. Even beyond actual performance and skills, observation can provide insight into so-called “soft skills,” such as how they handle pressure, adapt to new challenges, and interact with colleagues. It’s important to invest in the time and training needed to carry out effective, unbiased observation. Observers should factor in the possibility that employee apprehension may result in inconsistent results. Additionally, observation might not capture all aspects of an employee’s capabilities. 4. Formal Assessment Think tests, simulations, and sandboxes. RedThread reports that 53% of respondents who use formal job skills assessments do so because of compliance and regulatory requirements for certain roles, including necessary certifications or credentials. Formal assessments can be very valuable. They increase objectivity, help clarify the role for applicants (who may be defining the skill differently than you do), provide leaders with data, and save time for recruiters. However, they don’t always align with the role or tell you what you need to know. Paying attention to assessment quality is critical for the best outcomes in skills verification. Upskilling Your Career Skills Assessment Approach In this first installment of our exploration into skills verification approaches, the basic methods we’ve discussed serve as a foundational step. It's important to recognise, however, that these initial methods, while effective up to a certain point, might not suffice for roles requiring deeper or more specialized skill verification. And as the skills trend continues to evolve, leaders will increasingly desire more confidence, accuracy, or granularity in their skills data. In Part 2 of this series, we explore 3 more rigorous and comprehensive approaches to meeting the evolving demands of talent acquisition and employee upskilling programs. One Model: Skills-Based Recruiting Depends on Data One Model provides a people analytics platform that enhances skills-based recruiting by leveraging data-driven insights to identify skill gaps and predict future talent needs. We help organizations make more informed hiring decisions and better align their recruitment strategies with their business objectives. Learn how to build a people data platform that will allow you to do better skills-based hiring.
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5 min read
Gina Calvert
In Part 1 of our series on job skills assessments, we explored 4 simple ways to verify skills as identified by RedThread Research. RedThread members may access the full report authored by Heather Gilmartin and Dani Johnson. In Part 2, we delve into 3 sophisticated techniques that leverage both internal and external data to ensure a more accurate job skills assessment approach. As the landscape of skills-based recruiting expands, it becomes evident that some roles and contexts demand more nuanced and data-intensive verification methods than others. 1. Comparison to External Benchmarks When verifying skills, it’s crucial to measure them against established external standards. Yet, according to RedThread, only 11% of organizations do so. Benchmarking helps companies understand how their candidates' skills stack up against industry standards. In addition to providing a clear perspective on talent level relative to the broader market, it helps the organization future-proof their talent strategy and competitive edge. However, relying solely on external benchmarks may overlook unique aspects of a company’s culture or specific job roles that require customized skill sets. This approach also assumes that industry standards are up-to-date and sufficiently granular for an organization’s needs, which may not always be the case in fast-changing industries. Effective benchmarking relies on advanced skills intelligence tools, thus requiring an investment in technology or access to benchmarking data. As with other verification methods, benchmarks are most effective when used in conjunction with internal assessments. These platforms can integrate with existing HR systems to provide deeper insights and real-time data that help refine benchmarking efforts against industry standards. 2. Inference from HR Data Skills prediction based on HR data involves analyzing information from HR technology systems to infer employee skills. AI models predict employees’ skills based on a range of data sources. It’s quick, effective, and doesn’t require much employee involvement, RedThread explains. They report that 13% of employers currently make use of this career skills assessment method. This method uses historical data, such as past job performances, training records, and employee interactions, to predict skill levels and identify potential gaps. As it continues to evolve, the accuracy of skill predictions generally increases with the number of data points processed by AI. While powerful, this approach can be limited by the quality and completeness of the data collected. Biases in historical data can also lead to skewed predictions, making it essential to continuously update and review data inputs to ensure accuracy and fairness. HR data on industry skills is typically purchased through Human Resource Information (HRIS), Learning Management (LMS), Talent Marketplaces, Applicant Tracking (ATS), and Performance Management systems. Such systems enhance the accuracy of skills predictions by utilizing machine learning models which improve as they process more diverse and comprehensive data sets. 3. Inference from Work Data Using work system data to measure skills involves analyzing real-time data from work processes and outputs. By evaluating the quality, efficiency, and creativity of the work produced, organizations can gain a precise understanding of an employee's practical skills. This method requires sophisticated data analysis tools and expertise. It is also more complex than using HR data because it demands advanced technical integrations and substantial cross-functional collaboration to identify relevant metrics for specific skills. However, RedThread concludes that this is the only skills verification method that offers real-time insights into daily work and enables decisions at scale, based on performance data. This is where One Model shines, by seamlessly integrating with multiple data sources across the organization, enabling a more holistic and real-time assessment of employee skills based on actual work outputs. How One Model Partnerships Elevate Job Skills Assessment with Advanced Data-Driven Approaches Lightcast is a leading expert in the labor market. They collect and process a wide array of data, including job postings, resumes, and work history profiles. This data is aligned to job titles and skills classifications every two weeks. By merging Lightcast's extensive knowledge of the external labor market with One Model's ability to unlock people data, organizations can gain business insights relative to industry-wide talent trends. A One Model partnership empowers HR teams to: Enhance the consistency of data in reporting by adding standardized titles to current roles Analyze talent headcount, career paths, retention with accuracy Better align skills with job roles to enhance skills knowledge and plan for the future Ready to spend your time sharing insights, not integrating your people data? Learn how One Model integrations can help you see the whole picture. Or get a peek under the hood at how One Model could specifically benefit your organization. Request a demo.
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5 min read
Dennis Behrman
Transitioning to a data-driven HR system can be daunting. So our VP of Sales and Solutions Architect Leader, Phil Schrader, met up with Yuli Lopez, Partner and Principal at Culture Curated, to discuss best practices for HR leaders embarking on or looking for guidance on that journey. Yuli describes her own mindset to this transition in the video below. Read on for a few additional tips for anyone going through or leading this level of change and upheaval. Embrace a Growth Mindset Adapting to digital systems is a learning curve for everyone involved. It’s crucial to approach this transition with an open mind, ready to embrace new methodologies and technologies. Yuli was two steps ahead in this regard. “[As an HRVP], it was exciting to be able to have information that I had been chasing,” she said. For teams or individuals feeling nervous about or resistant to using data in HR, she encourages new users to “jump in and put time on your calendar for just exploring.” She’s right. Venturing beyond familiar or unexpected information can unveil insights you weren’t even aware you needed. This exploration can help users begin to understand how data stories are constructed from visibility into the details impacting employees. Of course, a willingness to experiment works best in a culture where mistakes are seen as opportunities for growth rather than failures. A growth mindset not only enhances individual capabilities but collectively elevates the organization. Practice Adaptability The transition to using data in HR is less a straight line and more a series of learning opportunities, commonly thought of as obstacles. In this journey, embracing change and having the ability to pivot your HR mindset is paramount. Leaders who quickly adapt to the unexpected and use every challenge as a stepping stone towards innovation will cultivate a flexible environment. Open dialogue will be the norm, ensuring that every team member feels they have a voice in this transformative process. Adaptability is undergirded by two key characteristics (Source) that leaders must both personify and incentivize employees to develop: Emotional Resilience: The inner strength required to navigate through challenges and preserve mental and emotional health during times of change. Personal Responsibility: The commitment to proactively manage how we react to change, ensuring we are in control of our own development and progress. In addition to technical training, coaching on the dynamics of change and change management can be useful. Prioritize Collaboration Digital transitions benefit significantly from diverse perspectives and expertise, not to mention a strong, collaborative team. Engaging team members in the planning and implementation phases ensures that the digital solutions adopted are user-friendly and genuinely address the needs of the organization. Yuli notes that it’s important to partner with other departments. “There may be other aspects you’re not thinking about. If you go to them with a hypothesis, together you may be able to draw unexpected insights. This collaborative approach not only facilitates smoother adoption but also strengthens the sense of ownership among staff, fostering a supportive environment for change. Create a Strong Visionary Perspective Vision casters are like seasoned captains navigating through uncharted waters. They have a keen eye on the distant horizon, focusing on the incredible benefits that lie ahead. For a data transition, that could be delivering impactful insights across your organization and easily translating workforce data into cost allocations. These visionaries don't just keep these exciting perspectives to themselves; they share them, painting a vivid picture of the future and recruiting buy-in for an efficient process. As both cheerleaders and coaches rolled into one, these leaders are in the trenches, reminding everyone why the upheaval of transition is worth it. They champion and model patience and persistence, highlighting what every step closer to using data in HR means for the team, the organization and clients. How One Model Helps These mindsets are fundamental for HR leaders guiding their departments through the digital transition, but the technology of choice plays an enormous role in the outcomes of the journey. One Model provides the people analytics solution technology that orchestrates everything decision makers need to be able to quickly make brilliant workforce decisions.
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7 min read
Dennis Behrman
Few tasks can be as perplexing — and oddly satisfying — as the alchemy of turning headcount numbers into meaningful cost allocations by work days in a month and then having the option to break it down by department or any other variable you desire. With business demands rapidly evolving, the age-old adage of "time is money" has never been more accurate. Yet navigating the complexities of cost allocation, also referred to as overhead allocation, and crafting the perfect cost allocation plan can be a Herculean task. As you may know, cost allocation involves the identification and allocation of expenses to various activities, individuals, projects, or any relevant cost-related entities. Its primary objective is to equitably distribute costs among different departments, facilitate profitability calculations, and establish transfer pricing. Essentially, cost allocation serves as a means to gauge financial performance and enhance the decision-making process. Since your employees are by in large your greatest investment, understanding their cost allocation on many levels has immense benefits. As Phil shows in the video above, One Model makes this process seamless — and it’s all thanks to the power of our data orchestration model. Learn more about our People Data Cloud Platform. The Changing Landscape of HR Data It is no longer enough to get a holistic cost allocation from your headcount. Organizations across the globe need to be able to slice and dice their data to really understand how those costs are changing over time and how to best build a thriving workforce. Traditional views showing headcount over time are excellent starters, but the main course? That's translating those numbers into actionable cost insights. After all, understanding not just the size but also the cost of your workforce over time is the key to informed decision-making for both finance and operations teams. For example, slicing and dicing dynamic cost allocation over time, like total days in month breakout and broken down by department, supervisor hierarchy level, or by length of time employed can lead to insights that can change policy or articulate critical headcount needs. How does One Model accomplish this? One Model possesses unique capabilities that can transform your traditional headcount chart into a sophisticated cost analysis tool. What makes us unique? It all has to do with the data model. Once your data is modelled, you gain access to a variety of metrics that you can use as is or modify to fit your specific business needs. Diving into your compensation grouping of metrics, you can replace the “headcount, end of period” metric with “headcount, beginning of period” or append it with the “average salary, end of period” metric. Delving deeper, the real magic happens as One Model enables you to convert that average salary into a robust cost allocation strategy. With the dynamic "compensation cost daily allocation" metric at your disposal, it's like having a personal assistant that adjusts effortlessly to varying time durations, including accommodating leap years. Furthermore, One Model recognises the fluctuations in costs, especially during shorter months or leap years, ensuring a more precise and insightful view of your financial landscape. This capability allows you to make more informed decisions and gain a deeper understanding of your organisation's financial dynamics. Segmenting Cost Allocation Metrics Each organisation is akin to a mosaic, with numerous sections and subdivisions. With One Model, you can delve into each segment, examining the cost allocation intricacies at every level. The insights gleaned can empower both finance and operations professionals, offering clarity in strategy and resource allocation. Why is Overhead Allocation such an important metric? Cost allocation is crucial for various reasons in business and financial management. Here are four key reasons why it's important to pay attention to cost allocation: Fairness and Equity Overhead allocation ensures that costs are distributed fairly among different departments, products, or projects. This fairness is essential for budget allocation and growth in each department. Performance Measurement Allocating costs accurately allows for better measurement of the performance of different departments or business segments. By attributing costs to specific activities, it becomes easier to identify areas of inefficiency and make necessary improvements. Profitability Analysis Cost allocation helps in determining the profitability of products, services, or business units. This information is invaluable for making strategic decisions about resource allocation, product pricing, and business expansion. However, read our other considerations when breaking down revenue in our average revenue per employee blog. Resource Allocation When costs are allocated appropriately, organisations can allocate resources more effectively. It helps in identifying where additional resources are needed and where resources might be overallocated, leading to cost savings. Visualising Cost: The Power of Representation One Model lets you visualise your cost allocation journey over time through detailed charts. While this can present a plethora of data, each data point offers invaluable insights. For those who prefer a more structured representation, a tabulated view can provide clarity. All you need to do is create a data set that shows the amount of cost to allocate, along with the start and end dates of that allocation. From current headcount to cost allocation for recruiting, the process to get the answer is the same. For example, if you spent $10,000 on job advertisements on LinkedIn from Jan. 1, 2018, to Dec. 31, 2018, One Model can efficiently allocate that spend per day throughout the year. This becomes very useful when combined with other metrics over periods of time. For example, I can compare what I'm spending on LinkedIn with the number of applications I receive from LinkedIn during that period. This yields a "Cost Per Application" metric that I can use to compare the effectiveness of LinkedIn relative to other sources. The Takeaway If the daunting task of juggling countless spreadsheets, numbers, and formulas sounds all too familiar, there's a better way. One Model is designed to transform the perplexing world of cost allocation and overhead allocation and creating a tailored cost allocation plan into a more straightforward, efficient process. So, if late-night data crunching is your current reality, it's time to explore the capabilities of One Model. Let us show you how One Model does this 1:1
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10 min read
Joe Grohovsky
John Sumser, one of the most insightful industry analysts in HR, recently wrote an article providing guidance on the selection of machine learning/AI tools. That article is found HERE, and can serve as a rubric for reviewing AI and predictive analysis tools for use in your people analytics practice or HR operations. Much of our work day is filled with conversations regarding the One Model tool and how it fits into an organization's People Analytics initiative. This is often the first practical exposure a customer contact has using Artificial Intelligence (AI), so a significant amount of time is invested in explaining AI and the dangers of misusing it. Good Questions to Ask About Artificial Intelligence Solutions - And Our Answers! Our product, One AI, delivers a suite of easy-to-use predictive pipelines and data extensions, allowing organizations to build, understand, and predict workforce behaviors. Artificial Intelligence in its simplest form is about automating a decision process. We class our predictive modeling engine as AI because it is built to automate the decisions usually made by a human data scientist in building and testing predictive models. In essence, we’ve built our own automated machine learning toolkit that rapidly discovers, builds, and tests many hundreds of potential data features, predictive models, and parameter tuning to ultimately select the best fit for the business objective at hand. Unlike other predictive applications in the market, One AI provides full transparency and configurability, which implicitly encompasses peer review. Every predictive output is not only peered reviewable within a given moment of time but also for all time. This post will follow a Q&A style as we comment on each of John’s 12 critical questions to ask an artificial intelligence company. 1) Tell me about the data used to train the algorithms and models. Ideally, all data available to One Model is used for feeding the machine learning engine - the more the better. You cannot overload One AI because it is going to wade through everything you throw at it and decide which data points are relevant, and how much history it should use, and then select, clean, and position that data as part of its process. This means we should feed every single system we have available into the engine from the HRIS, ATS, Survey, Payroll, Absence, Talent Management - everything and the kitchen sink as long as we’re ethically okay with its potential use. This is not a one size fits all algorithm; each model is unique to the customer, their data set, and their target problem. The content of training data can also be user-defined. Users define what type of data is brought into the modeling process, choosing which variables, filters, or cuts will be offered. At any time if users want to specify how individual fields will be treated, they have the ability to do so with the same types of levers as you would have in creating your own model externally. 2) How long will it take for the system to be trained? The scope of data and the machine learning pipeline determine training time. The capacity to create models is intrinsically available in One AI and training can take anywhere from 5 minutes to 20+ hours. For example, we automatically schedule re-training a turnover prediction model for a 15k employee-customer in the space of 45 minutes. 3) Can we make changes to our historical data? Yes, data can be set to be held static or use fresh data every time the model is trained. One AI acts as a data science orchestration toolkit that automates the data refresh, training, build and ongoing maintenance of the model. Models are typically scheduled to potentially refresh on a regular basis e.g. monthly. With every run extensive reports are created, time-stamped, and logged so users can always return to summary reports of what the data looked like, the decisions made, and the performance of the model at any given time. 4) What happens when you turn it off? How much notice will we receive if you turn it off? One AI models and pipelines are completely persisted. They can be turned on and off with no loss of data or logic. We are a data science orchestration toolset for building and managing predictive models at scale. Is AI being offered in a solution for your HR Team? Download our latest whitepaper to get the questions you should ask in the next sales pitch when someone is trying to sell you technology with AI. 5) Do we own what the machine learned from us? How do we take those data with us? Yes, customers own the results from their predictive models, and those results are easily downloaded. Results and models are based upon your organizations data. One Model customers only see their own results, and these results are not combined with other data for any purpose. All the decisions that the machine made to select a model are shown and could be used to recreate the model externally as well. 6) What is the total cost of ownership? Predictive modeling, along with all features of our One AI product, are inclusive within the One Model suite subscription fee. 7) How do we tell when the models and algorithms are “drifting”? Each predictive model is generated and its results are fully transparent. Once a One AI run is finished, two reports are generated for review: Results Summary – This report details the model selected and its performance. Exploratory Data Analysis – This report details the state of the data that the model was trained on so users can determine if the present-state data has changed drastically. Models are typically scheduled to be re-trained every month with any new data received. The new models can be compared to the previous model using the output reports generated. It is expected that models will degrade over time and they should be replaced regularly with better performing models incorporating recent data. This is a huge burden on a human team, hence the need for data science orchestration automating the manual process and taking data science delivery to scale. 8) What sort of training comes with the service? One Model’s customers are trained on all aspects of our People Analytics tool. Training is offered for non-Data Scientists to be able to interpret the Results Summary and Exploratory Data Analysis reports so they can feel comfortable deploying models. A named One Model Customer Service Manager is available to aid and provide guidance if needed. 9) What do we do when circumstances change? One AI is built with change in mind. If the data changes in a way that breaks the model or the model drifts enough that a retrain is necessary, users can restart the automated machine learning pipelines to bring in new data and create a new pipeline. The new model can be compared to the previous model. One AI also allows work to occur on a draft version of a model while the active model is being run in production. 10) How do we monitor system performance? The Results Summary and Exploratory Data Analysis charts provide extensive model performance and diagnostic data. Actual real-world results can be used to assess the performance of the model by overlaying predictions with outcomes within the One Model application. This is also typically how results are distributed to users through the main analytics visualization toolsets. When comparing actual results against predictions, One Model cautions users to be aware of underlying data changes or company behaviors skewing results. For example, an attrition model may identify risk due to an employee being under-trained. If that employee is then trained and chooses to remain with the organization, then the model may have been correct but because the training data changed results can’t really be compared. In the case of this employee their risk score today would be lower than their risk score from several months ago prior to training. The action to provide additional training may indeed have been a response from the organization to address the attrition risk, and actions like these that are specifically made to address risk must also be captured to inform the model if mitigation actions have taken place. The Results Summary and Exploratory Data Analysis reports typically build enough trust in cross-validation that system performance questions are not an issue. 11) What are your views on product liability? One AI provides tooling to create models along with the reports for model explanation and interpretation of results. All models and results are based exclusively on a customer’s own data. The customer must review the model’s results and choose to deploy and how they use those results within the organization. We provide transparency into our modeling and explanations to provide confidence and knowledge of what the machine is doing and not just trusting a black box algorithm is working (or not). This is different from other vendors who may deliver inflexible canned models that were trained on data other than the customers or are inflexible to use a unique customer data set relevant to the problem. I would be skeptical of any algorithm that cannot be explained or its performance tracked over time. 12) Get an inventory of every process in your system that uses machine intelligence. Each One Model customer decides how specific models will be run for them, and how to apply One AI. These predictive models typically include attrition risk, time to fill, promotability, and headcount forecast. Customers own every model and the result generated within their One Model tool. One AI empowers our customers to combine the appropriate science with a strong awareness of their business needs. Our most productive One AI users utilize the tool by asking it critical business questions, understanding all relative data ethics, and providing appropriate guidance to their organization. If you would like to learn more about One AI, and how it can address your specific people analytics needs, schedule some time with a team member below.
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6 min read
Phil Schrader
It’s always good news when a prospective One Model customer tells me that they use SuccessFactors for recruiting. Given that HR technology in general and applicant tracking systems in particular seldom involve feelings of pleasure, my statement bears a bit of explanation. I wouldn’t chalk it up to nostalgia, though like many members of the One Model team, I had a career layover at SuccessFactors. Instead, my feelings for SuccessFactors recruiting are based on that system’s unique position in the evolution of applicant tracking systems. I think of SuccessFactors as the “Goldilocks ATS”. On one hand, SFSF doesn’t properly fit in with the new generation of ATS systems like SmartRecruiters, Greenhouse, or Lever. But like those systems, SFSF is young enough to have an API and for it to have grown up in a heavily integrated technology landscape. On the other hand, SFSF can’t really be lumped in with the older generation of ATS systems like Kenexa and Taleo either. However, yet again, it is close enough to have picked up a very positive trait from that older crowd. Specifically, it still manages to concern itself with the mundane task of, ya know, tracking applicant statuses. (Yeah, yeah, new systems, candidate experience is great, but couldn’t you also jot down when a recruiter reviewed a given application and leave that note somewhere where we could find it later without building a report???) In short, SFSF Recruiting is a tweener and better for it. If you are like me, and you happen to have been born in the fuzzy years between Gen X and Millennials, then you can relate: you're young enough to have been introduced to web design and email in high school, and old enough to have not had Facebook and cell phones in college. So let’s take a look at the magic of tracking application status history using data from SuccessFactors RCM, an applicant tracking system. While it seems like a no-brainer, not all ATSs provide full Application Status history via an API. Since it's basically the backbone of any type of recruiting analytics, it's fortunate that SuccessFactors does provide it. For those of you who want to poke around in your own data a bit, the data gets logged in an API object called JobApplicationStatusAuditTrail. In fact, not only is the status history data available, but custom configurations are accounted for and made available via the API as well. This is one of the reasons why at One Model we feel that without a doubt, SuccessFactors has the best API architecture for getting data out to support an analytics program. Learn more about our SuccessFactors integration. But there is something that not even the Goldilocks ATS can pull off -- making sense of the data. It’s great to know when an application hits a given status, but it’s a mistake to think that recruiting is a calm and orderly process where applications invariably progress from status to status in a logical order. In reality, recruiters are out there in the wild doing their best to match candidates with hiring managers in an ever-shifting context of business priorities, human preferences, and compliance requirements. Things happen. Applicants are shuffled from requisition to requisition. Statuses get skipped. Offers are rescinded. Job requisitions get cancelled without applicants getting reassigned. And that’s where you need a flexible people analytics solution like One Model. You’ll probably also want a high-end espresso machine and a giant whiteboard because we’re still going to need to work out some business logic to measure what matters in the hectic, nonlinear, applicant-shuffling real world of recruiting. Once we have the data, One Model works with customers to group and order their application statuses based on their needs. From there, the data is modeled to allow for reporting on the events of applications moving between statuses as well as the status of applications at any point in history. You can even look back at any point in time and see how many applications were at a particular status alongside the highest status those applications eventually made it to. And yes - we can do time to fill. There are a billion ways of calculating it. SuccessFactors does their customers a favor by allowing them to configure how they would like to calculate time to fill and then putting the number in a column for reporting. If you're like most customers though, one calculation isn't enough. Fortunately, One Model can do additional calculations any way you want them-- as well as offering a "days open" metric and grouped dimension that's accurate both current point in time as well as historically. “Days in status” is available as well, if you want to get more granular. Plus, on the topic of time to fill, there’s an additional tool in One Model’s toolkit. It’s called One AI and it enables customers to utilize machine learning to help predict not only time to fill, but also the attributes of candidates that make them more likely to receive an offer or get hired. However, that is another topic for another day. For today, the good news is that if you have SuccessFactors Recruiting, we’ll have API access to the status history data and customizations we need to help you make sense of what's going on in recruiting. No custom reports or extra connections are required. Connecting your ATS and HRIS data also means you can look at metrics like the cost of your applicant sourcing and how your recruiters are affecting your employee outcomes long term. So here’s to SuccessFactors Applicant Tracking System, the Goldilocks ATS. Ready to get more out of SuccessFactors? Click the button below and we'll show you exactly how, and how fast you can have it. **Quick Announcement** Click here to view our Success with SuccessFactors Webinar recording and learn how to create a people data strategy!
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10 min read
Phil Schrader
The One Model difference that really sets us apart is our ability to extract all your messy data and clean it into a standardized data catalog. Let's dive deeper. One Model delivers people analytics infrastructure. We accelerate every phase of your analytics roadmap. The later phases of that roadmap are pretty fun and exciting. Machine learning. Data Augmentation. Etc. Believe me, you’re going to hear a ton about that from us this year. But not today. Today we’re going to back up for a minute and pay homage to an absolutely wonderful thing about One Model: We will help you clean up your data mess. Messy Data? Don't distress. Josh Bersin used this phrasing in his talk at the People Analytics and the Future of Work conference. From my notes at PAFOW on Feb 2, 2018: You know there are huge opportunities to act like a business person in people analytics. In the talk right before Josh’s, Jonathan Ferrar reminded us that you get $13.01 back for every dollar you spend on analytics. But you have to get your house in order first. And that’s going to be hard. Our product engineering team at One Model has spent their careers figuring out how to pull data from HR systems and organizing it all into effective data models that are ready for analytics. If your team prefers, your company can spend years and massive budgets figuring all this out... Or, you can take advantage of One Model. When you sign up with One Model: 1) We take on responsibility for helping you extract all the data from your HR systems and related tools. 2) We connect and refine all that data into a standard data catalog that produces answers your team will actually trust. Learn what happened to Synk when they finally had trust. Big data cleansing starts with extracting the data from all your HR and related tools. We will extract all the data you want from all the systems you want through integrations and custom reports. It’s part of the deal. And it’s a big deal! For some perspective, check out this Workday resource document and figure out how you’ll extract your workers’ FTE allocation from it. Or if Oracle is your thing, you can go to our HRIS comparison blog and read about how much fun our founder, Chris, had figuring out how to get a suitable analytics data set out of Fusion. In fact, my coworker Josh is pulling some Oracle data as we speak and let me tell you, I’m pretty happy to be working on this post instead. Luckily for you, you don’t need to reinvent this wheel! Call us up. We’ll happily talk through the particulars of your systems and the relevant work we’ve already done. The documentation for these systems (for the most part) is out there, so it’s not that this is a bunch of classified top-secret stuff. We simply have a lot of accumulated experience getting data out of HR systems and have built proprietary processes to ensure you get the most data from your tools. In many cases, like Workday, for example, we can activate the custom integration we’ve already built and have your core data set populated in One Model. If you go down that road on your own, it’ll take you 2 - 3 days just to arrange the internal meeting to talk about how to make a plan to get all this data extracted. We spent over 10,000 development hours working on our Workday extraction process alone. And once you do get the data out, there’s still a mountain of work ahead of you. Which brings us to... The next step is refining your extracted data into a standardized data catalog. How do you define and govern the standard ways you are going to analyze your people data? Let’s take a simple example, like termination rate. The numerator part of this is actually pretty straightforward. You count up the number of terminations. Beyond that, you will want to map termination codes into voluntary and involuntary, exclude (or include) contractors, etc. Let’s just assume all this goes fine. Now what about the bottom part? You had, say 10 terminations in the given period of time, so your termination rate is... relative to what headcount? The starting headcount for that period? The ending headcount? The average headcount? How about the daily average headcount? Go with this for two reasons. 1) It’s the most accurate. You won’t unintentionally under or overstate termination rate, giving you a more accurate basis of comparison over time and the ability to correctly pro-rate values across departments. See here for details. And 2) If you are thinking of doing this in-house, it’ll be fun to tell your team that they need to work out how to deliver daily average headcounts for all the different dimensions and cuts to meet your cleaning data requirements. If you really want to, you can fight the daily average headcount battle and many others internally. But we haven’t even gotten to time modeling yet, which is so much fun it may get its own upcoming One Model Difference post. Or the unspeakable joy you will find managing organizational structure changes, see #10. On the other hand, One Model comes complete with a standard metrics catalog of over 590 metrics, along with the data processing logic and system integrations necessary to collect that data and calculate those metrics. You can create, tweak, and define your metrics any way you want to. But you do not have to start from scratch. If you think about it. This One Model difference makes all the difference. Ultimately, you simply have to clean up your messy data. We recognize that. We’ve been through it before. And we make it part of the deal. Our customers choose One Model because we're raising the standard and setting the pace for people analytics. If you are spending time gathering and maintaining data, then the yardstick for what good people analytics is going to accelerate away from you. If you want to catch up, book a demo below and we can talk. Tell us you want to meet. About One Model: One Model helps thriving companies make consistently great talent decisions at all levels of the organization. Large and rapidly-growing companies rely on our People Data Cloud™ people analytics platform because it takes all of the heavy lifting out of data extraction, cleansing, modeling, analytics, and reporting of enterprise workforce data. One Model pioneered people data orchestration, innovative visualizations, and flexible predictive models. HR and business teams trust its accurate reports and analyses. Data scientists, engineers, and people analytics professionals love the reduced technical burden. People Data Cloud is a uniquely transparent platform that drives ethical decisions and ensures the highest levels of security and privacy that human resource management demands.
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8 min read
Dennis Behrman
Ever play with a Magic 8 Ball? Back in the day, you could ask it any question and get an answer in just a few seconds. And if you didn't like its response, you could just shake it again for a new prediction. So simple, so satisfying. Today's HR teams and businesses obviously need more reliable ways of predicting outcomes and forecasting results than a Magic 8 Ball. But while forecasting and predicting sound similar, they're actually two different problem-solving techniques. Below, we'll go over both and explain what they're best suited for. What is HR Forecasting? At first glance, the Magic 8 ball "predicts" or "forecasts" an answer to your question. This is not how forecasting works (at least, for successful companies or HR departments). Instead, HR forecasting is a process of predicting or estimating future events based on past and present data and— most commonly—by analysis of trends. "Guessing" doesn't cut it. For example, we could use predictive forecasting to discover how many customer calls Phil, our product evangelist, is likely to receive in the next day. Or how many product demos he'll lead over the next week. The data from previous years is already available in our CRM, and it can help us accurately predict and anticipate future sales and marketing events where Phil may be needed. A forecast, unlike a prediction, must have logic to it. It must be defendable. This logic is what differentiates it from the Magic 8 ball's lucky guess. After all, even a broken watch is right two times a day. What is Predictive Analytics? Predictive analytics is the practice of extracting information from existing data sets in order to determine patterns and trends that could potentially predict future outcomes. It doesn't tell you what will happen in the future, but rather, what might happen. For example, predictive analytics could help identify customers who are likely to purchase our new One AI software over the next 90 days. To do so, we could indicate a desired outcome (a purchase of our People Analytics software solution) and work backwards to identify traits in customer data that have previously indicated they are ready to make a purchase soon. (For example, they might have the decision-making authority on their People Analytics team, have an established budget for the project, completed a demo, and found Phil likeable and helpful.) Predictive modeling and analytics would run the data and establish which of these factors actually contributed to the sale. Maybe we'd find out Phil's likability didn't matter because the software was so helpful that customers found value in it anyway. Either way, predictive analytics and predictive modeling would review the data and help us figure that out — a far cry from our Magic 8 ball. Which Do You Need? Which do you need—forecasting or predictive analytics? The short answer: probably both. Forecasting helps you project trends—like how headcount or attrition rates might shift over time. It’s great for setting expectations and planning ahead. Predictive analytics, on the other hand, digs into the "why" behind those trends. It identifies patterns and flags specific risks or opportunities—like which employees are most likely to leave or what factors drive engagement. Together, they give you both the big picture and the specific actions to take. Ready to explore predictive analytics? We can help you figure it out and get started. Connect with us. Forecasting vs. Predictive Analysis: Other Relevant Terms Machine Learning - machine learning is a branch of artificial intelligence (ai) where computers learn to act and adapt to new data without being programmed to do so. The computer is able to act independently of human interaction. Read Machine Learning Blog. Data Science - data science is the study of big data that seeks to extract meaningful knowledge and insights from large amounts of complex data in various forms. Data Mining - data mining is the process of discovering patterns in large data sets. Big Data - big data is another term for a data set that's too large or complex for traditional data-processing software. Learn about our data warehouse. Predictive Modeling - Predictive modeling is a form of artificial intelligence that uses data mining and probability to forecast or estimate more granular, specific outcomes. Learn more about predictive analytics. Descriptive Analytics - Descriptive analytics is a type of post-mortem analysis in that it looks at past performance. It evaluates that performance by mining historical data to look for the reasons behind previous successes and failures. Prescriptive Analytics - prescriptive analytics is an area of business analytics dedicated to finding the potential best course of action for a given situation. Data Analytics - plain and simple, data analytics is the science of inspecting, cleansing, transforming, and modeling data in order to draw insights from raw information sources. People Analytics - All these elements are important for people analytics. Need basics? Learn more about People Analytics. About One Model One Model’s People Analytics solutions help thriving companies make consistently great talent decisions at all levels of the organization. Large and rapidly-growing companies rely on our People Analytics platform and One AI because they takes all of the heavy lifting out of data extraction, cleansing, modeling, analytics, and reporting of enterprise workforce data.
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14 min read
Lisa Meehan
The way a company structures its workforce is crucial to its success. Workforce structures determine how employees are organised, how work is delegated, and how communication flows throughout the organisation. Workforce structures refer to the way a company organises its employees, financial responsibilities, and the relationships among them. It provides a framework for managing and coordinating work activities. There are several types of enterprise structures and your organisation uses several of them, so let’s talk through different ones and see how you can visualise them. Types of workforce structures Most workforce structures can best be displayed as an org chart. An organisational chart, or org chart, is an essential tool for any enterprise structure as it provides a clear and concise visual representation of the hierarchy, roles, and relationships among employees. It enables employees to understand where they fit into the organisation and how their role contributes to the overall goals of the company. Functional One of the most common structures in business today, a functional organisational structure groups employees according to the functions they perform, such as marketing, accounting, or operations. This allows for specialised expertise in each function, where everyone has a defined role and clear lines of communication. Location and structural overlay It may be that only a division of the company is broken up into location-based structures. For instance, this can be common in sales or HR talent acquisition departments where you have an East, Northeast division of responsibility. Supervisor hierarchy You may have also heard of position hierarchy or supervisory hierarchy. This is a slight modification of the traditional Hierarchical model. You can see these a lot in support or in places within the organisation where approvals are needed. Hierarchy establishes the connection between a superior and the subordinates within an organisation. The supervision hierarchy report exhibits the designated supervisor, presenting their immediate reports, followed by their respective reports, and so on. It encompasses the option to include the employee number, along with the name and job title of each individual, based on the chosen level of supervisory depth. A supervisor hierarchy shows who reports to who. It refers to the structure of reporting relationships within an organisation, where supervisors are responsible for overseeing the work and performance of their subordinates. In a typical supervisor hierarchy, each supervisor has a team of employees reporting directly to them. Often for the people running those units, there is a 1:1 but that’s not always perfect. The supervisors themselves report to higher-level managers or executives, forming a chain of command. The reporting relationships follow a top-down approach — with information, instructions, and feedback flowing from higher-level supervisors to lower-level employees. This hierarchical structure ensures clear lines of authority, accountability, and efficient communication within the organisation. Cost centre structure hierarchy The cost centre structure refers to the total collection of different cost types, including both fixed and variable expenses, that constitute the overall expenditures of a business. This is where the financials are run. It’s normally wrapped up in the chart of accounts. Organisations use the cost centre structure to establish pricing and pinpoint opportunities for minimising costs. This is typically how the finance system works and who is financially accountable for the funds that they spend. This can be different from who runs the business units. Therefore, this view can often be out of alignment with the structural hierarchy. To put it simply, it’s because Finance runs the financials and HR runs the business structure. This type of view often coincides with internal company political struggles. Why? Finance likes to be in control of its space and typically doesn’t like HR veering into it. But if HR can get a cost structure into a people data view, it’s typically a good thing. For instance, this will allow the finance team to get activity- or project-based accounting, or the total cost of the project including the hard numbers and people resources to make real assessments on the ROI of various initiatives. You can only get this view when finance and people data are combined. Matrix structure While not easy to visualise, this structure is really important to get right. A Matrix workforce structure generally refers to a type of organizational setup where employees are assigned to multiple reporting lines or managers simultaneously, as opposed to a traditional hierarchical structure where each employee reports to only one manager. In a matrix workforce, individuals are part of cross-functional teams and can work on various projects simultaneously, often with different sets of colleagues and supervisors. The matrix structure is most often used in large, complex organizations that handle multiple projects simultaneously and require a high degree of collaboration across departments. It is commonly found in industries such as technology, engineering, consulting, and pharmaceuticals. Additionally, matrix structures are prevalent in multinational corporations, where teams need to coordinate and work across different geographical regions. How does One Model help? As you can see, getting different views of the various structures within your business can have profound impacts on your understanding. One Model creates alignment for customers, so they can pivot between those different views with the included people insights. This is really important so you can create a mapping between your financial structures and people structures to become the translator that brings that world together within the organisation. Senior leaders typically want to see where all the money is being spent and where the people are so they can make informed decisions. So views that bring this data together make One Model incredibly valuable to our current customers. Our products empower you to change the view with a click of a button, so you get a complete view of what is actually going on. You can also cross-tabulate those views and link them together. Want to see One Model in action? Watch this quick demo video. How security plays into analysing workforce structures A basic organisational breakout may not be too concerning, but once you start applying analytics to your charts to get a better understanding of how key insights or talents are distributed throughout your organisation, you run into issues. That’s why having a tool like One Model with strong roles-based security that locks sensitive information to specific roles allows you to create a public view that instantly keeps your data safe. Security plays a crucial role in analysing workforce structures by focusing on access controls, user authentication, data protection, security awareness, incident response, vendor and third-party risk, and compliance with regulations. By incorporating security considerations into workforce analysis, organisations can identify vulnerabilities, mitigate risks, and establish a robust security foundation for their operations. Explore the evolution of workforce models Want to learn more about the evolution of workforce planning models over the past four decades and the key role that enterprise segmentation plays in achieving great analytics? Watch our webinar with Peter Howes, a thought leader and pioneer in the field of analytics and strategic planning models. He discusses how these structures have changed to a more strategic approach that’s focused on meeting the needs of the business. 7 benefits of incorporating people analytics into your workforce structures Incorporating people analytics into various workforce structures can provide organisations with valuable insights and significant benefits. People analytics, also known as HR analytics or workforce analytics, involves gathering and analysing data about employees to make informed decisions and improve organisational performance. Here are seven ways incorporating people analytics can positively impact workforce structures: 1. Data-Driven Decision-Making: People analytics paired with workforce structure views allows organisations to base their decisions on objective data rather than relying solely on intuition or anecdotal evidence. By overlaying workforce data on top of various structures, organisations can gain insights into critical aspects such as employee performance, engagement, turnover, and productivity to quickly see where trouble resides in the business. These data-driven insights enable more informed decision-making in areas like talent acquisition, talent development, succession planning, and performance management. 2. Talent Acquisition and Retention: People analytics inserted into your workforce structure views can highlight where your most loyal and high-performing employees exist. Seeing this allows you to identify the most effective recruitment channels, evaluate candidate profiles, and predict the likelihood of candidate success — so your team can build impactful strategies. By analysing data on employee turnover and retention, organisations can better visualise the factors influencing attrition rates and develop targeted retention strategies. It can also facilitate the identification of high-potential employees for succession planning and talent development initiatives. 3. Performance Management: Incorporating people analytics into an enterprise structure allows organisations to evaluate employee performance objectively and uncover great leaders and employees. By analysing performance data, organisations can identify top performers, evaluate goal attainment, and provide targeted feedback and development opportunities. People analytics can also help uncover performance patterns and trends, enabling managers to make data-driven decisions regarding promotions, rewards, and recognition. 4. Employee Engagement and Satisfaction: Organisational structures paired with people analytics provides a map of employee engagement levels, job satisfaction, and factors that impact overall employee experience. This will quickly allow you to understand the health of various teams within your business. By analysing data from employee surveys, feedback platforms, and other sources, organisations can identify areas for improvement and take proactive measures to enhance employee engagement and satisfaction. 5. Workforce Planning and Optimisation: Workforce hierarchy paired with people analytics plays a vital role in strategic workforce planning and optimisation. By analysing workforce data, organisations can assess their workforce's current and future needs, identify employee gaps, and develop strategies for workforce development and succession planning. People analytics can also help optimise workforce structures by identifying areas of organisational inefficiency or redundancy, enabling resource allocation and restructuring initiatives. 6. Diversity and Inclusion: Where do your DE&I community members reside in your org? Which areas of the business are most diverse? Incorporating people analytics into your workforce structure can support diversity and inclusion efforts by analysing demographic data. This allows organisations to assess representation, identify potential biases, and implement targeted diversity and inclusion initiatives. 7. Predictive Analytics and Future Insights: People analytics enables organisations to leverage predictive analytics to forecast future trends and outcomes related to the workforce. By analysing historical data, organisations can identify patterns and make predictions about attrition rates, talent shortages, skill requirements, and workforce needs. These insights allow proactive planning and decision-making, ensuring the organisation is prepared for future workforce challenges. In summary, workforce structures already exist in your organisation, the question is can you use them to better understand your business and create efficiencies? If you can’t, or if the process is a major project for your HR team, then you need to consider people analytics software like One Model that empowers you to transform how your leaders make decisions. We’d love to show you how One Model can help your organisation make better talent decisions. Request a demo today!
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13 min read
Nicholas Garbis
At some point, every successful People Analytics team will develop a meaningful partnership with the Finance organization. Unfortunately, this partnership is usually not easily achieved and it's quite normal for initial alignment efforts to last for a couple of years (or more!). We are delighted to repost this insightful blog post authored by Nicholas Garbis on May 4, 2021. Revisiting his valuable insights will help us all foster a deeper understanding of how HR and Finance can collaborate more effectively. A new or maturing People Analytics team may fail to recognize the effort level required and not prioritize the work needed to establish this critical partnership with Finance. They do so at their own peril. The day will inevitably arrive when a great analytics product from the PA team will be dismissed by senior leaders when they see the foundational headcount numbers do not match. The PA team will be lacking in a clear explanation that is supported by the CFO and Financial Planning & Analysis (FP&A) leaders. But why is this the case? And how can HR and People Analytics teams do a better job of establishing the partnership? Analyzing the analytics conflicts between finance and HR Lack of alignment on workforce data At the heart of the issue is a lack of alignment on the most basic workforce metric: headcount. Both Finance and HR teams are often sharing headcount data with senior leaders. In many companies, the numbers are different. This creates distrust and frustration, and I will contend that, given Finance’s influence in most organizations, the HR team is on the losing end of these collisions. End result is that the organization spends time debating the figures (at a granular level) and misses the opportunity to make talent decisions that support the various company strategies (eg, growth, innovation, cultural reinvention, cost optimization). While headcount is at the foundation, there are several other areas where such disconnects arise and create similar challenges: workforce costs, contingent workers, position management, re-organizations, workforce budgets/plans, movements, etc... Solving the basic headcount alignment is the first step in setting the partnership. Source of the Disconnect: "Headcount Dialects" and "Dialectical Thinking" The disconnect in headcount figures is nearly always one of definition. Strange as it may sound, Finance and HR do not naturally count the workforce in the same way. It's as if there is a 'headcount dialect" that each needs to learn in order to communicate with the other. Therefore, if they have not spent some intentional, focused time on aligning definitions and processes, they will continue to collide with each other (and HR will fail to gain the trust needed to build an analytics/evidence-based culture around workforce decisions). The dialectical thinking challenge is for Finance and HR to recognize that the same data can be presented in (at least) two different ways and both can be simultaneously accurate. It is for the organization to determine which definition is considered "correct" for each anticipated use case (and then stick to that plan). Primary disconnection points Two primary areas of disconnect are the definition of the term “headcount” and whether a cost or organizational hierarchy is being used. Definition of “Headcount”: There are several components of this, underscoring the need for alignment when it comes to finance headcount vs HR headcount. Using Full-Time Equivalent (FTE) or Employee Count: Employees that are working less than full-time are often in the system with FTE values of 1.0 (full-time), 0.5 (half-time), and every range of fraction in between. The Employee Count, on the other hand, will count each employee as 1 (sometimes lightly referred to as a “nose count” to distinguish it from the FTE values). In some companies, interns/co-op employees are in the system with FTE value of 0, even though they are being paid. Determining Which Status Codes are to be Included: Employees are captured in the HR system as being active or inactive, on short-term or long-term leave of absence (LOA, “garden leave”), and any number of custom values that are used to align with the HR processes. In many companies, the FTE values are updated to align with the change in status. Agreeing on which status codes are counted in "headcount" is required for setting the foundation. Organization versus Cost Hierarchy: The headcount data can be rolled up (and broken down) in at least two ways: based on the organization/supervisor hierarchy structure or based on the cost center/financial hierarchy. Each has its unique value, and neither is wrong -- they are simply two representations of the same underlying data. It’s quite common that insufficient time has been spent in aligning, reconciling, and validating these hierarchies and determining which one should be used in which situations. Organization Hierarchy: This is sometimes called the “supervisory hierarchy” and represents “who reports to whom” up the chain of command to the CEO. This hierarchy is representative of how work is being managed and how the workforce is structured. Each supervisor, regardless of who is paying for their team members, is responsible for the productivity, engagement, performance, development, and usually the compensation decisions, too. Viewing headcount through the organization hierarchy will provide headcount values (indicating the number of resources) for each business unit, each central function, etc... The organization hierarchy is appropriate for understanding how work is being done, performance is being managed, the effectiveness of leaders and teams, and all other human capital management concerns. It is also useful in some cost-related analyses such as evaluation and optimization of span-of-control and organization layers. Cost Hierarchy: This is sometimes referred to as “who is paying for whom” and is rarely in perfect alignment with the organization hierarchy. There is a good reason for this, as there are situations when a position in one part of the organization (eg, research & development) is being funded by another (eg, a product or region business unit). In these cases, one leader is paying for the work and the work is being managed by a supervisor within another leader's organization. I have seen "cross-billing" situations going as high as 20% of a given organization. When headcount is shown in a cost hierarchy, it indicates what will hit the general ledger and the financial reporting of the business units. It has a valid and proper place, but it is mostly about accounting, budgeting, and financial planning. Which business unit is right? The truth is that as long as you have all the workforce data accurately captured in the system, everything is right. This sounds trite, but it puts emphasis on the task at hand which is to determine a shared understanding and establish rules for what will be counted and how, which situations will use which variations, and what agreed-upon labeling will be in place for charts/tables shared with others. Some organizations that have a culture of compliance and governance could set this up as part of an HR data governance effort (where headcount and other workforce metrics would be defined, managed, and communicated). Going further, there is a need beyond the Finance and HR/People Analytics leader to socialize whatever is determined as these running rules across the Finance and HR organizations. These teams all need to be aligned. How does One Model help finance and HR collaborate? With a People Analytics solution like One Model in place, the conversations between HR and Finance can be had with much more clarity and speed. This becomes easier because, within One Model all of the workforce data is captured, data quality is managed, and all related dimensions (eg, hierarchies, employee attributes) are available for analysis. Two examples of content that is specifically designed to facilitate the Finance-HR alignment discussions are: Headcount Storyboard. Setting up a storyboard which shows headcount represented in multiple ways: FTEs versus employee counts, variations of which statuses are included/excluded, etc. This information becomes readily comparable with the metric definitions only a click away. Even better, the storyboard can be shared with the Finance and HR partners in the discussion to explore on their own after the session. One Model is the best tool for counting headcount over time. Hierarchy Storyboard. Providing views of the headcount as seen using the supervisor and cost hierarchies side-by-side will help to emphasize that both are simultaneously correct (ie, the grand total is exactly the same). This can also provide an opportunity to investigate some of the situations where the cost and organizational hierarchy are not aligned. In many cases, these situations can be understood. Still, occasionally there are errors from previous reorganizations/transfers which resulted in costing information not being updated for a given employee (or group of employees). With the data in front of the teams, the discussion can move from “Which one is right?” to “Which way should be used when we meet with leaders next time?” When you have One Model, you can bring HR and Finance together faster and more easily ... and that helps you to accelerate your people analytics journey. Need Help Talking to Finance? Let us know you'd like to chat.
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Featured
5 min read
Phil Schrader
Analytics is a funny discipline. On one hand, we deal with idealized models of how the world works. On the other hand, we are constantly tripped up by pesky things like the real world. One of these sneaky hard things is how best to count up people at various points in time, particularly when they are liable to move around. In other words, how do you keep track of people at a given point in time, especially when you have to derive that information from a date range? Within people analytics, you run into this problem all the time. In other areas, it isn’t as big of a deal. Outside of working hours (sometimes maybe during working hours), I run into this when I’m in the middle of a spreadsheet full of NBA players. Let's explore by looking at an easy-to-reference story from 2018. Close your eyes and imagine I’m about to create an amazing calculation when I realize that I haven’t taken player trades into consideration. George Hill, for example, starts the season in Sacramento but ends it in Cleveland. How do you handle that? Extra column? Extra row? What if he had gotten traded again? Two extra columns? Ugh! My spreadsheet is ruined! Fortunately, One Model is set up for this sort of point-in-time metric. Just tell us George Hill’s effective and end dates and the corresponding metrics will be handled automatically. Given the data below, One Model would place him in the Start of Period (SOP) Headcount for Sacramento and End of Period (EOP) Headcount for Cleveland. Along the way, we could tally up the trade events. In this scenario, Sacramento records an outbound trade of Hill and Cleveland tallies an inbound trade. The trade itself would be a cumulative metric. You could ask, “How many inbound trades did Cleveland make in February?” and add them all up. Answer-- they made about a billion of them. Putting it all together, we can say that Hill counts in Cleveland’s headcount at any point in time after Feb 7. (Over that period Cleveland accumulated 4 new players through trades.) So the good news is that this is easy to manage in One Model. Team Effective Date End Date Sacramento 2017-07-10 2018-02-07 Cleveland 2018-02-08 --- The bad news is that you might not be used to looking at data this way. Generally speaking, people are pretty comfortable with cumulative metrics (How many hires did we make in January?). They may even explore how to calculate monthly headcount and are pretty comfortable with the current point in time (How many people are in my organization). However, being able to dip into any particular point in time is new. You might not have run into many point-in-time scenarios before-- or you might have run into versions that you could work around. But, there is no hiding from them in people analytics. Your ability to count employees over time is essential. Unsure how to count people over time? Never fear. We’ve got a video below walking you through some examples. If you think this point in time stuff is pretty cool, then grab a cup of coffee and check out our previous post on the Recruiting Cholesterol graph. There we continue to take a more intense look beyond monthly and yearly headcount, and continue to dive deeper into point-in-time calculations. Also, if you looked at the data above and immediately became concerned about the fact that Hill was traded sometime during the day on the 8th of February and whether his last day in Sacramento should be listed as the 7th or the 8th-- then please refer to the One Model career page. You’ll fit right in with Jamie :) Want to read more? Check out all of our People Analytics resources. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Its newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data.
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0 min read
Lauren Canada
This infographic reveals 4 key HR metrics to strengthen your next data story, so you can prevent costly turnover and retain top talent. Start scrolling to explore the piece!
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6 min read
Dennis Behrman
Artificial intelligence (AI) has become an integral part of various industries, revolutionizing the way organizations make decisions. However, with the rapid advancement of AI technology, concerns about its potential and ethical implications have emerged. As a result, governments around the world are preparing to enact regulations to address the use of AI in people decisions. In this blog post, we will explore the scope of these forthcoming regulations and discuss how People Data Cloud can help ensure equitable, ethical, and legally-compliant practices in automated decision-making across organizations. Broad Scope of Regulations While generative AI, such as ChatGPT, has been the catalyst for these regulations, it is important to note that the scope will not be limited to such technologies alone. Instead, the regulations are expected to encompass a wide range of automated decision technologies, including rule-based systems and rudimentary scoring methods. By extending the regulatory framework to cover diverse AI applications, governments aim to ensure fairness and transparency in all areas of decision-making. Beyond Talent Acquisition Although talent acquisition processes like interview selection and hiring criteria are likely to be subject to regulation, the scope of these regulations will go far beyond recruitment alone. Promotions, raises, relocations, terminations, and numerous other people decisions will also be included. Recognizing the potential impact of AI on employees' careers and well-being, governments seek to create an equitable and just environment across the entire employee lifecycle. Focus on Eliminating Bias and Ensuring Ethical Practices One of the primary objectives of these regulations will be to eliminate bias in AI-driven decision-making. Biases can arise from historical data, flawed algorithms, or inadequate training, leading to discriminatory outcomes. Governments will emphasize the need for organizations to proactively identify and mitigate biases, ensuring that decisions are based on merit and competence rather than factors such as race, gender, or age. Ethical considerations, including privacy and consent, will also be critical aspects of the regulatory landscape. Be Prepared. Join the Regulations and Standards Masterclass today. Learning about AI regulations and standards for HR has never been easier with an enlightening video series from experts across the space sharing the key concepts you need to know. A Holistic Approach to Compliance To comply with forthcoming AI regulations, organizations must evaluate their entire people data ecosystem. This includes assessing where data resides, which technologies are involved in decision-making processes, the level of human review and transparency afforded, and the overall auditability of automated decisions. Achieving compliance will require robust systems that enable organizations to monitor and assess the fairness and transparency of their AI-driven decisions. One AI is Your Automated People Decision Compliance Platform As governments gear up to regulate AI in people decisions, organizations must be prepared to adapt and comply with the evolving legal landscape. The scope of these regulations will extend beyond generative AI and encompass a broad range of automated decision technologies. Moreover, regulations will address not only talent acquisition but also various aspects of employee decision-making. Emphasizing the elimination of bias and ethical practices, governments seek to create fair and equitable workplaces. To ensure compliance with AI regulations, organizations can leverage platforms like One Model's One AI, which is fully embedded into every People Data Cloud product. This platform provides the necessary machine learning and predictive modeling capabilities, acting as a "clean room" to enable compliant and data-informed people decisions. By leveraging such tools, organizations can future-proof themselves against audits and demonstrate their commitment to ethical and unbiased decision-making in the AI era. Request a Personal Demo to See How One AI Keeps Your Enterprise People Decisions Ethical, Transparent, and Legally Compliant Learn more about One AI HR Software
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8 min read
Jamie Strnisha
Given the Great Resignation, recent mass layoffs, and store closures, the past year has shown just how crucial strategic workforce planning can be to overall organisational health and longevity. But it’s not always easy for HR teams to understand where to start when developing workforce planning strategies. So let’s dive into what strategic workforce planning is, how it differs from organisational workforce planning, and how people analytics can transform what your HR team can achieve with your workforce planning strategies. What is strategic workforce planning? Strategic workforce planning in a nutshell is having the right people in the right roles at the right time at the right costs, which can lead to better productivity and lower costs. Strategic workforce planning is one of the most important elements of HR strategy. It helps businesses identify skills gaps, carefully manage resources, benchmark performance against competitors, and ensure proper budget allocation for the organisation. Strategic workforce planning is the process of analysing an organisation's workforce needs, both present and future, and developing strategies to meet those needs. It involves assessing the current workforce, developing scenarios, identifying gaps and future needs, analysing people data to inform decisions, and creating plans to address those gaps and needs. Practising effective workplace planning also involves continuously measuring and monitoring the implementation and effectiveness of those plans against your KPIs and organisational goals. The process typically involves gathering and analysing data about the current workforce, such as employee skills, cost, and demographics, as well as external factors that may impact workforce needs, such as changes in technology, industry trends, and economic conditions. This can include hiring new people, training current employees, and planning for future changes in the workforce. The difference between strategic vs. operational workforce planning Strategic workforce planning and operational workforce planning are two different approaches to managing an organisation's workforce needs. Strategic workforce planning focuses on long-term workforce planning, typically looking at a three- to five-year horizon or beyond. It involves analysing the organisation's strategic goals and objectives and determining the workforce requirements needed to achieve them. This includes identifying the skills, knowledge, and capabilities that will be needed, and creating plans to develop and acquire those resources. Strategic workforce planning is a high-level planning process that is typically undertaken by senior management and HR leaders. Watch a One Model strategic planning session. Operational workforce planning, also known as strategic staffing or headcount planning, is more focused on short-term workforce needs, usually over a six-month to two-year time horizon. It involves short-term management of the workforce and is focused on ensuring that the workforce has the resources it needs to succeed. For example, this may include ensuring that an organisation can staff up to meet seasonal demands (retail at Christmas, farming during the summer, etc.). Operational workforce planning is often carried out by mid-level and front-line managers. How has strategic workforce planning changed over time? Strategic workforce planning has undergone significant changes over time in response to changes in the economy, technology, and social trends. Here are some of the key changes that have occurred: Increased focus on skills: In the past, strategic workforce planning tended to focus on job titles and positions rather than skills-based workforce planning. However, today's strategic workforce planning is more focused on identifying the specific skills and knowledge that are needed for each role. Data-driven approach: Advances in technology have made it easier to collect and analyse workforce data, leading to a more data-driven approach to strategic workforce planning. This allows organisations to make more informed decisions about their workforce needs. Emphasis on flexibility: With the rise of the gig economy and remote work, organisations are increasingly seeking more flexible workforce solutions. This has led to a greater emphasis on strategic workforce planning that can adapt to changing conditions — empowering organisations to optimise which roles should be full-time or contract, or which roles can be hybrid or in-office. Strategic alignment: Strategic workforce planning has evolved to be more closely aligned with organisational strategy, helping organisations ensure that they have the right people with the right skills in the right positions to achieve the organisation’s strategy. Peter Howes does a great job of showcasing how HR workforce planning has changed over time. Watch the recorded webinar. How strategic workplace planning impacts workforce planning Strategic workplace planning can have a significant impact on workforce planning in a number of ways. Here are some examples: Attracting and retaining top talent: A well-designed workplace can be a major factor in attracting and retaining top talent. Promoting collaboration and productivity: By designing a workspace that supports teamwork and communication, organisations can help employees to work together more effectively and reduce workforce productivity issues. Supporting health and well-being: Creating an environment that is focused on keeping employees healthy and reducing stress helps with your long-term planning by ensuring your workforce stays at peak performance. Adapting to changing workforce needs: If an organisation is shifting towards more remote work or hybrid work arrangements, workplace planning can be used to create a workspace that supports those arrangements. What role does people analytics play in strategic workforce planning? People analytics plays an important role in strategic workforce planning by providing data-driven insights into an organisation's workforce. By using data and analytics tools, like One Model, organisations can better understand their current workforce and identify trends and patterns that can inform their workforce planning strategies. People analytics can help organisations to: Identify workforce gaps: By analysing workforce data, organisations can identify areas where they have a shortage of skills or talent, allowing them to focus their strategic workforce planning efforts on addressing those gaps. Forecast future workforce needs: People analytics can be used to project future workforce needs based on factors such as demographic changes, industry trends, and business goals. Optimise workforce efficiency: By analysing workforce data, organisations can identify opportunities to improve workforce efficiency, such as by reallocating resources or adjusting work schedules. Measure the effectiveness of strategic workforce planning: People analytics can be used to track the success of workforce planning strategies over time, allowing organisations to adjust their plans as needed to achieve better outcomes. Increase ROI Businesses can make fast decisions, optimise ROI, and improve customer and stakeholder satisfaction by leveraging data-driven insights into trends and predicting future needs. How One Model supports better strategic workforce planning One Model is a people analytics company that helps organisations transform their workforce data into actionable insights for better decision-making. By leveraging advanced analytics, artificial intelligence, and machine learning, One Model can support better strategic workforce planning capabilities for businesses of all sizes. One of the key strengths of One Model is its ability to integrate data from multiple HR systems, such as HRIS, ATS, LMS, and others, into a single data warehouse. This allows organisations to gain a complete view of their workforce data, eliminating the need to switch between different systems to analyse data. In addition to its advanced analytics capabilities and intuitive interface, One Model also offers a customizable dashboard that allows HR professionals to monitor and track key workforce metrics. With this tool, HR teams can identify areas of concern, measure the success of their workforce planning strategies, and adjust their plans as needed. Overall, One Model supports better strategic workforce planning by providing a single, integrated platform for workforce data analytics, advanced analytics models, and customizable dashboards. This enables organisations to make more informed decisions and, ultimately, achieve their business goals. Discover how One Model’s People Analytics can support your strategic workforce planning.
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5 min read
Joe Grohovsky
"Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat." - Sun Tzu Complex people analytics (PA) projects risk losing sight of what is profoundly important as they endeavor to fulfill all aspirational requirements. Identifying and delivering business insight is their purpose, not simply fulfilling a stakeholder’s tactical wish list of presentation-layer features. However, far too often PA initiatives are launched with requirements dominated by this tactical wish list without a true appreciation for the value of the metrics contained within each report. The funding and focus involved clearly classify these initiatives as Strategic HR projects. Instead of blindly focusing on what presentation tactics will be used, consider first a strategy for building better insights. These strategic conversations should begin with the number (metric/measure) in question. This number is critical and is the cornerstone for all other discussions. This number should be accurate and meaningful. Everything else within a PA initiative is the tactical positioning of that strategic number. Accuracy Without accurate numbers, a reporting effort is wasted. Ask yourself these questions. Is the number derived from trusted, validated source data? Is the source data modeled specifically for your organization? Does your definition of that number align with what will be provided? These questions are more than simplistic check boxes. Consider Headcount, which is the most basic HR measure. Is it based on the Start of the Period, End of the Period, or Average Daily Headcount? Are retroactive changes accommodated? What will happen when introducing additional data sources such as Engagement or Performance? Are you forced to work with templated data and a rigid data model? Interested in learning how to create a stellar People Data Platform? Read our latest whitepaper to understand the steps your team needs to take to create an analytics-ready data platform that will give your team reliable, accurate information that can help propel your people analytics projects toward success. Meaningfulness Not all numbers are equal or valuable. When considering specific metrics, consider these questions. Is this number important on its own, or does it merely provide context? Is it actionable? Considering the above, an easy analogy would be the numbers a physician uses during a patient’s annual physical examination. Those numbers include things like age, height, weight, blood pressure, etc. Age and height are uncontrollable and immune to any action. However, these numbers still provide valuable context for other numbers. Weight and blood pressure would be considered actionable and the focal point for discussion. Once actionable numbers are identified, ask yourself “So What.” Will this insight drive any internal decisions? If not, it is best to focus elsewhere. These questions will determine meaningfulness. Presentation of Numbers After accurate and meaningful numbers are established, a conversation on presentation tactics can occur. Awareness of internal culture and data consumer preferences is critical in this step. Most PA initiatives serve a broad spectrum of data consumers that may involve: HR Business Partners Analysts Center of Excellence Data Scientists Line of Business Managers Self-Service capabilities Senior Executives Each group is best served by providing varying amounts of support, flexibility, and handholding. Common differences for each group would include the decision to provide a summary or detailed data, the amount of context provided, or the amount of supporting documentation required to establish metric validity. Summary It is understandable that PA professionals become fascinated with whiz-bang features contained within presentation capabilities. Ease of data consumption is important, but please realize that it trails behind generating accurate, meaningful numbers. Storytelling your way through People Analytics without substance supporting you is risky. For examples of impactful HR projects, or information on how One Model approaches this topic, please connect with us.
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9 min read
Lauren Canada
Effective workforce and employee listening is one of the most critical skills for HR professionals. To address workforce needs, HR team members must be actively and attentively listening to their employees and workers. Gathering information about the workforce is vital, but that doesn’t mean it’s simple. Listening to your workforce means giving each member respect, time, and attention, so you can truly understand what’s going on and identify the best way to respond. This becomes more difficult in a hybrid workplace and can be complicated at scale for larger organisations. So let’s dive into three ways your HR team can practise effective employee listening at scale. 1. Facilitate more meaningful conversations Speaking to the workforce and using their feedback to support decision-making is how HR really began as a profession. Conversations refer to the 1:1 interactions, observations, and ethnographic tools that HR uses to understand the workforce and what your workforce needs. These are very human tools that can be a powerful method for HR storytelling within an organisation. When conducted effectively, conversations allow HR personnel, managers, and leaders to gain a nuanced understanding of their workforce that technology can’t yet replicate. For instance, it will be a long time before computers can comprehend how grief impacts performance, the unsettling chaos of a reorganisation, or the pride of a promotion. Despite recent advances, empathy, connection, and meaning-making will remain distinctly human domains for some time. On the other hand, bias and human error in conversations is a concern, and there are dangers in relying solely on conversations to inform the HR decision-making process. These are issues that must be thoughtfully planned for and mitigated — so you want to use other employee listening strategies to help validate, verify, and correct for bias in information gathered from conversations. There are three types of conversations that HR teams can use to practise effective employee listening: 1. Formal Conversations These include regular 1:1s, performance reviews, and formal checkpoints that ensure the workforce is heard, managed, and supported. These conversations not only help managers and HR leaders evaluate their employees' performance but also provide an opportunity for the organisation to gather information and better understand the employee experience. 2. Informal Conversations This refers to casual chats that take place around the “watercooler” (in person or remote), where employees can share what's really going on. These conversations can lead to surprising insights about the workplace, culture, and organisation. For instance, employees might discuss work-related challenges, share ideas for improvement, or provide feedback on a topic that you wouldn’t expect. Such conversations can help managers and HR leaders identify potential issues before they become problems. Informal conversations can be a great avenue for HR to gain business context that isn’t captured elsewhere. 3. Ethnographic Research The most formalised version of conversation-based information gathering is ethnographic research — referring to scientific and qualitative research techniques such as observation, participation, and immersion in the workplace to gain cultural and organisational understanding. Ethnographic research can provide a validated and scientifically sound understanding of employee behaviour, well-being, and attitudes, and it can also uncover hidden dynamics and cultural norms that might not come to light through formal or informal conversations alone. By conducting ethnographic research, organisations can gain a deeper understanding of their workforce and tailor their strategies and policies accordingly. 2. Collect information through surveys and forms Engagement surveys and other forms, like performance or training reviews, capture new data that might not be otherwise captured by conversations or other avenues. Surveys are a great method for gathering information from a large number of people quickly. You could spend 30 minutes speaking to every person in your organisation, or you could send a survey that everyone completes on their own time. Surveys can provide a structured, valid, and reliable method to collect information about workforce needs, attitudes, opinions, behaviours, and demographics. Here are three elements you might include in your next HR survey to improve your employee listening strategy: 1. Structured survey questions This includes questions that are answered by a multiple-choice scale like, "How satisfied are you with your current role?" and "Do you feel valued by your employer?". With numeric responses, it’s much easier to parse through and analyse the responses. 2. Open-ended survey questions These questions provide a prompt with a text box for a response. These could include a variety of open-ended topics like “Please tell us about your onboarding experience.” or “Do you have the tools you need to succeed in your role?”. The volume and variety of data that is brought back through open-ended surveys is much higher than structured surveys, so these require further coding or understanding before they can be used in decision-making. 3. Psychometric surveys Psychometric surveys gather information about employees' psychology, attitudes, and sentiments, which can be helpful in understanding variations in trends such as retention and attrition. These questions can be either structured or open-ended, depending on the desired results. 3. Use data from your technology systems As technology is increasingly integrated into workplace operations, your workforce’s interactions with technology can generate a wealth of data about people, processes, and work habits — making your tech stack a powerful employee listening tool. Skilled data engineers, analysts, and data scientists can process this data to extract valuable insights about the workforce. Systems data exists already for nearly every aspect of the work experience today, from hire to termination and from performance management to learning. And it can be collected quickly, passively, and with less bias than conversations or surveys. Plus, when handled correctly, this dataset allows for more sophisticated data techniques and broader perspectives of the organisation as a whole. For an end-to-end approach to employee and workforce listening, which is needed for workforce planning, workforce readiness, or skills gaps analysis, you can use the data within your technology systems. But today’s organisations use so many different technology systems, making it difficult to aggregate this data into an understandable format that can help inform HR decisions. Here are three types of technologies that offer systems data HR teams can use for better employee listening: 1. HR tech This is the traditional tech stack managed by HR tech teams, including systems that handle HR-related processes and programmes (e.g., Core HRIS, ATS, Performance Management, LMS). For example, when a worker is hired, the applicant tracking system (ATS) captures data about their demographics, prior experiences, and the interviewing team's assessment. 2. Collaboration tech Systems capturing collaboration (e.g., Slack, Microsoft Teams, Zoom, Google Docs, etc.) can be powerful tools because they produce information about teams, interactions, and how work gets done within an organisation. Techniques like organisational network analysis can reveal how information flows through an organisation or identify influential individuals. 3. Work tech Work tech refers to technology capturing broad work data outside of HR tech (e.g., procurement systems, code tracking, or attendance). Systems like intranets, timekeeping, expense systems, and ticketing systems fall into this category. These work tech systems also produce data that can be used to recreate, model, and analyse the flow of work in the workplace. By associating these systems with HR tech systems, we can build powerful stories connecting HR data to work outcomes. How One Model supports employee listening at scale One Model is an AI-powered people analytics platform that empowers HR teams to centralise data from multiple technology sources into a single place — for easier analysis and better HR decision making. By bringing all HR data into One Model, HR leaders can get deeper insights into their workforce and perform more effective employee listening at scale. This allows you to listen to your entire workforce from every possible angle, so you can uncover workforce needs, increase engagement, reduce burnout, and address issues in a timely manner. Plus, you can refocus valuable HR time from building dashboards and aggregating data to analysing reports and improving the organisation. Discover how Colgate uses One Model Colgate shares how they used One Model to improve their employee listening strategy, understand their current workforce, and adjust their HR approach to meet their DEIB goals. Or fill out the form to sign up for a One Model demo today! Request a Demo Today!
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24 min read
Richard Rosenow
Listening at Scale Effective listening is arguably the most critical skill for HR professionals. To address workforce needs, HR team members must be proficient in active and attentive listening. Gathering information about the workforce is as vital to an HR team as air and water. To listen to a member of the workforce is to give them respect, time, and attention, and to hear what is going on. It’s the oldest way we learn. We’ve seen listening programs grow from those roots into programs that survey the full company and beyond. I’ve often referred to people analytics as "decision support for HR," but another phrase I’ve used is that people analytics can be described as "listening at scale." To date, that’s been treated as more a metaphor for how we work with data rather than adopting systems data and people analytics into the listening ecosystem. However, I believe we can take this further and fully integrate the analysis of workforce systems data into an integrated framework for information gathering and listening. Understanding People: A Three-Channel Framework Margaret Mead, an anthropologist, best captured the complexity of working with humans with her quote: “What people say, what people do, and what people say they do are entirely different things." While humorous, I believe this quote can also act as the foundation to inspire an integrated framework for workforce listening. Mead's quote effectively outlines three “information channels” for gathering information about the workforce: conversations, surveys, and systems. I’ve rearranged them slightly for the purposes of this blog: “What people say” = Conversations: People having conversations in the workplace “What people say they do” = Surveys: Respondents assessing themselves and their ideas through surveys “What people do” = Systems: What people actually do in the workplace which can be tracked within HRIS or collaboration technologies (HR Tech / Work Tech / Collaboration tools) And I am very careful to say information channels above. As I’ll detail in this article, conversations, surveys, and systems are where workforce information is generated. Data and insights then flow from those channels to central storytellers and decision-makers. This is an end-to-end view of the HR decision-making process. This is an alternative way to view data to how we usually discuss data in HR. We often hear data described by its topic (e.g. Recruiting data, L&D data, or Comp data), source system (e.g. Workday data, Greenhouse data) or its application (e.g. descriptive, predictive, prescriptive data). This channel view seeks to depict the supply chain of information. Let’s delve deeper into this framework to create a more comprehensive understanding of the workforce. I believe this holistic approach to listening will allow HR professionals to make better-informed workforce decisions that positively impact the organization. Conversations Speaking to the workforce and making use of that information to support decision-making is how we got our start as an HR profession. Conversations refer to the 1:1 interactions, observations, and ethnographic tools that HR employs to understand the workforce. These are very human tools and these tools can be a powerful method for sense-making and storytelling within an organization. When conducted effectively, conversations allow HR personnel, managers, and leaders to gain a nuanced understanding of their workforce that technology struggles to replicate. For instance, it will be a long time before computers can comprehend how grief impacts performance, the unsettling chaos of a reorganization, or the pride of a promotion. Despite recent advances, empathy, connection, and meaning-making will remain distinctly human domains for some time. In the move towards data-driven decision-making, I believe we have underestimated the impact that these conversations can have on decision-making. The anthropological sensemaking that occurs when an experienced HRBP listens to the workforce is unmatched when it comes to quickly understanding cultural dynamics and understanding the core of workforce issues. Bias and human error in this channel of conversations is a well-documented concern and there are dangers in relying solely on conversations to inform the HR decision-making process. These are issues that must be thoughtfully planned for and mitigated, both in how this method is employed, but also the use of other channels to validate, verify, and correct for bias in information gathered from this channel. However, that does not mean that those other channels will replace conversations and conversation still has an important place in decision-making. I see three breakouts within the information channel of conversation: Formal Conversations: These include regular 1:1s, performance reviews, and formal checkpoints that ensure the workforce is heard, managed, and supported. These conversations not only help managers and HR leaders evaluate their employees' performance but also provide an opportunity for information gathering for the organization and for understanding the employee experience. Informal Conversations: This refers to the casual conversations that take place around the “watercooler” (in person or remote), where employees can share what's really going on. These conversations can lead to surprising insights about the workplace, culture, and organization. For instance, employees might discuss work-related challenges, share ideas for improvement, or provide feedback on a topic that you wouldn’t expect. Such conversations can help managers and HR leaders identify potential issues before they become problems, and can be a channel for business context that is not otherwise captured. Ethnographic research: The most formalized version of conversation-based information gathering would be ethnographic research. This refers to the scientific and qualitative research techniques such as observation, participation, and immersion in the workplace to gain cultural and organizational understanding. Ethnographic research can provide a validated and scientifically sound understanding of employee behavior and attitudes, and can also uncover hidden dynamics and cultural norms that might not be apparent through formal or informal conversations alone. By conducting ethnographic research, organizations can gain a deeper understanding of their workforce and tailor their strategies and policies accordingly. Want to see how One Model turns conversation data into analytics? Survey This channel refers to the toolkit around the scaled collection of novel data. I use the word Survey as surveys are a great example, but this channel represents whenever a form is completed to capture novel data that is otherwise not captured by a system passively. This includes engagement surveys and other forms such as filling out performance reviews or feedback forms after trainings. Surveys are a method to gather information from a large amount of people quickly. I could spend 30 minutes speaking to 80 people (a full back-to-back week for me and a 30-minute disruption for every person I speak to) or I could design and send a survey that everyone completes on their own time. Surveys can provide a structured, valid, and reliable method to collect information about workforce attitudes, opinions, behaviors, and demographics. Some breakouts for the survey: Structured survey questions: Questions about the environment, factors in the workplace, and information that the creator wishes to assess. Ideally structured and evidence-based. Questions could include items like "How satisfied are you with your current role?" and "Do you feel valued by your employer?" followed by a distinct multiple-choice scale. Open-ended survey questions: Open-ended survey questions provide a prompt with a text box for a respondent to complete. These questions could include a variety of open-ended topics like “please tell us about your onboarding” or “Are there tools you need to perform your role that you cannot acquire?”. The volume and variety of data that is brought back through open-ended surveys is much higher than structured surveys and these require further coding or understanding before they can be used in decision-making. Psychometric surveys: Psychometric survey questions could be either structured or open-ended, so this is a bit of a false breakout, but it is important to call out as it is a unique type of information gathered about the psychology of the employee in the workplace. Psychometric surveys gather information about employees' attitudes and sentiments which can be helpful in understanding variations in trends such as attrition. Systems The third internal information channel in this framework is systems. As technology is increasingly integrated into workplace operations, the workforce’s interactions with technology generate a wealth of data about people, processes, and work habits. Skilled data engineers, analysts, and data scientists can process this data to extract valuable insights about the workforce. The key advantage of systems data is its readiness for use, as well as the growing volume and speed of its generation. Systems data exists already for nearly every aspect of the work experience today, from hire to termination and from performance management to learning. This broad dataset, when properly extracted and prepared, enables more sophisticated data techniques to be brought to bear faster compared to the other channels. Systems data can also offer a broader perspective of the organization as a whole. Conversation and surveys gather information from each employee from their personal viewpoint, but their perspective may not be broad enough to see organization-wide issues. The view of what is going on end-to-end, which is needed for workforce planning, workforce readiness, or skills gaps analysis, can be generated from this systems channel. Systems data is also valuable because it is largely a passive data source, produced as a byproduct of work conducted through technology. Consequently, it is less subject to biases and limitations of human perception, memory, or interpretation. However, systems data often lack the nuanced information density of business context provided by conversation and survey methods. Additionally, the bias it does have is often embedded in the software design choices which can often be harder to detect and understand. Choices made by programmers regarding UX, data capture, native reports, and interactions available can introduce potential areas for bias in the extracted information. Systems data can be further categorized into three main breakouts: HR tech: This is the traditional tech stack managed by HR tech teams. Systems handling HR-related processes and programs (e.g., Core HRIS, ATS, Performance Management, LMS). For example, when a worker is hired, the applicant tracking system (ATS) captures data about their demographics, prior experiences, and the interviewing team's assessment. Collaboration Tech: Systems capturing collaboration (e.g., Slack, Microsoft Teams). These tools (Slack, Teams, Zoom, Google Docs, etc.) produce information about teams, interactions, and how work gets done within an organization. Techniques like organizational network analysis can reveal how information flows through an organization or identify influential individuals. Work Tech: Technology capturing broad work data outside of HR tech (e.g., procurement systems, code tracking, or attendance). Systems like intranets, timekeeping, expense systems, and ticketing systems. These work tech systems also produce data that can be used to recreate, model, and analyze the flow or work in the workplace. By associating these systems with HR tech systems, we can build powerful stories connecting HR data to work outcomes. Tradeoffs in Information Channels Selecting the right channels for a given decision is vital for success. To do so I see the need to weigh the tradeoffs in trust, effort, and information density. Trust Trust is a key factor in how we interpret information that comes from the various channels. For instance, information from conversations can be difficult to trust, particularly when not everyone involved is present or when they are not recorded, transcribed, or made public. If I talk to my manager about a coworker, my manager will need to verify their side of the story. Even when conversations are recorded, they can still be misleading. Surveys are generally more trusted than conversations due to the structured way that they are delivered. Surveys can have academic ties on their design and are typically more consistent, reliable, and objective than conversations. That said, it can still be difficult to know what someone was thinking when they read a question on their own. Employees may also have an incentive to game a survey or mislead the survey, which can lead to reduced trust. Lastly, systems data is considered to be the most trusted type of data because it is generated as a byproduct and (optimally) unchanged from when it was generated. Unlike conversations or surveys, systems data doesn't rely as much on interpretation as there is not as much subjective context. Instead, the systems channel provides information simply on the actions that have occurred. As a result, systems data is often seen as a more trusted source of information. Effort The effort required to create information from each channel is another consideration. Conversation data is rarely converted into what we think of as data that we could interact with in a spreadsheet (tabular) but is usually synthesized and interpreted by each person who had the conversations. Making sense of many conversations and even having to have many conversations makes this channel high effort to scale. Survey responses are much easier to information due to consistency and the planning involved in creating a survey tool. The data that comes back from closed-ended surveys and many psychometric surveys can be quickly analyzed in tabular formats. For open-ended surveys many of the concerns of conversation come back in, but on a more contained scale. As stated above, the systems channel produces data that is relatively ready to use. While getting data extracted and modeled can require some upfront effort, the effort is more contained and much lower than trying to generate information from language in the prior channels. This low effort to analyze is in part what has made systems data so popular with People Analytics teams. Information Density Density refers to the richness of information each channel provides. Each channel has a certain density of core information, but some channels layer on personal and business nuance, context, and depth. This factor is where conversation shines and where I believe we've underestimated the information channel. Conversations between people are incredibly dense with information passage for core content, but then also including additional streams of information on the pitch of voice, body language, and facial expressions. Open-ended surveys try to address the content nuance but still lag conversation on those other human nuances. The systems channel falls far behind then on this factor as systems data is limited to capturing only predefined data points and largely passive data points. Combining the Tradeoffs One way to mitigate the strengths and weaknesses of these channels is to pull them into a narrative together. For example, systems data can provide a high-level overview of the situation and help frame the story, survey data be used to capture precise additional information needed for a study, and then follow-up, conversations can provide a much deeper understanding of the context of the problems at hand. While combining HRIS output with surveys and conversations can be challenging, translating all three into workforce information is what allows us to pull them both into a coherent narrative. For example, the information generated from the systems channel, “what they do”, may tell a story that there is a high turnover rate among a specific demographic within an organization. Relying solely on systems information, we may jump to the conclusion that this demographic is not a good fit for the company. However, by also listening to "what people say they do" through engagement surveys, we may discover that this demographic is leaving because of a lack of training or career advancement opportunities. Furthermore, if we listen to "what people say" in follow-up conversations between HRBPs and employees, we may identify that there is a particular manager that is not allowing teams to attend trainings. All three channels together create a comprehensive story. There are instances when each channel should also be used independently. Employee relations professionals' investigations may depend solely on conversations, bypassing surveys or systems. Surveys can offer feedback on large-scale events not covered by systems and where conversations are not feasible. Systems data may be all that is needed for a first-pass analysis or exploratory pass at understanding the organization. Example A software development firm leverages information from the systems channel to identify patterns of late-night work activity among its employees. By approaching the data with empathy and understanding, they initiate conversations with affected employees and discover that tight deadlines and unrealistic expectations are causing stress and burnout. As a result, the firm adjusts its project management approach to prioritize employee well-being and work-life balance. They perform a quarterly survey on these topics going forward which finds that the changes they implemented have led to a healthier and more sustainable work environment. Information Channel Framework for Decision Making Let’s introduce a more complex diagram now to house this framework. In the following graphic, I’ve laid out the supply chain from each information channel and how it is converted to information. That information can then be combined in a common form and once it is synthesized and analyzed it becomes stories which inform decisions. Also included in the graphic are the talent strategy for a business (how they want to create strategic advantages with talent) and the experience of the decision maker, which also inform stories. Those two areas have unique influence and in turn are influenced by decisions made by a decision maker. As a final note, this article was focused on internal channels of information, but some additional external channels of information could be external labor market data (information about the context that a workforce sits within) or evidence-based practices (academically validated information). This flow from information generation to how we inform decisions with stories should be top of mind for any team working in employee listening, people analytics, or HR. We ground ourselves when we are reminded that our goal is to support the HR decision-making process to drive business results. Now that we have created a framework and explored the value of combining conversations, surveys, and systems data for a comprehensive understanding of the workforce, let's focus specifically on what it means to bring the systems channel into the conversation on listening. Historically, conversations and surveys have been treated as listening tools, but if the systems channel also generates information about the workforce, we can make the case that we need to listen to employee information through all three channels. Systems data as Listening Here are five ways benefits that an HR team can achieve by blending systems data into the conversation on listening along with a fictional story detailing how this could work in practice: Engaging HRBPs: By embracing systems data as a form of listening, we can make analytics more accessible to HR professionals who may be more comfortable with traditional listening methods. HRBPs are good at listening and this is another way to do what they are good at. By viewing systems as another way to listen, we can reduce initial fears and skepticism that someone feels when they hear “HR Analytics” or “People Analytics” which will help bring HR into the fold, tapping into their strengths. Example - Engaging HRBPs: An HR business partner at a retail organization believes that a new schedule that has been set for employees could be causing work-life balance issues. They have had conversations with a few employees which prompted the investigation and after sending out a work-life balance survey they confirmed the issue. However, leadership was still not convinced, so the HR business partner listened to the data from the time-management system to analyze patterns of absenteeism and tardiness among employees before and after the shifts were changed and they found significant increases in each, which they brought into their story. The HRBP took this story which was informed by conversations, survey, and systems data to the leadership team and it convinced them to make a change to the shift schedule, resulting in improved attendance and employee satisfaction. Integrated storytelling: This framework creates a more integrated approach to analytics, where we can combine the insights gained from systems data with other channels of information to create a more complete picture of the situation at hand. This integrated approach to methods will lead to better workforce decisions as more information can be brought to bear. Example - integrated storytelling: A healthcare organization seeks to improve diversity and inclusion within its workforce. By combining data from employee demographic systems, engagement surveys, and focus group conversations, they created a comprehensive narrative that revealed disparities in career development opportunities for underrepresented groups. As a result, the organization implemented targeted mentorship programs and inclusive leadership training, which fostered a more diverse and inclusive workplace. Strengthens employee trust: Organizations can demonstrate that they value their employees by actively listening to them through various channels, including systems data. By framing systems data as a form of listening and bringing empathy to bear on that, teams can communicate to employees why they are performing analysis and reduce mistrust related to the analysis of systems data. Example: A financial services firm transparently communicates their use of systems data to track employee work patterns in order to optimize team productivity. By sharing this information with employees, explaining how data is protected, and explaining how the data would be used to inform the HR decision-making process, employees felt more involved in the process and trust the company's intentions, which leads to increased engagement and commitment. Reduce debate: Recognizing that all three information channels — conversations, surveys, and systems—are necessary to tell complete human stories fosters a collaborative environment between different teams and functions. This encourages analytics teams, listening teams, and HR business partners to work together to create a comprehensive narrative, rather than focusing on just one aspect of data collection. Example: In a manufacturing company, there is disagreement between HR and operations teams about the most effective way to allocate resources for employee training. By incorporating data from all three channels—systems data on employee performance, survey feedback on training preferences, and conversations with both employees and managers—they are able to reach a consensus that ultimately leads to more efficient training and improved workforce capabilities. Human-centered analytics: Framing systems data as listening to the workforce emphasizes empathy and understanding. We should always remember that behind every data point in HR is a human who has a livelihood, friends, family, and a world outside of work. Approaching systems data as listening to employees reminds analytics teams to respect the human behind the data and ensures that the focus remains on the human aspect, rather than treating employees as data points, which ultimately leads to better workforce decisions. Call to Action As a reminder for all three systems, transparency is key. Employees deserve to know what information is being gathered, how it is used, and who can see or share information that they have provided or that has been collected about them. Proper data privacy controls, data governance, and agreements between company and employee must be established. Without empathy and these protections, all information channels will break down. As HR leaders and People Analytics professionals, we must recognize the value of each channel in capturing the complexity and richness of the workforce's experiences, needs, and perspectives. This framework that takes us from information to decisions also shifts us out of our methodology-based functions (e.g. HRBP holding conversations, Employee Listening doing surveys, and People Analytics working with) and reminds us that our common end goal is informing workforce decisions to drive business results. Upon reading the paper I've gotten the question from a few reviewers around “does this mean the name People Analytics needs to change”. I can see where they're coming from that analytics inspires the stats and management of systems data, but when we look at the core of the word “analytics” it is the science of analysis. I think we are still safe. If we were to go somewhere else someday? I could see us landing on Workforce Decision Support, naming the function on our outcome rather than method, but I don't think that's worth losing the brand we've built under People Analytics today. As we move forward in an increasingly data-driven world, it is crucial that we remain grounded in empathy and the human aspect of decision-making. Understanding and supporting the individuals that make up our organizations is core to who we are in HR. By actively seeking input from the workforce through all available data channels and embracing a comprehensive listening approach, we will be better equipped to drive meaningful change, foster employee trust, and ensure the long-term success of our organizations. Margaret Mead hit a point of truth when she said, “What people say, what people do, and what people say they do are entirely different things.", but we’ll end on another quote from Mead which I’ll pass on to you as you think about the work required to get these three channels speaking together instead of apart at your organization: “Never doubt that a small group of thoughtful, committed, citizens can change the world. Indeed, it is the only thing that ever has.”― Margaret Mead Many thanks to Mike Merritt, Kyle Davidson, Keith Kellersohn, Peter Ward, Beverly Tarulli, Ethan Burris, Shahfar Shaari, Allen Kamin, Anna Tavis, Al Adamsen, Lyndon Llanes and many others for wonderful conversations on this topic and your feedback! I am grateful and reminded daily of what an incredible community we have in the people analytics world. Interested in talking to my team to learn more? Fill out the form below.
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9 min read
Richard Rosenow
It’s very difficult to do people analytics without data. Finding and extracting workforce data to use for analytics is maybe the first and most common challenge that people analytics teams encounter. In this blog post, I’ll share tips I’ve learned about data extraction for HR teams, common challenges involved in extracting data, and best practices for overcoming these challenges. By applying these tips, HR teams can more effectively and efficiently extract data to drive business value and insights. What is Data Extraction? Data extraction is the process of extracting data from one or more sources and transforming it into a usable format for further analysis or processing. It is the "E" in "ETL". In the context of HR, data extraction is an essential process for collecting and organizing data related to the workforce, such as core HRIS, employee demographics, performance data, and engagement data. By extracting this data, HR teams can more effectively analyze and utilize it to make informed decisions and drive business value. Data extraction may involve extracting data from various sources, such as databases, spreadsheets, and HR systems. This is the first in a series we're writing on the people data platform. If you'd like to learn more, Download the whitepaper. 5 Tips to Ensure HR Data Extraction Success 1. Prioritize and Align Extracted Data with the Needs of the Business First and foremost, it is important for people analytics teams to prioritize what data they go after based on the needs and challenges of the business. If the business is experiencing high attrition, start with the HRIS data and build an analysis on termination trends. However, if the business is concerned about understanding remote work, the starting point for data extraction may need to be the survey system to get insights on employee voice back to leadership teams. Delivering against critical business needs adds value to the company, builds trust, and creates the buy-in needed for future projects. There’s a time and a place to pursue novel data to generate insights that the business is not expecting, but without a foundation of trust and a history of delivering against core business concerns that can be a difficult road. When you’re building your data extraction roadmap, start with the data where you can get to value quickly. 2. Be Thoughtful About What You Extract Workforce data is inherently different from other data in the company as underneath each data point is a coworker with a livelihood, career, friends and family, and personal details. It is critical that People Analytics teams be careful about what they extract and that they are thoughtful about use cases for the data. It’s an important ethical decision to make sure the data is private, secured, and safe in storage as well as in the extraction tools and pipelines that get the data into storage. There are ethical approaches you should be thinking about, but we also live in an environment now where there are hard legal requirements related to the extraction and storage of workforce data. Depending on the nature of the data and where you operate, you may be required to comply with CPRA (California), SOX, HIPAA, and GDPR to name a few. Of note, GDPR applies to EU citizens wherever they reside and not just individuals residing in the EU. So if you employ any EU citizens or are considering hiring EU citizens, GDPR regulations are critical when it comes to data extraction. 3. Build the Business Case to Pull More It can be difficult to convince IT teams or central data engineering functions to support HR data extraction. So when you do get someone to assist, there can be a certain anxiety around the idea of “what if I need more”. This can cause a team to over-extract data or pull too much of it too soon. The feeling is understandable. I’ve been there. But as I’ve said before, the people analytics flywheel is a phenomenon that can be realised if you focus on prioritized business problems. This gives you the chance to revisit the data extraction conversation down the road should you need more. Your future arguments for data extraction will be stronger if business needs continue to be the rationale for additional requests for data extraction support. 4. Automate Your Extractions A native report is a report that comes pre-packaged with your HR system. While native reports are helpful to early data extraction wins, they can be difficult to scale and standardise. Native reports tend to have the following effects. They are usually just a subset of the data within the system that are typically pulled through a graphic user interface, which makes them rigid and difficult to repeat. They are prone to time out if you pull too much data or pull too frequently. They may end up looking different depending on which user pulled them due to filters, permission settings, and the effective date range for the data pulled. (HR never closes the books!) Over time, you’ll need to move away from native reports and to an API or another method to extract the data from the system. An API gets you access to the full data set, pulls data more frequently, and introduces standardisation and repeatability by leveraging data extraction tools and relying less on GUIs. APIs never get bored, can be logged and audited, and can run on their own. Automation changes repetitive and high-variance tasks into trusted processes. 5. Extract for Data Science, not just Reporting See the video above to learn more about extracting data from Workday. Meaningful analysis requires more data and often different data than snapshot extraction methods like native reports can provide. Snapshot extraction can handle basics, such as headcount reporting but cannot report what the company looked like on a given day. When you extract your HR data, make sure that you extract what you need for data science and not just your reporting needs. Data science applications require wider data sets and more features. The time component is the most important part of HR data science. An employee might touch 10 different HR systems as he or she joins a company, so the data in each system needs to be joined to the same employee record in a harmonized and sequential order. Make sure that the data in each system is captured at the time of the action with the time stamp. Naturally this creates a “transaction-level” record. Without those transaction records, you can end up with messy data. Examples include data that shows someone being promoted before they were hired or terminated before a transfer. HR is also notorious for back-dating work. Transaction-level records can prevent issues arising from those behaviors. Finally, your data science necessitates extracting the correct components. Prioritize Data Extraction, But Be Aware of the Nuances Are you ready to explore how to extract hr data at your company? Data extraction is an essential part of conducting people analytics. It is important for people analytics teams to prioritize their data extractions based on the needs and challenges of the business, be thoughtful about which data points are extracted, consider automating their data extractions, and be careful about the nuances of the data they extract. Looking to Extract Data Out of Your Specific HRIS? Download our Resources Now! Delivering People Analytics out of Workday Delivering People Analytics from Successfactors
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6 min read
Marcus Joseph
In the US, leave is having a moment. From the US President’s State of the Union to New York’s 12 weeks of fully paid parental leave, to the FAMILY Act legislation, leave has been all over the feeds, which is encouraging given the majority of US workers struggle to take advantage of our current policy’s benefits. While most of the coverage seems to focus on longer-term family leave, in today's working environment, paid sick leave is more important than ever. In fact, US workers without paid sick leave could be three to four times more likely to quit their job than comparable workers who have this benefit. This is especially true for hourly versus salaried employees and for female employees who tend to disproportionately carry caregiving responsibilities outside of paid work. For most industrialized countries, sick worker pay is not a critical issue. In fact, 32 of 34 OECD countries guarantee paid leave for personal illness. Who are the two OECD countries holding out? The United States and North Korea. So, let’s dive into the American problem and what it can mean for businesses managing workers in the US. With a “tripledemic” threat of flu, COVID-19, and Respiratory Syncytial Virus (RSV), it’s evident that company sick pay is a critical benefit for companies of all sizes. Even some US government studies concluded that there was a noticeable rise in workers who quit with unpaid leave during 2020. FFCRA Leave and Changing Paid Sick Leave Law Amid COVID-19 The COVID-19 pandemic revealed that paid leave is essential to employee well-being and safety. In the past, paid leave was not considered critical to supporting the American economy. As COVID-19 cases ramped up, allowing workers to stay home or care for their sick family members helped meet real human needs, combat the spread of COVID-19 and mitigate the impact on the American economy. The Families First Coronavirus Response Act (FFCRA) was eventually implemented, which required certain employers to provide FFCRA leave and expanded family and medical leave for specified reasons related to COVID-19. About 25% of US firms did increase their sick leave options and one study found that states, where workers gained increased leave benefits under FFCRA, reported an average of 400 fewer cases of COVID-19 per day. However, 90% of companies reported these increases were intended to be temporary. Since Covid, there also seems to be a renewed interest from the Biden administration to make paid leave a requirement. During the State of the Union 2023, he backed up his claim to stop workers from being stiffed by fighting for paid family and medical leave. His secretary of labor is also calling for better national standards to mark the 30th anniversary of the Family and Medical Leave Act. Need to track Covid illnesses at your organisation? Try our free resource. Rising Turnover Reveals Paid Sick Leave Is Critical to Employee Retention One key reason why our people analytics teams should consider paid sick leave in our turnover models is the impact on retention. Certain populations of workers are much more likely to quit over paid leave. This means that employers who don't offer this benefit are at a disadvantage when it comes to retaining critical team members. The rise in turnover rates is already a nationwide problem. Plus, replacement costs for an employee can be as high as 50% to 60%, with overall costs from 90% to 200%. Offering paid sick leave is not only critical benefit employees look for in a business, but it is also a great way to live out your values of caring about individual well-being and your desire for employees to stay with your company for the long haul. Increase Employee Productivity and Engagement With PTO It’s pretty clear that when employees are out sick, they are not able to work and be productive. However, offering employees the ability to take the time they need to recover without worrying about losing pay will also positively impact productivity levels when those sick team members are back in action and healthy. When employees feel like their employer cares about them and their well-being, they are more likely to be engaged while at work. This leads to improved morale and a better work environment for everyone involved—improving life outcomes for individuals, the bottom line as an organization and your brand as an employer of choice. Ultimately, your standard sick leave policy is a factor your HR analytics team should consider when analyzing retention rates. Understanding how much your average PTO and sick leave is affecting your workforce this cold, flu and COVID season may be the difference between keeping and losing employees and remaining competitive in your market. HR teams should invest in knowing the internal and external story the data tells us and sharing it with leadership. Doing so could help improve employee retention rates, reduce turnover-related costs, and increase productivity in the long run–and help turn leave’s current “moment” into our new norm.
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7 min read
Chris Butler
The employee survey still is perhaps the most ubiquitous tool in use for HR to give their employees a voice. It may be changing and being disrupted (debatable) by regular or real-time continuous listening and other feedback mechanisms. Regardless, employee survey data collection will continue. I am, however, constantly amazed by the amount of power that is overlooked in these surveys. We’re gathering some incredibly powerful and telling data. Yet, we barely use a portion of the informational wealth it holds. Why? Most organizations don’t know how to leverage the confidential employee survey results correctly and maintain the privacy provisions they agreed with your employees during data collection. The Iceberg: The Employee Survey Analytics You're Missing Specifically, you are missing out on connecting employee survey answers to post-survey behaviours. Did the people who said they were going to leave actually leave? Did the people who answered they lack opportunity for training, actually take a training course when offered? Did a person who saw a lack of advancement opportunities leave the company for a promotion? How do employee rewards affect subsequent engagement scores? And of course, there are hundreds of examples that could be thrown out there, it is almost a limitless source of questioning, you don’t get this level of analysis ROI from any other data source. Anonymous vs. Confidential Surveys First, let me bring anyone who isn’t familiar with the difference up to speed. An anonymous survey is one where all data is collected without any identifiers at all on the data. It is impossible to link back to a person. There’s very little you can do with this data apart from what is collected at the time of questioning. A confidential survey, on the other hand, is collected with an employee identifier associated with the results. This doesn’t mean that the survey is open, usually, the results are not directly available to anyone from the business which provides effective anonymity. The survey vendor that collected these results though does have these identifiers and in your contract with them, they have agreed to the privacy provisions requested and communicated to your employees. And a number of survey vendors will be able to take additional data from you, load it into their systems and be able to show a greater level of analysis than you typically get from a straight survey. This is better than nothing but still far short of amazing. Most companies, however, are not aware that survey vendors are generally happy (accepting at least) to transfer this employee-identified data to a third party as long as all confidentiality and privacy restrictions that they, the customer, and the employees agreed to when the survey was collected. A three-way data transfer agreement can be signed where, in the case of One Model, we agree to secure access to the data and maintain confidentiality from the customer organization. Usually, this confidentiality provision means we need to: Restrict the data source from direct access. In our case, it resides in a separate database schema that is inaccessible by even a customer that has direct access to our data warehouse. Provide ‘Restricted’ metrics that provide an aggregate-only view of the data, i.e. only show data where there are more than 5 responses or more than 5 employees in a data set. The definition of how this is restricted needs to be flexible to account for different types of surveys. Manage Restricted metrics as a vendor, preventing them from being created or edited by the company when a restricted data set is in use. Support employee survey dimensionality that adheres to this restriction so you can’t inadvertently expose data by slicing a non-restricted metric by a survey dimension and several other dimensions to create a cut to a population that otherwise may be identifiable. Get Ready to Level Up Employee Survey Analysis! Your employee survey analytics can begin once your survey data is connected to every other data point you hold about your employees. For many of our customers that means dozens of people data sources across the recruit to retire, and business data spectrums. Want to know what the people who left the organization said in their last survey? Three clicks and a few seconds later and you have the results. Want to know if the people you are recruiting are fitting in culturally and which source of hire they were recruited from Or if low tenure terminations show any particular trends in engagement, or culture responses? Or whether people who were previously highly engaged and have a subsequent drop in engagement have a lack of (choose your own adventure) advancement|compensation|training|skilled-peers|respect for management? Literally, you could build these questions and analysis points for days. This is what I mean, a whole new world opens up with a simple connection of a data set that almost every company has. What can I do? Go and check your last employee survey results and any vendor/employee agreements for how the data was to be collected and used. If the vendor doesn’t state how it’s being collected, check with them, often they are collecting an employee identifier (id, email, etc). If you are lucky you might have enough leeway to designate a person or two within your company to be able to run analysis directly. Otherwise, enquire about a data transfer agreement with a third party who will maintain confidentiality. I’ve had this conversation many times (you may need to push a little). If you don’t have data collected with an identifier, check with HR leadership on the purpose of the survey, and the privacy you want to provide employees with and plan any changes for integration into the next survey. This is a massively impactful data set for your people analytics, and for the most part, it’s being wasted. However, always remember to respect the privacy promise you made to employees, communicate how the data is being used and how their responses are protected from being identified. With the appropriate controls, as outlined above, you can confidentially link survey results to actual employee outcomes and take more informed action on the feedback you collected in the employee survey analysis. If you would like to take a look at how we secure and make survey data available for analysis, feel free to book a demonstration directly below. Ready to see us Merge Employee Survey Data with HRIS Data? Request a Demo!
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6 min read
Jamie Strnisha
In today's competitive business landscape, it's more important than ever for workplaces to provide value to employees, customers, and investors. Attracting top talent, boosting productivity, enabling innovation, and improving employee experience are all key goals to achieving higher value. Many progressive companies are accomplishing these goals by tracking facility analytics — including attendance tracking and workplace tracking — to make facility improvements. That’s right. Companies use facility analytics to improve their environments and retain top talent and you should too. What is Facility Analytics Facility analytics is the process of collecting data about how a workplace is being used daily. Space utilization data is one type of data that can be collected (through sensors, badge swipe, surveys, or observation studies) and integrated into your overall people analytics data lake. With this workplace analytics data, managers can use it to transition to make proactive, positive changes in the company’s culture and work environment. This can even include transitioning away from assigned workspaces to flexible shared spaces and remote work. Since the pandemic, many companies have made changes, like these, and were able to reduce property costs and optimize public use spaces. After analyzing which departments are using which spaces, changes can even be made to bring teams closer together to improve mobility, increase employee connections and boost productivity. Why Use Facility Analytics and Workspace-Related Data? There may be some that are afraid facility analytics may be too intrusive but, done correctly, it could actually be a critical tool in improving the overall workplace experience. A few examples of what time and attendance people analytics can help track include: The best days for employee gatherings (monitor the days where most employees are in the office). Collaboration among employees by using survey data to track communication and teamwork trends. Employee burnout and workload to determine what generative attributes are leading to turnover. Office movement to know where employees’ desks are versus where they actually worked. Hoteling policy and proximity analysis so managers can see who is using the hotel desks and if additional desks are needed. Energy-saving initiatives, such as changing the temperature or adding motion-sensor lights in unused workspaces. Office activity and meal planning. Contagious illnesses tracking (e.g., COVID-19) to identify and mitigate risks in the organization by knowing who is in the office on any given day. Facility data in people analytics can also be used to track employee productivity. If you find that teams are travelling long distances to meetings, you can move teams and/or encourage online meetings to reduce travel time and increase work availability. In addition, if you see that some people are consistently at the office for long periods of time, you may be able to intervene to prevent burnout. How to Use Attendance Tracking to Future-Proof Your Facility Offices provide social interaction, creativity options, and collaboration. Your goal should be to design a work environment that meets those needs and more. COVID-19 has changed the workplace as we know it. The pandemic gave many people a taste of remote work they never had. With so many employees working remotely, companies are starting to realize that the traditional 9-5 in an office setting may not be necessary. In fact, Forrester Research found that 60% of companies are now utilizing hybrid schedules where employees can work from home and in the office. This "next normal" workplace will require a new way of thinking when it comes to managing employees and facilities. Luckily, when you merge survey data, facility data, and your HRIS, you can start to understand how best to meet business objectives and employee needs by team, cohort, or distance from the office. Ways to Capture Workplace Analytics Wi-Fi Sensors Common Workplace Analytics can be tied to People Analytics and provide a more in-depth understanding of your people. Wi-Fi sensors can be used to track employee movement and usage of common areas. This data can then be used to make adjustments to the layout of the office, as well as cleaning and sanitizing schedules. Additionally, Wi-Fi sensors can be used to send alerts to employees when they enter an area that has been recently cleaned or sanitized. Mobile Apps Mobile apps can be used for a variety of purposes, such as monitoring attendance analytics, sending notifications and alerts, and providing access control to certain areas of the office. Reservation Systems Reservation systems allow employees to book workspace in advance. Additionally, they can be used for location analysis and people analytics (e.g., tracking employee usage of common areas). Badging Data Badging data refers to the workspace-related data collected by security badges that employees wear. This data can be used for a variety of purposes, such as tracking employee movement, identifying trends, attendance tracking, and improving security protocols. Get Maximum Value From Analytics Workspace With One Model Unlock your valuable facility analytics and attendance tracking data with One Model and specialized data modeling that enables you to extract, aggregate, and analyze your data like never before. See for Yourself. Connect with Us Today. Facility analytics is a powerful tool that today's workplaces can use to improve employee experience and boost productivity. One Model seamlessly connects your facility tracking data with other third-party resources — such as a Human Resources Information System (HRIS), Integrated Workforce Management System (IWMS), and surveys about the workspace — to help you improve the workplace and stay ahead of the competition.
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4 min read
Richard Rosenow
People analytics is a rapidly growing field that is helping businesses around the world to better understand and manage their employees. Businesses today are seeking ways to improve efficiency and gain a competitive edge all while retaining top talent. People analytics is an increasingly popular tool that helps organizations achieve these goals by providing insights into the behavior and performance of their employees. This is exactly why business leaders should care about HR analytics. Business Benefits of People Analytics Here are five key benefits that businesses can experience by using people analytics: Visibility People Analytics can be used to get a better understanding of what is happening within your company. By organizing and analyzing data about your workforce, you can gain a clearer picture of what is happening across your organization. This allows you to identify issues and opportunities for improvement, and take action to address them. Listening It is not possible for a CEO to have one-on-one conversations with every employee in the company. However, people analytics allows you to gather and analyze feedback from your employees through surveys, interviews, and other methods to bring workforce stories to life for leadership teams. This allows you to understand the concerns and needs of your workforce and make informed decisions based on this data. Identify Trends People analytics can help you spot larger trends and patterns that may not be immediately apparent to the human eye. For example, by analyzing attrition data, you may discover that employees who live a certain distance from the office are more likely to leave the company. This insight can help you make changes to improve retention and reduce turnover. Cost of Workforce Compensation is the largest single expense for many companies and a key factor in attracting, retaining, and recognizing top talent. People analytics can help you understand how different elements of compensation, such as base salary, bonuses, and equity, impact employee performance and retention. By analyzing this data, you can create a compensation strategy that is effective and fair for your workforce. When you think about it this way, it's really business analytics. In human resources, most of the critical information business leaders need to make decisions is found within their databases. Decision Support Insights produced through people analytics allow you to make decisions based on data rather than gut instincts and assumptions. By analyzing data about your workforce, you can identify opportunities for improvement and make informed decisions that are more likely to lead to success. Overall, people analytics is a powerful tool that can help businesses gain insights into their workforce and make data-driven decisions that drive efficiency, profitability, and retention. By connecting HR analytics to business benefits for your leaders, organizations can understand how to improve their operations, attract and retain top talent, and stay ahead of the competition. Did this help you get internal support? Schedule Time to Talk with Us Today.
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6 min read
Eliza Fury
Like many others, data is not a new concept. If you’re anything like me you’ve grown up with it. Whether it was making your first social media account or making your first online order, you’ve played a part in creating and maintaining data. That’s why it’s not surprising to know that data is leading us into the new Industrial Revolution. The difference with this revolution is that it’s taking place on our computers instead of in factories. The one lesson we can take from the old industrial revolution is the need to ensure people are safe within the work environment. Therefore, it's not surprising that HR analytics will be necessary to ensure a business has a vital human touch. Across our globe HR departments are changing to revolutionize the way they meet the growing demand for skilled workers. In a world where everything is digital, it’s essential that companies make the most of technology to stay ahead of the curve and this is no exception when hiring or upskilling current employees. In general, the most successful companies going forward will be ones that are employee-centric. This means greater pressure on HR departments, as evidenced by a recent survey which revealed that 64% expect more strain due to increased hybrid work environments, with 18% expecting a significant increase in their workload. As such, the HR revolution is a priority for many companies. Workforce Analytics vs. People Analytics: The Evolution of HRM On Gartner, workforce analytics is “an advanced set of data analysis tools and metrics for comprehensive workforce performance measurement and improvement”. HR has evolved to include HR analytics which has, in turn, assisted departments in providing organizions with clearer business insights. This means including people analytics and data analytics to get a holistic view of employees and delving deeper by not just looking into output levels but also attempting to identify longer-term trends within teams across multiple locations for better decision-making regarding workforce management. The first step you should know is the importance of individuals and how they are treated within the workplace. Much like our technology, the terminology has grown to reflect our workplace — people analytics, HR analytics, or workforce analytics — has shown the shift away from viewing employees solely through a production-oriented lens toward recognizing their human potential. This involves providing access to career development opportunities along with life support initiatives like vacation leave so that employees can grow both personally and professionally while still contributing positively in meaningful ways at work. HR in the 4th Industrial Revolution The key to unearthing what makes a successful business, is its people and how they are efficiently managed. Successful businesses give back to it’s people by Identifying top performers and rewarding those that give that sprinkle of extra effort. Focusing on people not only leads to higher rates of retention but also enables businesses to recognize potential in employees while they are still in an entry-level position. In the 21st century, companies that use HR analytics are revolutionaries as they can comprehensively evaluate performance. Workplaces are more complex than one perspective. In this employee revolution, data allows individuals to see unacknowledged high performers and use those insights to reward them — a concept that works hand-in-hand with staff retention. This engagement retains employees and saves professionals the cost of effort involved in replacing staff. How to Create a Better Work Environment One way to elevate top performers is to provide them with opportunities for promotion and recognition. A comprehensive people analytics platform can track how individuals react to certain situations, how they engage with tasks, how quickly they learn new skills, and how consistently they perform over time. HR data can catapult your work environment for the better by allowing you to access who is ready for a promotion or deserving of a bonus. In addition to tracking individual performance, people analytics software also provides valuable insights into how teams interact and processes flow within the organization. Managers can use this information to adjust how they divide tasks, form teams, and incentivize their employees. This can help maximize the value of each individual in the team and ensure that everyone feels like a contributing member. HR revolutionary companies can also potentially see methods that have unintentionally lowered effective and committed employees. By utilizing people analytics software over time, companies can look at HR data related to diversity, learning, and employee experience to give them a foundation of what it means to create a positive and cost-effective environment. Powering Your HR Engine to Make Better Decisions Ideally, all companies should be focusing on spearheading the HR evolution. There are no alternatives that are as effective as data-driven insights, whether it’s assessing employee performance or engagement levels, employers can recognize how to maintain teams that enjoy their work environments and learn from your top performers. Ultimately, your team is like an engine within your factory. A business's goal is to keep that engine running. HR analytics allows you to power that engine and make better decisions — because, without a well-oiled machine, no progress can be made.
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7 min read
Phil Schrader
People analytics provides insight into your organisation’s workforce. Your company’s workforce is at or near the top of your organisation’s expenses and strategic assets. Describing the importance of people analytics is very much an exercise in stating the obvious. For this reason, more and more companies are relying on people analytics, and that reliance is growing even as economic conditions change. In fact, as economic conditions become more challenging, people analytics becomes more, not less, important. Imagine a pilot flying in bad weather. Data on altitude, speed, location, etc become even more critical in that context. So yes, it makes sense to invest in people analytics now, even amidst our current economic concerns. People analytics in a recession is one of the most measurable strategies that HR can pursue. Whether you are hiring during a tight labor market or working through the implications of layoffs and reorganizations, you will want accurate, multi-dimensional, effective-dated, relational analytics ready to guide your decisions. People analytics doesn’t just help organise HR data. It generates faster insights from widely-dispersed HR data to make better talent decisions. For example, your people teams can better manage workforce and staffing levels, maximise productivity, and avoid guesswork about their diversity and inclusion objectives. “New and improved” HR reports alone won’t cut it. With people analytics, your analysts and managers can run exploratory data analysis to connect and understand relationships, trends, and patterns across all of their data. Additionally, the analysis adds context and meaning to the numbers and trends that you’re already seeing. The advantages of people analytics and why you should budget for it in a recession. Advantage #1 - Save money with people analytics. For nearly every business, labor is one of its most significant costs. But human capital is essential to generating revenue. HR analytics provides strategic and tactical visibility into one of your organisation’s most vital resources - its people. When your company uses analytics to manage the right people out, it can also use analytics to help you focus your recruitment efforts. After all, replacement costs for an employee can be as high as 50% to 60% with overall costs from 90% to 200%. For example, if an employee makes $60,000 per year, it costs $30,000 to $45,000 just to replace that employee and about $54,000 to $120,000 in overall losses to the company. HR analytics can also become a strategic advisor to your business to show insights into how your organization is changing. For example, people analytics can track trends in overtime pay, pay rate change for various positions, and revenue per employee (to name a few). While the revenue per employee calculation is a macro number, it’s important for you to be attuned to how it’s changing. Knowing the trends of your revenue per employee can lead directly to asking important questions about your people strategy: Are we investing in people now for future revenue later? Are we running significantly leaner than we have in the past? Are we running too lean? If metrics like revenue per employee or overtime pay are dropping or increasing over time, it could indicate that adjustments need to be made on a departmental level. Advantage #2 - Identify trends affecting morale or productivity. People analytics can also help you identify trends within your workforce that may be negatively affecting your business. HR data can help you pinpoint what is causing the change, and then address these issues early so you can avoid potential problems down the road. For example, Cornerstone used metrics such as policy violations and involuntary terminations to identify “toxic” employees harming the company’s productivity. The findings showed that hiring a toxic employee is costly for employers — to the tune of $13,000. And this number doesn’t even include long-term productivity losses due to the negative effects those toxic employees had on their colleagues. Source. With people analytics, Cornerstone identified common behavioral characteristics of toxic employees and now uses this data to make more informed hiring decisions. This created immediate benefits for their existing employees as well as future advantages as their workforce evolved. Advantage #3 - Recruit and retain top talent. The many benefits of people analytics also include a competitive edge when it comes to recruiting and retaining top talent. By understanding the needs and wants of your employees, you can create a workplace that is more attractive to potential candidates. In a world where data is constantly being updated, it's important for talent acquisition and HR leaders to make informed decisions quickly. HR analytics gives them that power at speed (rather than waiting months before seeing what happened). Using AI to discover related qualities of your top performers can also help your acquisitions team select candidates that will fit well into your culture and start driving results. Advantage #4 - Identify high-performing departments. Another one of the advantages of HR analytics is its ability to pinpoint positive changes as well. HR leaders can track department performance to know when to reward or incentivize employees for their productivity and work ethic. Additionally, it can help you keep your employees happy and engaged, which is essential for maintaining a high level of productivity (and sales). For example, Best Buy analyzed its HR data to discover that a 0.1% increase in employee engagement resulted in more than a $100,000 increase in annual income. Further, AMC’s people data showed that the theaters with top-performing managers earned $300,000 more in annual sales than the other theaters. These HR insights also helped this Fortune 500 company identify top talent and ideal candidates for its managerial positions, which ultimately resulted in a 6.3% increase in engagement, a 43% reduction in turnover, and a 1.2% rise in profit per customer. Identify Trends With Real-Time Labor Market Intelligence Ultimately, HR analytics offers real-time labor market intelligence to help businesses identify pain points causing turnover — something that’s essential in today’s hiring climate infamously referred to as “The Great Resignation.” The rise in turnover rates is a nationwide problem. It’s important for companies to find out why their employees are leaving and then create an effective strategy so they can stop the trend before it gets worse. One Model’s people analytics software can be a valuable tool for any business, especially during a downturn. In short: You should budget for HR analytics as an investment, not a cost. If you’re worried about a recession, you can start performing complex analysis on your data in just a few weeks. Let us show you 1:1
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5 min read
Stacia Damron
Is your company meeting its diversity goals? More importantly, if it is, are you adequately measuring diversity and inclusion success? While we may have the best intentions, today’s companies need to be focused on not just monitoring hiring metrics - but effectively analyzing them - in order to make a DE&I difference in the long term. But first, in order to do that, we need to take a look at key metrics for diversity and inclusion success. Let's talk about these diversity KPIs we’re measuring and why we’re measuring them. Without further ado, here’s 4 out-of-the box ways to measure diversity-related success that don’t have to do with hiring - all of which can help you supplement enhance your current reporting. Number 1: Rate and Timing of an Individual’s Promotions Are non-minority groups typically promoted every year and a half when minorities are promoted two years? Are all employees held accountable to the same expectations and metrics for success? Is your company providing a clearly-defined path to promotion opportunities, regardless of race or gender? Every hire should be rewarded for notable successes and achievement, and promoted according to a clear set of criteria. Make sure that’s happening across the organization - including minority groups. Digging into these metrics can help determine those answers and in the very least – put you on a path to asking the right questions. Number 2: Title and Seniority Do employees with the same levels of educational background and qualifications receive equitable salaries and titles? Often, minorities are underpaid compared to their non-minority counterparts. Measuring and tracking rank and pay metrics are two good ways to spot incongruences catch them early – giving your company a chance to correct a wage gap versus inadvertently widening it over time. Quantitative measures of diversity, like this, can help you see trends over time because changing diversity turning radius is a long process. Keep your eye on historically underpaid groups. A fairly paid employee is a happy, loyal employee. Number 3: Exposure to Upper Management and Inclusion in Special Assignments Global studies cited in a Forbes article revealed that a whopping 79 percent of people who quit their jobs cite ‘lack of appreciation’ as their reason for leaving. Do your employees – including minority groups - feel valued? Are you empowering them to make an impact? Unsurprisingly, people who feel a sense of autonomy and inclusion report higher satisfaction with their jobs – and are therefore more likely to stay. Are all groups within the organization equal-opportunity contributors? Bonus: On that note - are you performing any types of employee satisfaction surveys? Number 4: Training and Education Programs and Partnerships In 2014, Google made headlines for partnering with Code School. They committed to providing thousands of paid accounts to provide free training for select women and minorities already in tech. Does your company have a similar partnership or initiative with your community or company? As simple as it sounds – don’t just set it and forget it - track the relevant diversity KPIs that determine success and measure the results of your programs to determine if it is in fact, helping achieve your commitments towards improving diversity. The Summary: Success Comes by Measuring Diversity and Inclusion Hopefully, one of two (heck - maybe all four) of the items above resonated with you, and you’re excited to go tinker with your reporting platform. But wait - what if you have all this data, and you WANT to make some predictive models and see correlations in the data - and you’re all giddy to go do it - but you don’t have the tools in place? That’s where One Model can help. Give us your data in its messiest, most useless form, load it into our platform, and we’ll help you fully leverage that data of yours. Want to learn more? Let's connect about diversity metrics today. About One Model: One Model provides a data management platform and comprehensive suite of People Analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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9 min read
Chris Butler
HR professionals have heard the stories and read the news. Large organisations are having considerable success implementing a people analytics strategy for their organisations. That may leave you wondering what people analytics success can do for your own organisation. Perhaps you fantasise about incredible dashboards, with charts and graphs that are elegant and easy to disseminate across your teams and decision-makers. Maybe you yearn for your organisation’s people data to be governed and protected with the same diligence as other enterprise resource planning (ERP) data strategies. Or perhaps you simply want an end to the back-and-forth that’s associated with custom analysis and forecasting. Wouldn’t it be nice to have an HR data analytics technology that orchestrates everything needed for decision-makers to be able to make brilliant decisions quickly? Envision Winning on HR Analytics It's important to think about how your organisation will win with an HR analytics results approach that encompasses people analytics tools. For example, you will need to make a choice between buying an HR analytics platform or building one. If you choose to build a people analytics platform in-house (or you engage an outside party to build a custom people analytics platform for you, then you are accepting a loss in scalability, reducing time to value, and almost certainly limiting the completeness of your analytics-ready data set. We explain more about this choice in a recent whitepaper. Learn more On the other hand, if you choose to buy an off-the-shelf people analytics platform, you will surely find out that not all solutions are the same. As one of the industry’s most respected people analytics platforms, One Model brings an obsession around customer success that is unique when compared to other solutions on the market. We asked a number of our team here at One Model to share why they’re so passionate about customer success with people analytics. If you’re asking yourself, “What can people analytics do for me?”, keep reading. Building a Product that Creates Success Will Myers, One Model Product Lead Will spends his days making sure that One Model’s People Data Cloud™ people analytics platform delivers the result that our customers expect. He notes that, before you can create brilliant people stories or deliver impactful insights to across your organisation, you must first access the data that is needed, anywhere it may live. That’s tricky because these traditional data sources and repositories often lack the interfaces needed to do this. So you have to trust the team behind the technology to get data orchestration where it needs to be. Delivering Results by Changing How HR Teams Work Kelley Kirkpatrick, One Model Customer Success Lead in Australia Throughout her career, Kelley has seen HR teams collaborate over people data in countless ways. She has a unique perspective when it comes to investing in human resources data analysis technology. In her video, she lets us know that both data and people are key things to “get right” when expanding people analytics capabilities. Transparency drives trust, so Kelley works to ensure that People Data Cloud is the most transparent people analytics tool for her customers. It’s her favourite way to directly access metrics and models built from your data. Quick Turn-arounds Lead to More Wins Nicole LI, One Model Senior UX designer Nicole shares a great example that many technology buyers overlook when selecting a software vendor or technology partner. Most customers expect continuous improvement and rapid innovation. But they rarely get that from large companies. She’s extremely proud of One Model’s approach. It’s exciting to turn around upgrades and new features in a 2-week sprint. As our Senior UX Designer, Nicole thrives on solving problems quickly for her customers. She has some exciting user experience innovations to roll out in the coming months, so stay tuned. Everyone in Your Organisation Wins Jen Lincoln, One Model Customer Success Specialist Jen points out that unlocking your data to all the people leaders in the organisation generates excitement within her customer’s internal teams. One Model is democratising analytics and machine learning, so more people can make better decisions, faster. Have you been able to guess another big One Model strength from these videos? One Model People Make the Difference You can clearly see in these videos that the people we have at One Model make all the difference in your company’s people analytics success experience. We have talented product designers and developers that create unique, innovative tech and customer success champions that roll up their sleeves and do the heavy lifting for all of our customers. Our difference boils down to three strengths: people, platform, and product. I’m honoured to work with every member of the One Model team. We love talking about winning on HR Analytics! Want to have a conversation with a great member of the One Model team? Request Time to Chat with Us Today.
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6 min read
Nicholas Garbis
How do we measure the value of People Analytics? Is your organization making better, more data-informed talent decisions today versus a year ago? This is the ultimate test of any People Analytics program, initiative, team, COE, or department. If the answer is yes, the investments in People Analytics continue and expand. If the answer is no, then People Analytics budgets are questioned. So how can we demonstrate the value of People Analytics? In our latest whitepaper, "Measuring the Value of People Analytics," we address this from the ground up, starting with the mission of people analytics and moving into the utilization of the content delivered by the People Analytics team. Click here to get the full version. With a more comprehensive view of the how People Analytics creates value, you will be better positioned to build your business case for People Analytics. Whether you are seeking initial, incremental, or transformational level investments, this value framework will help you to convince your organization to become fully invested in HR analytics. Tackling the ROI Conundrum The proposed ROI calculations that many vendors recommend for People Analytics are not very good -- and some are downright laughable. This is one of the reasons I worked on this paper. Two common approaches: Estimated savings through efficiencies of system consolidation or process acceleration Estimated savings from the consolidation of systems or accelerating processes. Reduction in attrition or faster time-to-fill of job postings or other KPIs. The promise that People Analytics technology will reduce turnover and putting a financial value on it ... then hiding when the 'Great Resignation' starts or saying 'it would have been worse!' Ugh! This is not honest or helpful. We can do better in declaring the value we propose to generate. This is one of the key points in the paper. We Need to Address the Big “Why?” Why are we investing in People Analytics? Is the deliverable we're committing to - the “return” on the investment - as simple as a bit of system and process savings and some hypothetical lift to a couple of KPIs? Mission of People Analytics: Drive better, faster, talent decisions at all levels of the organization. We are investing resources in people analytics to drive and accelerate this mission. The value of People Analytics should be judged by the quality of talent decisions that are being made across the organization. We may not be able to get directly at measuring the quality of talent decisions (though we will address that in an upcoming paper), but we can use utilization as a proxy to get started. If our People Analytics deliverables are being utilized, we can logically assume that the users are placing value on them. They are 'voting' for the content. If it was not valuable, they would ignore it. In the paper, we demonstrate how utilization can be used to calculate value with relative ease across your People Analytics portfolio. Value Journey for People Analytics Looking at each 'analytics event' through a process sequence, a "value journey," we will see how critical People Analytics content is in delivering value at scale. To impact talent decisions at all levels of the organization, we need to build a smooth and fast self-service cycle (left side) by focusing on: creating analytics mindset/culture, applying user-centered product design, and communicating effectively and applying sound change management. "We have data that can help here." The diagram below shows the target picture, where a user, encountering the talent elements of a business challenge thinks "We have data that can help here." This is the critical first step that ideally flows them into a set of high-quality People Analytics products that can deliver the needed insights. Any business challenge can be divided into talent elements (staffing, skills, productivity, etc.) and non-talent elements (market forces, supplier issues, etc.). People Analytics provides value through products and services that support understanding and solving for the talent elements of the challenge. To impact talent decisions at scale requires People Analytics teams to deliver insight-generating self-service solutions. So now that we’ve covered that, how do we measure the value of People Analytics at your company? Is there a formula we can use to make our People Analytics investments more intentional? If so, how can we determine: Where we should focus our efforts? What content or communications efforts are necessary to deliver the outcomes we expect? Another core assumption in People Analytics is that your leaders’ time is a scarce and valuable resource. And we will use that assumption to anchor our value measurement approach. We assume that your organization’s leaders: Are selective about what they spend their time on. Choose to spend their time on things they consider valuable. See value in content if they engage with it regularly. Will rely on content that continues to inform better talent decisions over time. Download the paper to see the way we have calculated the value of a small People Analytics portfolio based on the value-utilization framework. Further work is needed to articulate how to measure the change in talent decision quality more directly. We will be tackling that in future content -- so keep an eye out for it! Get the equation in our Measuring the Value of People Analytics Whitepaper Ready to see how upgrading your People's Analytics solution will improve the value your team is bringing to the business?
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Featured
11 min read
Nicholas Garbis
The role of the Human Resources function is to ensure that the organization has the talent it needs to execute its strategies, making HR a strategic partner for the business. So if you’re an HR leader, your focus must always be on making the best talent decisions – best for the organization and best for the people in it. People Analytics is the most important part of your HR strategy because the best decisions are always data-driven ones. Mission of HR: Deliver a sustainably high-performing workforce that is engaged in their work, having positive, inclusive experiences with the organization, its leaders, and their team. Pillars of HR Strategy I think of HR strategy as having two pillars, each critical to the successful execution of an individual HR team’s mission. The first is the delivery and engagement/execution pillar, and it represents the HR organization’s systems, goals, metrics, processes, policies, procedures, and programs. Pillar 1: Delivery & Engagement Delivery Engagement/ Execution Talent Acquisition Employee Value Proposition (EVP) Employee Experience (Journey Design) Performance Management Compensation & Benefits Internal Communications Succession Planning Talent & Workforce Management Opinion Survey HR Operations & Technology etc. Learning & Development etc. Manager Effectiveness etc. But data is not information, and information is not knowledge. The best decisions involve all of these attributes. That’s why the second pillar of an HR strategy is decision support. People Analytics is the engine that powers the decision support for talent. It consists of the systems that organize the HR data to generate insights, the products that enable the People Analytics team to achieve scale and deliver services directly to leaders. These elements will enable the organization to make the most optimal people decisions for the organization. Pillar 2: Decision Support via People Analytics Systems Products Services Data Warehouse Interactive Analytics Ad-Hoc Analytics Analytics / Visualization Storyboards & Dashboards Workforce Planning Organizational Network Analysis C-Suite/Board Reporting & Analysis Location Strategy HR Operations & Technology etc. Predictive Models etc. Market Analytics etc. Without People Analytics, the human resource strategy won't be supported by sound decisions and can't be implemented. This will jeopardize the HR mission and risk the overall organizational strategy. On the floor, this can manifest itself as having the “wrong people in the wrong seats” or leaders making decisions that result in a sub-optimal or under-utilized workforce or introducing risk. Learn how to calculate the value of People Analytics. Do you need a People Analytics Strategy? Yes, of course. Strategy involves making resource and prioritization decisions. All People Analytics strategies must balance technology and consulting choices and recognize that there is no single strategy that's suitable for everyone. Some organizations need decision support tools that are quick and flexible. Others require robust and secure tools to support extremely complex decisions and are willing to sacrifice speed. And the incumbent capabilities and change readiness of each organization will vary. A sound People Analytics strategy will support the ways in which your organization makes decisions. And yes, your People Analytics strategy should be aligned to support the overall organization’s strategy and the HR mission. People Analytics Mission: Ensure that people decisions at all levels of the organization can be informed by quality data and insights, delivered through products and services that are ethical, easy to use and supported by effective communications and training. People Analytics teams will vary in their strategies for technology, deliverables, operating model, internal collaborations, and communications. Your people analytics strategy should articulate the technologies, deliverables, operating models, and methods of communication that will enable the best talent decisions. These decisions will be made by central groups such as the HR leadership team, as well as HR and business leaders in every part of the organization. The value of people analytics is to be measured by the improvement in talent decisions. But how do you conceptualize that value, nevertheless measure it? The People Analytics Value Cycle The value of People Analytics is the degree to which people data and insights are integrated into the organization’s talent decisions. People Analytics deliverables that are underutilized such as unused models, reports, and dashboards all incur costs to maintain and they contribute to technical debt through decommissioning, reviewing, or redesigning. The People Analytics team generates value for the organization every time a talent decision is made using data or actionable insights. Here are the steps that decision makers take to generate value. Seeing the opportunity to apply data to the decision Clarifying what questions will need to be answered Knowing where to access the data & analytics Generating insights from the data & analytics Making decision on action to take Implementing the action Following up to measure the impact of the action Delivering value from People Analytics requires an understanding of the behaviors that you are trying to shape. People analytics technology can multiply the value created by the team. People Analytics Tools Accelerate Time to Value People analytics technologies are often never seen by the end consumers of its decision support. Most users will never interact with the back-end technologies like data warehouses and predictive models. The users will work with innovative front-end solutions such as storyboards, dashboards, and reports that have been designed specifically for HR and business leaders. People Analytics technologies need to accelerate the process of data being available and applied in talent decisions. Visual tools such as storyboards, dashboards, and planning tools that HR and business leaders will use in their talent decisions require the integration of many unique sources of data. The software platform and the visual design should give the People Analytics team flexibility to create what is needed. The team needs to be responsive to the demand for new content and the ability to easily mine new insights. It may be tempting for HR IT teams and data engineers to build the data warehouse internally, but it is likely to take too long and cost too much. Plus, there’s the risk that a DIY data warehouse ends up being less flexible than a software-as-a-service platform. A SaaS-based solution like One Model delivers data integration, data warehousing, pre-built and custom storyboards, and predictive modeling tools, all in one package. SaaS solutions tend to cost less with a faster time to value, and include continuous innovation as well. Another key technology consideration is the visualization front-end which will be used by HR and business leaders. Sound visual design of the interface and its graphical components create wider accessibility and accelerate decisions by giving users who can generate insights in a moment’s notice. We recommend a People Analytics technology roadmap that addresses these areas. Data sources. The upstream systems from which data must be integrated. Data processing. The way the data from these source systems will be extracted, transformed, and loaded, including derived data and metrics calculations. Content. Creation of effective visualizations, storyboards, dashboards, and reports. Predictive modeling. Clear prioritization of the predictive models to be explored and developed. Employ a Product Mindset More and more People Analytics leaders are adopting a “product mindset” with respect to their deliverables. The product mindset appreciates that users have choices when they are seeking insights and that the People Analytics deliverables need to be easy and insightful. A product mindset incorporates concepts such as portfolio management, road mapping, user research and feedback, benchmarks and metrics, deploying minimum viable products, and managing and communicating change. Adopting a product mindset will help ensure that the People Analytics team is always delivering value to the organization. Choose Your Operating Model There isn’t a perfect People Analytics operating model for any particular type of organization. There is no right answer, but some approaches will be better and generate more value than others. The key is to design your team intentionally with a focus on value. The team structure, roles and responsibilities, and processes must align with the needs of the internal customers. The team should be composed of an appropriate mix of technical and consulting capabilities. Some teams may need more data engineers, others may need more visual storytellers. Make It Happen Since every organization strives for better, data-driven support, People Analytics is a critical facet of an effective human resource strategy. Talent decisions are the most important decisions any organization can make, and can help make HR a strategic partner to the overall business. People Analytics is decision science for the HR function and is a key pillar of HR strategy. Making it happen means being able to communicate the value it will bring in order to get the investment and support you need. Start by calculating that value. Get the equation in our Measuring the Value of People Analytics whitepaper Ready to see how upgrading your people's analytics solution will improve the value your team is bringing to the business?
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Featured
17 min read
Chris Butler
Workday vs SuccessFactors vs Oracle Ratings Based on Experience Integrating HR Tech for People Analytics This vendor-by-vendor comparison will be a living post and we will continue to update as we have time to collect thoughts on each vendor and as we complete integrations with new vendors. Not every source we work with will be listed here but we'll cover the major ones that we often work with. At One Model we get to see the data and structure from a load of HR systems, and beyond, basically anything that holds employee or person data is fair game as a core system to integrate for workforce analytics. After more than a decade of HR analytics integration architecture experience where the solution is directly integrating data from these systems into analytics and reporting solutions, we have a lot of experience to share. Below I'll share our experience with highlights from each system and how they align with creating a people analytics warehouse. Some are better than others from a data perspective and there's certainly some vendors that are yet to understand that access to data is already a core requirement of buyers looking at any new technology. Bookmark this blog, add your email to the subscription email list to the right, or follow me (Chris Butler) and One Model on LinkedIn to stay up to date. A Quick Note on HRIS Platform Ratings Ratings are provided as an anecdotal and unscientific evaluation of our experience in gaining access to, maintaining, and working with the data held in the associated systems. They are my opinions.] If you would like to make use of any of our integrations in a stand-alone capacity, we now offer a data warehouse only product where you utilize just our data pipeline and modelling engine to extract and transform data into a data warehouse hosted by One Model or your own data warehouse. We'll be releasing some more public details soon but you are a company that likes to roll your own analytics, visualizations, and just need some help with the data side of the house, we can certainly help. Contact Us Cloud HRIS Comparison Workday One Model rating - 2.5/5 Method - API for standard objects, built-in reporting for custom objects (via reporting-as-a-service, or "RaaS") The Good - Great documentation, Easy to enable API access and control of accessible fields, and Good data structures once you have access. The RaaS option does a good job but is limited. The Bad - Slow; Slow; Slow; No custom fields available in API, Geared towards providing a snapshot, number of parallel connections limited, constant tweaking required as new behaviors identified, Expert integration skills required; True incremental feeds require you to read and interpret a transaction log Workday Requires a Custom-Built People Analytics Integration Architecture Workday analytics embedded into the product is underwhelming and we're yet to see Prism Analytics make a dent in filling the needs that people analytics teams or HR analysts have beyond convenience analytics. So in the meantime, if you are serious about improving reporting and people analytics for Workday you're going to need to get the data out of there and into somewhere else. On the surface, Workday looks to have a great API, and the documentation available is excellent. However, the single biggest downfall is that the API is focused on providing a snapshot, which is fine for simple list reports but does not allow a people analytics team to deliver any worthwhile historical analysis. You don't get the bulk history output of other systems or the ability to cobble it together from complete effective-dated transactions across objects. To capture the complete history we had to build an intense process of programmatically retrieving data, evaluating, and running other API calls to build the full history that we need. If you want more detail take a look at my blog post on the subject The end of the snapshot workday edition. The complexity of the integration, therefore, is multiplied and the time taken suffers immensely due to the object-oriented architecture that requires you to load each object into memory in order to be able to retrieve it. A full destructive data extraction means you're looking at 8+ hours for a small-medium enterprise and expanding to a week if you're a giant. The problem is exacerbated by the number of parallel connections allowed to run at a fraction of the stated limit. A full historical API integration here is not for the faint of heart or skill, we have spent 12+ months enhancing and tweaking our integration with each release (weekly) to improve performance and solve data challenges. Our integration to give a sense of scale generates some 500+ tables that we bring together in our modelling engine in preparation for analytics. Beware of Oversimplifying the API Integration Out-of-the-box integration plugins are going to be focused on the snapshot version of data as well so if you don't have the integration resources available I wouldn't attempt an API integration. My advice is to stick with the built-in reporting tools to get off the ground. The RaaS tools do a good job of combining objects and running in a performant manner (better than the API). However, they will also be snapshot focused and as painful as it will be to build and run each timepoint you will at least be able to obtain a basic feed to build upon. You won't have the full change history for deeper analysis until you can create a larger integration, or can drop in One Model. Robert Goodman wrote a good blog a little while back looking at both the API and his decision to use RaaS at the time, take a read here. Workday API vs RaaS Regardless of the problems we see with the architecture, the API is decent and one of our favorite integrations to work with. It is, however, little wonder that with the data challenges we have seen and experienced, half of our customers are now Workday customers. One Model Integration Capabilities with Workday One Model consumes the Public Web Service API's for all standard objects and fields. One Model configures and manages the services for API extractions, customers need only to create and supply a permissioned account for the extraction. Custom objects and fields need to use a Raas (Report as a service) definition created by the customer in the Enterprise Interface Builder (EIB). The Report can then be transferred by SFTP or can be interacted with as an API itself. Figure 1: One Model's data extraction from Workday SuccessFactors One Model rating - 4/5 Method - API The Good - A dynamic API that includes all custom MDF data!! Runs relatively quickly; Comprehensive module coverage; The Bad - Several API endpoints that need to be combined to complete the data view; Can drop data without indication; At times confusing data structures 4 out of 5 is a pretty phenomenal rating in my book. I almost gave SuccessFactors a perfect 5 but there are still some missing pieces from the API libraries and we've experienced some dropped data at times that have required some adaptations in our integration. Overall, the collection of SF APIs is a thing of beauty for one specific reason: it is dynamic and can accommodate any of the Meta Data Framework (MDF) custom changes in its stride. This makes life incredibly easy when working across multiple different customers and means we can run a single integration against any customer and accurately retrieve all customizations without even thinking about them. Compared to Workday where the API is static in definition and only covers the standard objects this facet alone is just awesome. This dynamic nature though isn't without its complexities. It does mean you need to build an integration that can interrogate the API and iterate through each of its customizations. However, once it is complete it functions well and can adapt to changing configurations as a result. Prepare to Merge API Integrations for People Analytics Multiple API endpoints also require different integrations to be merged. This is a result of both upgrades in the APIs available in the case of the older SuccessFactors API and the OData API as well as providing an API to acquired parts of the platform (i.e. Learning from the Plateau acquisition). We're actually just happy there is now an API to retrieve learning data as this used to be a huge bug bear when I worked at SuccessFactors on the Workforce Analytics product. The only SF product I know of right now that doesn't have the ability to extract from an API is Recruiting Marketing (RMK) from the jobs2web acquisition, hopefully, this changes in the future. Full disclosure, I used to hate working with SuccessFactors data when we had to deal with flat files and RDFs, but with the API integration in place, we can be up and running with a new SuccessFactors customer in a few hours and be confident all customizations are present. Another option - Integration Center I haven't spoken here about the new Integration Center release from earlier last year as we haven't used it ourselves and only have anecdotal evidence from what we've read. It looks like you could get what you need using the Integration Center and deliver the output to your warehouse. You will obviously need to build each of the outputs for the integration which may take a lot of time but the data structure from what I can tell looks solid for staging into an analytics framework. There are likely a lot of tables to extract and maintain though, we currently run around 400+ tables for a SuccessFactors customer and model these into an analytics-ready model. If anyone has used the Integration Center in an analytics deployment please feel free to comment below or reach out and I would be happy to host your perspective here. One Model Integration Capabilities with SAP SuccessFactors One Model consumes the SF REST API's for all standard fields as well as all customized fields including any use of the MDF framework. One Model configures and manages the service for API extractions, customers need only to create and supply a permissioned account for the extraction. SF has built a great API that is able to provide all customizations as part of the native API feed. We do us more than one API though as the new OData API doesn't provide enough information and we have to use multiple endpoints in order to extract a complete data set. This is expertly handled by One Model software. Figure 2: One Model's data extraction from SuccessFactors Oracle HCM Cloud (Fusion) One Model rating - 2/5 Method - HCM Extracts functionality all other methods discounted from use The Good - HCM Extracts is reasonable once you have it set up. History and all fields available. Public documentation. The Bad - The user interface is incredibly slow and frustrating. Documentation has huge gaps from one stage to the next where experience is assumed. API is not functional from a people analytics perspective: missing fields, missing history, suitable only for point-to-point integrations. Reporting/BI Publisher if you can get it working is a maintenance burden for enhancements. HCM Extracts works well but the output is best delivered as an XML file. I think I lost a lot of hair and put on ten pounds (or was it ten kilos?!) working through a suitable extraction method for the HCM Cloud suite that was going to give us the right level of data granularity for proper historically accurate people analytics data. We tried every method of data extraction from the API to using BI Publisher reports and templates. I can see why people who are experienced in the Oracle domain stick with it for decades, the experience here is hard-won and akin to a level of magic. The barriers to entry for new players are just so high that even I as a software engineer, data expert, and with a career spent in HR data many times over, could not figure out how to get a piece of functionality working that in other systems would take a handful of clicks. Many Paths to HRIS System Integration In looking to build an extraction for people analytics you have a number of methods at your disposal. There's now an API and the built-in reporting could be a reasonable option for you if you have some experience with BI Publisher. There are also the HCM Extracts built for bulk extraction purposes. We quickly discounted the API as not yet being up to scratch for people analytics purposes since it lacks access to subject areas, and fields, and cannot provide the level of history and granularity that we need. I hope that the API can be improved in the future as it is generally our favorite method for extraction. We then spent days and probably weeks trying to get the built-in reporting and BI Publisher templates to work correctly and deliver us the data we're used to from our time using Oracles on-premise solutions (quite a good data structure). Alas, this was one of the most frustrating experiences of my life, it really says something when I had to go find a copy of MS Word 2006 in order to use a plugin that for some reason just wouldn't load in MS Word 2016, all to edit and build a template file to be uploaded, creating multiple manual touchpoints whenever a change is required. Why is life so difficult?? Even with a bunch of time lost to this endeavour our experience was that we could probably get all the data we needed using the reporting/BI publisher route but that it was going to be a maintenance nightmare if an extract had to change requiring an Oracle developer to make sure everything ran correctly. If you have experienced resources this may work for you still. We eventually settled on the HCM Extracts solution provided that while mind-numbingly frustrating to use the interface to build and extract will at least reliably provide access to the full data set and deliver it in an output that with some tooling can be ingested quite well. There are a number of options for how you can export the data and we would usually prefer a CSV style extraction but the hierarchical nature of the extraction process here means that XML becomes the preferred method unless you want to burn the best years of your life creating individual outputs for each object tediously by hand in a semi-responsive interface. We, therefore, figured it would be easier, and enhance maintainability if we built our own .xml parser for our data pipeline to ingest the data set. There are .xml to .csv parsers available (some for free) if you need to find one but my experience with them is they struggle with some files to deliver a clean output for ingestion. With an extract defined though there's a good number of options on how to deliver and schedule the output and reliability is good. We've only had a few issues since the upfront hard work was completed. Changing an extract as well is relatively straightforward if you want to add a field or object you can do so through the front-end interface in a single touchpoint. We do love Oracle data, and don't get me wrong - the construction and integrity are good and we have a repeatable solution for our customer base that we can deliver at will, but it was a harrowing trip of discovery that to me, explains why we see so few organizations from the Oracle ecosystem that are out there talking about their achievements. Don't make me go back, mommy! Want to Better Understand How One Model can Help You? Request a Demo Today. Other HRIS Comparisons Coming Soon ADP Workforce Now
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11 min read
Jamie Strnisha
One of the most common reporting challenges companies face is balancing headcount over time by adding and subtracting Hires and Terminations. How to Calculate Headcount? The process seems like it should be simple, especially when someone with a background in finance or accounting first looks at the issue. The misconception is that the company’s headcount will balance in much the same way money in a financial statement balances, where the analyst takes an initial amount of money the company has, and adds the money that came in for the month (e.g. customer sales, invoices) and subtracts the money that went out for the month (e.g. transportation, payroll cost, rent) and results in a final amount for the month, which then starts over the next month. If that’s how a financial statement is balanced, it seems that the same concept should be easily applied to balancing headcount reporting metrics. It might seem that a company should be able to use the following formula: Starting Headcount + Hires – Terminations = Ending Headcount And everything would balance and net out. Unfortunately, accounting does not seem to work out the same way in HR as it does in Finance. Rarely (if ever) does this simple formula work when counting people instead of money. There are a number of common reasons why this formula fails when applying it to the reconciliation of headcount reports over time: People that start at the beginning of the month are included in both starting headcount and hires for the time period, leading to some of the same people being counted twice. People that leave at the end of the month are included in both ending headcount and terminations for the time period, again leading to double counting. People that are on leave of absence may suddenly enter or exit headcount without hire or termination. The company may have restrictions on certain types of workers (e.g. Interns, Contractors) and exclude them from the headcount when they are in that category. If these workers move from an excluded category to one that is included in the headcount, or vice versa, they might suddenly appear or disappear from the headcount without hire or termination. The company may want to exclude certain Hire and Termination actions, such as acquisitions or divestitures, which again will cause an unbalanced headcount and a worker to suddenly appear or disappear from the headcount Fortunately, One Model can solve all of these issues in balancing headcount relatively easily by creating a new set of metrics specific to the company’s data that include populations that might not normally be counted, and exclude or include Hires and Terminations at the beginning and end of a time period. The New Metric: Reconciliation Headcount This Reconciliation Headcount Reporting Metric is effectively a more accurate mathematical equation that balances headcount to reflect these quirks in people data to match the financial statement approach to reconciliation. Each customer that works with One Model will have a slightly different version of a Reconciliation Headcount metric based on the methodology they use to count a Hire or Termination. An example formula for this metric may look like this: (Ending Headcount + Terminations on the Last Day of the Previous Time – Terminations on the Last Day of the Current Time Period) – (Starting Headcount + Hires – Terminations – Divestitures) When properly constructed, the new metric will correctly sum to 0, eliminating the problem HR sometimes has in justifying apparent irregularities in reconciling headcount. If the Metric does not equal 0, it means that there is at least one person or event in the data that does not have a requisite hire or termination to balance it out and that the company should investigate the record. One Model can also provide the company with a set of metrics that explain the difference between the events and populations that are included in the inputs for the new metric calculation and what the company would otherwise use for standard reporting on headcount, hires and terminations. One Model Application: Using the Reconciliation Headcount Metric Once the Reconciliation Headcount Metric is created, it can be used to monitor and understand data changes over time that might not be apparent in a less refined approach to reconciliation. The following is an example of the Headcount Reconciliation Metric for Company A across 4 months. If all factors that affect headcount are included in the reconciliation calculation, then each month the Headcount Reconciliation Metric would show as 0. In this example, it appears that the headcounts for November and February have no irregularities, but for December and January, instead of the expected 0, the results show 1 in December and 2 in January. Pinpointing the Discrepancy: Is it Even Possible? Without the Reconciliation Headcount Metric and One Model, it could be difficult to pinpoint the source of these discrepancies. In fact, it might not be possible at all, depending on how the data was reported. If an analyst was lucky enough to be using lists of individuals to perform the reconciliation and had the actual records for all relevant points in time (the beginning and end of each month), they might be able to figure out the specific people accounting for the differences in December and January by using vlookup formulas in Excel to locate each difference. Of course, this would make the entire reconciliation process very time consuming Reconciling headcount may not even be possible in all situations. In some cases, the analyst may be adding or subtracting data from past months’ reports that have already been aggregated. Using aggregated data would make the reconciliation process almost impossible, since the data in the source system may have changed since the reports were run, and the analyst would not be able to pinpoint the specific people whose situations are creating the discrepancies. One Model’s List Report Feature: Easy Identification of the Discrepancy in Headcount Reporting Metrics The One Model platform has a unique feature that eliminates all of these problems and makes the reconciliation process very easy. This feature is called List Reports. In One Model, a user can take a metric and then look within it to find the data points that are causing the discrepancies. In the example of Company A, where it appeared that there were discrepancies in the December and January headcount reports, the analyst creates a List Report that includes the Headcount Reconciliation Metric, Worker Number and Name of every individual accounted for in that period. Any individual whose status changed during the time period but was not properly accounted for in the reconciliation process would be flagged as a + or - in the Headcount Reconciliation column. The List Report can then be filtered to show only those individuals whose records are the cause of the apparent accounting error. In the example of Company A above, there was a net discrepancy of one for the month of December. By exporting the data and filtering out the 0s, only one record had a +1 in December: In only a couple simple steps, it was easy to determine that Joe Williams’s record is the source of the discrepancy in the headcount for December. Identifying the Reason for the Discrepancy and Rebalancing Headcount After identifying Joe Williams, the next question is why his record caused this discrepancy. Since it appears that his record caused an addition to headcount, it may make sense to first look at the data for hires and see if a new code was added to Company A’s HRIS that was not included originally. In the example for Company A, Joe entered Company A through an acquisition that was not coded as a hire. As a result, it now is apparent that the Headcount Reconciliation metric should be revised to include individuals who joined Company A through an acquisition. After Headcount Balances, What Next? Net Internal Change This Headcount Reconciliation Metric can now be used to better understand net internal change within Company A. In the example below, Company A’s Headcount Reconciliation Metric is broken out by Department. In disaggregated form, it’s easy to see that in December the company had 1 net move into Commercial and 1 net move out of HR. Even more helpful, it’s possible to see that while Headcount balanced at the overall level for November and February, there were actually movements across departments in those months. The fact that those movements netted to zero made them seem to vanish from the reconciliation metric, but One Model still makes it possible to identify this movement. One Model’s List Report Identifies the Individual Change Records Looking again at December, adding the Department field into the List Report reveals a department change for a different worker. In this situation, we see that Chris Jones moved from HR to Commercial in December. Using the Reconciliation Headcount Metric, makes it possible to look at internal movements and understand how the company’s headcount has changed internally over time. Difference between Typical Internal Movement Metrics and the Headcount Reconciliation Net Change While customers can traditionally use events like Transfers, Promotions, Demotions to pinpoint internal movement, these methods can often be deceiving. Very often customers do not have strict business processes about what is being counted in these movements and events in the HRIS are coded as Transfers when they’re technically a data correction. A Promotion may get coded as such when it really is a Transfer or Lateral move because the manager wants to send a positive message to an employee. While the net difference derived from the Headcount Reconciliation Metric doesn’t necessarily resolve all of those issues, it allows the analyst to see the specific internal net change across time. The examples above used months, but the time period could have been any (e.g. year, quarter, week). If you want to know more about One Model or Headcount Reconciliation, we’d be happy to talk to you. Personally, I love talking about people data and how to construct metrics to drive business decisions. Want to learn how your company can benefit from using One Model? Have questions on your team's specific challenges in balancing headcount and internal net movements? Learn more about the benefits of One Model and sign up for a demo.
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9 min read
Chris Butler
A major shift has occurred in Human Resources over the past five years. The world went from a handful of companies experimenting with people analytics - early adopters - to thousands of companies investing in dedicated roles and teams to take on a new way of thinking about Human Resources. What can you learn from the early adopters? Every CHRO who has moved forward with people analytics has some secrets to success. These secrets will help you provide a better, faster way to more effectively deploy HR resources and build a successful organization. Better human resources strategy means better business You need to accept and measure change There will be change. That is the only constant. Change could come from any number of places like new technology, new customers, new leadership, new products, new competition, new market and strategies. You will get maximum benefit from your HR strategy if you accept the reality that change is the only constant - the only certainty is a world of uncertainty. If you want to survive in a world of uncertainty you need to have a process to constantly take in new information to understand changing reality and use this new information to adapt. You need a way to measure to see if your organization is changing in the way that you and your leadership team expect. Change is what people analytics is for. Hire specific talent for a meaningful business advantage The problem is that most HR strategies are far too general to develop any sustainable advantages: “We will hire great people.” Great idea - you and everyone else! You cannot do everything well all the time - as the cost to attract and retain top talent just gets more expensive so you have to choose. You have to choose specifically where you want your business advantage to be and then you have to figure out how to create this advantage through People. In this way we and others may realize that the people's perspective of the business is not necessarily based in “soft stuff”, “political correctness” and administrative minutia, but profound business insights that arise in conjunction with observation and reason. Basically there are two types of insight: that which is not based on any special observable reason and that which is backed by observable reason. In the case of the second type of view, an individual is motivated to examine an insight and investigate its relevance to his or her situation, needs and requirements. Actions are applied after seeing the reason why this insight is advantageous. Change with people, new action, is motivated by new insight; that insight is powered by people analytics. Don’t get led astray by “traditional” HR metrics it is not always clear how to relate HR actions to business impact, and so we settle to monitor activities as a measure of progress which is a traditional metric for success. Measuring progress as activities that have an unknown relationship to current business objectives leads HR into waste. HR is broken into multiple functional centers of excellence (Staffing, Benefits, Compensation, Labor Relations, Talent Management, Organization Design), each with different goals and activities, we end up with hundreds of metrics that do not align with each other and do not drive towards a unified goal. This results in efforts that either have no impact or work against each other, not to mention waste in the process of analytics itself. Because we have not previously devised a single HR metric that has a direct business impact that can be applied universally across organizations and sub organizations, we substitute simplistic measures that while a good intention, may not be a universally good idea, may conflict with other objectives and may not correlate in any way with measurable business impact. This results in the wrong efforts/objectives. Investing heavily in quantitative metrics doesn’t automatically give us solutions. Metrics can usually tell us what’s going wrong, usually not why. The more you invest in quantitative metrics, with a process for qualitative input, the more you end up drowning in a sea of non-actionable data. Create a culture of success Those leaders who want to create a healthy organization or “culture of success” are motivated (or should be motivated) to attain a genuine camaraderie with all people in an organization. When a group of people have a common vision, maintain a sense that they are all in it together, and have compassion for each other then there is nothing that cannot be accomplished. At this juncture, in addition to many great spiritual teachings of varied doctrines, we also have a foundation of great insights in science and engineering and access to those examples and methods. At the heart of it: we analyze people within an organization for benefit to that organization - and that is also the people. It must be both. Useful analysis helps us all understand current reality and take the right actions now to achieve optimal outcomes: an outcome of joint benefit to managers, employees, shareholders and possibly society. A continual reduction in tenacious organizational problems and continual reinforcement of a culture of success is the ultimate result of useful analysis. Disciplined action (as opposed to frantic thrashing) is the benefit of useful analysis. Our concept of a healthy organization is not something physical. Therefore the spread of a healthy culture depends on increasing the depth of understanding of the benefit of new actions to provide strong motivation to pursue those new actions. When we are able to reduce the defects in how we think about people in an organization a healthy culture will naturally increase. Thus, effecting positive transformations in organizations through observation and feedback situation by situation, subdivision by subdivision, manager by manager, and employee by employee is the method we will employ to effect the change we desire. Unlike manufactured goods it should be fairly obvious that culture is not a tangible entity, that it cannot be sold or bought in the marketplace or physically constructed. Watch out for HR constraints (budget, credibility, time) Most of the programs HR watches over have very large budgets. Labor costs are frequently 70% or more of revenue. Benefits may represent 30% or more of labor costs. On an absolute basis these costs increase over time as the employee base grows. Things get sideways when business plan projections get off track and the cost of labor grows faster than revenue or when revenue retracts. It is critical for CHROs to be able to identify—quickly, early, and accurately— whether a project or activity is worth pursuing, rejecting, continuing or dropping so it may protect its commitments and preserve resources for those programs that drive the most value. Besides the obvious constraint of budget, the other constraint is credibility. In order to influence, HR professionals need to hold on to and build on what little credibility they start with. As CHRO you will have to justify HR’s right to have a seat at the business table by demonstrating the business impact of your programs to a CEO, management team or business line head to whom you support. At some stage, you will all be called on to demonstrate progress. Finally, we are all constrained by time. Every minute spent on an activity that is doomed to fail is wasted. HR has historically relied on two categorical measures of progress: how much stuff they are doing and how much people like what they are doing. Yet unfortunately, both of these metrics are unreliable proxies of business impact and both of these lead us down the wrong path—building something that ultimately does not matter, has no impact on the business or worse, the wrong impact. People analytics can be hard First, there is a misconception around how successful earth shattering people analytics get built. The media loves stories of “wunderkind” nerds invading HR who are so smart they helped the moribund HR function (usually at some cool tech company) figure this problem out. The reality, however, rarely plays out quite as simply. Even the unveiling of the hiring algorithms at Google, in Laszlo Bock's words, was years in the making, built on the contributions of many and several incremental innovations (and failures). Second, the classic technology-centric Reporting or “Business Intelligence” approach front-loads some downstream business partner involvement during a “requirements-gathering phase” but leaves most of the HR business partner and business customer validation until after the reporting solution is released. There is a large “middle” when the Analytics function disengages from the ultimate intended users of these reports for months, maybe even a year, while they build and test their solution. Sometimes the solution is rolled out in HR first, just to be sure it is safe for humans before inflicting it on the rest of the organization. Imagine a few wild eyed HR people hiding in the bushes outside the office preparing to jump up on an unexpecting executive on his way into work one morning. During this time, it’s quite possible for the Analytics function to either build too much or be led astray from building anything remotely useful to the organization. Third, People are complex and messy. People are not structural engineering challenges that are within the abilities of an engineer to control precisely. People and organizations are not like machines or computers. There is always a certain degree of uncertainty about the effect of our actions on people and organizations. We try things based on an entirely plausible premise and they fail. Usually we had not factored in or considered the thing or things which made it fail. There are too many variables, too many possibilities and too much change occurring within and all around us. Is this not in some sense the beauty of life? Would you rather take this away? In human systems, failure is not the problem, the problem is failure to learn from the failure. If we want to improve HR we should shift our attention to how we can learn more quickly. These Secrets are the CHRO’s Real Guide to People Analytics People analytics gives CHROs a better, faster way to more effectively deploy HR resources and build successful organizations. People analytics enable better listening, learning, strategic focus, measurable business impact, and rigorous process.
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7 min read
Chris Butler
When I first started work with InfoHRM in the people analytics domain back in 2006, we were the only vendor in the space and had been for over a decade. The product was called Workforce Analytics and Planning and after its acquisition by SuccessFactors (2010) and SAP (2012) it's still called that today. So what's the difference? Why do we have Workforce Analytics, HR Analytics, and People Analytics and can they be used interchangeably? I have to give credit to Hip Rodriguez for the subject of the blog. He posted about People Analytics vs HR Analytics a couple weeks ago and I've followed the conversation around it. Hip's Linkedin post here. So what does the data say? Workforce vs HR vs People? Being an analytical person at heart, I turned to the data and analyzed job titles containing "HR, Workforce, People, Human" and "Analytics or Analyst". As you can see in the table below (truncated for space), the data isn't supportive of people analytics being the most popular. In fact, you have to get down to row 25 before you see a people analytics title. HR Analytics and Workforce Analytics related titles are the clear leaders here by volume. Keep in mind though that titles particularly for less senior roles can take time to adapt, especially for more rigid position structures in larger organizations. Likely, many of these junior roles have a more basic reporting focus than an analytics focus. So why then does it feel like People Analytics has become the dominant term for what we do? The Evolution of HR Analytics (and my opinion) I believe that it's not so much a difference between HR Analytics and People Analytics, but rather, an evolution in the term. Let’s Start with the Evolution of Workforce Analytics Early when we were delivering Workforce Analytics it was to only a handful of forward-thinking organizations that also had the budget to be able to take workforce reporting seriously. I specifically say reporting because mostly that's what it was: getting data in the hands of executives and directors weren't happening at scale so even basic data would blow people's minds. It's crazy how often the same basic data still blows people's minds 20 years later. There were not many teams running project focused analysis like there are today. For example: looking at drivers of turnover to trial different retention initiatives or how onboarding programs affect net promoter scores of recent hires. Workforce Analytics was for the most part aggregate reporting. The analysis of this was primarily driven by hardcore segmentation of this data looking for nuggets of gold by a handful of curious people. It was done at scale with large numbers and rarely focused on small populations. A Look at the Difference Between HR Analytics HR Analytics is by far and away the most common term and has lived alongside Workforce Analytics for a very long time now. It is a natural extension of the naming of the human resources department, you're in HR and looking at HR data from our Human Resources Information System (HRIS) you are therefore an HR Analyst. If we were to be more aligned with the term we would be analyzing the effectiveness and the efficiency of the HR function e.g. HR Staffing ratios and everything else that goes along with it. An HR Analyst in this fashion would be more aligned with Talent Acquisition Analyst roles that we see growing in the domain today. In my view, HR Analytics is really no different to Workforce Analytics and we will see these titles transition towards People Analytics over time. Why Evolve to People Analytics Then? I do not believe there is a significant difference between people analytics vs HR analytics vs workforce analytics in terms of the work that we do. The evolution of the terms, in my opinion, has been more about how we view people as individuals in our organizations as opposed to the large scale aggregate of a workforce or even worse to me as "human resources". We've recognized as a discipline that people need to be treated and respected as individuals, that we need to provide career development, and life support, and that it is important that people actually take vacation time. It is treating people as people and not numbers cranking out widgets. It is no coincidence that knowledge worker organizations have been the biggest adopters of people analytics, they have the most to gain especially in the tight labor market where choice and compensation are abundant. The care for workers must exist whereas many years ago it was a different story. I love the fact that we have people analytics teams who are going deep on how they promote a diverse workforce, on how they create career development opportunities. We even have one customer that integrates cafeteria data into their solution to help identify what people are enjoying eating. So is it just a branding change? Yes, and No. Our space has definitely matured, and our capabilities have grown. We've moved from basic reporting and access to data which is now table stakes, to project-based analysis with intent and hypotheses to prove or dispel. People Analytics is a more mature discipline than it ever was but effectively the same activities could roll up under either term. Impacting people's work lives through our analysis of data is ultimately our goal, having in mind that outcome is why we'll see further adoption of People Analytics as a term. We'll see job titles change to reflect this move over time. And I'm certainly not always right, and there are larger nuances between these terms applicable to some organizations. Heather Whiteman gives a good overview of a more nuanced definition here Interested in Learning More? So whether you call it HR Analytics or People Analytics, if you're new to this and want to understand what it can do for an organization, check out the eBook written by Heather White and Nicholas Garbis on Explore the Power of People Analytics for a further dive in this area. Download eBook Today
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28 min read
Tony Ashton
[This article is taken from a presentation I delivered as part of a broader session on People Analytics for the Australian Human Resources Institute (AHRI) QLD Analytics Network on 7 October 2021. It uses the presentation slides and accompanying presentation script only slightly modified from the spoken word to fit the written form. thanks, Tony] This article focuses on setting up a people analytics capability in your organization and thinking about what the key challenges are and how to resolve those. Let’s start by talking about data-driven insights, people analytics is the focus. But more broadly, there is an untold number of articles and research papers on the importance of data-driven decision making and my bookshelf is full of these books and papers. And I'm sure yours are too (or your virtual bookshelves). Here is a nice example from Deloitte to set the scene. Of the organizations they surveyed 39% of those have a strong analytics culture, and 48% of those were significantly exceeding their business goals. Compared to those that didn't display a strong analytics culture, only 22% were significantly exceeding their goals. There's a double whammy in terms of the proportional impact that Analytics gives to an organization. But importantly, also here, there's this angle of culture. In the survey, most executives believe that they weren't really that insight-driven as an organization. So there is this challenge between the ability to pull data together, derive insights, and actually make decisions and make that a part of the framework for how business is done. So why is this important to HR? If you can't connect people to business outcomes, then you're really just doing stuff because you think it's a good idea. Doing stuff that you think is a good idea is ok, but it will only get you so far - being able to prove it is a good idea and measure your impact is another thing entirely. The importance of data-driven decision making for HR Using data helps you prioritize your strategies. You can't do everything, so you need to focus your resources on HR. Metrics and data help you do that and this helps you build that culture of data-driven decision making. If you think about the people space, people related decision making and HR processes are all underpinned by principles like merit, natural justice, fairness and transparency. Without a data-driven approach to this, you're very kind of at risk of replicating the same diversity issues that you see in many organizations: pay equity issues, how promotions and pay increases are awarded, or who should be hired. Some of these examples are on a macro scale, for example, your whole company's diversity profile, and some at the micro-level, but the same general principles apply. Data is important for setting strategy and for tactical decision making At the micro-scale let’s take a specific example of an individual hiring decision. We have selection criteria for hiring to ensure we get the right person for the role and use multi-source inputs to the process to base decisions on evidence and to avoid bias, nepotism, discrimination etc. At the micro-level, you heavily rely on good processes, training and company culture. Ideally, guiding these processes and strategies would be a great analytical understanding of your organization’s diversity profile, the skills and capabilities required for the next 2-3 years, market pay rates for similar roles, the complexity of the role, expected time to productivity… and on. Hiring strategies in the absence of this data are going to be much less effective than they would otherwise be. The reality is that HR has been somewhat late to the party around the use of data and people analytics. If we think about this from a simple business accountability perspective HR teams are custodians of lots of systems. Not many organizations have just one system. And even if you have just one, we still have to curate and care for that information. It's a rich asset to the organization. Putting data in the hands of managers is critical for creating a data-driven culture Let’s look at some research from the Annual HR Systems survey. This survey provides a rich set of longitudinal research and here I’ve highlighted some insights they developed regarding the differences between organizations that are data-driven compared to those that are less so. This construct is similar to the Deloitte research we talked about earlier. The bars on the left of the chart are the results for organizations that are described as not being data-driven, and bars on the right are those that are identified as being data-driven. As you would expect all segments on the right-hand side are higher than on the left, but by far the biggest difference and the thing that really stands out as being different is the deployment of information to managers, putting information in the hands of decision makers. I have circled this segment in red on the chart. So, this gives us something to think about in terms of what drives success. Success isn't necessarily having a great dashboard, success is determined by whether or not people are using data and making decisions with it. The “maturity” of people analytics There has been a lot written on this topic across the decades, there are more books and research papers than you can imagine. Just a few examples here. This is extremely well-trodden terrain and there is no shortage of great information to draw from. Facilitating the Utilization of HR Metrics – The Next HR Measurement Challenge; Irmer, Bernd E (Ph.D); Ellerby, Anastasia (MBA); Blannin, Heather, 2004 Early research on driving the adoption and use of people data In terms of the topic of adoption, this is a key theme for this discussion, and the focus is on the actual use of data in organizations. The image above is an extract from a paper published around 2004 by the InfoHRM company in partnership with the Corporate Leadership Council within the Corporate Executive Board (subsequently acquired by Gartner). This research identified the key phases of sophistication around the use of HR data for business impact. The phases were characterized as: getting your house in order by automating reporting and reducing the load from ad-hoc queries by introducing self-service starting to use more advanced metrics and multidimensional analysis, and then deploying more broadly into everyday decision-making and impacting business outcomes. Through a detailed survey and interview process companies self-identified into one of these categories regarding the maturity of their use of HR data. There was a big difference in what was required of companies in phase three and we will talk more about this. Facilitating the Utilization of HR Metrics – The Next HR Measurement Challenge; Irmer, Bernd E (Ph.D); Ellerby, Anastasia (MBA); Blannin, Heather, 2006 Two years later, the study was re-run and the framework was updated based on more findings and longitudinal data. There was an even stronger focus on understanding how to drive adoption and found that there was the dotted line after phase two was even more pronounced and that it was a really hard barrier for companies to jump across. The research provides a number of tactics and best practice advice to address this, but it was clear that having the technology to help scale, automate, improve quality etc. is necessary, but not sufficient for success and success takes good change management, cultural alignment and business impact orientation. It is the latter topics that also drove the creation of the additional phase, i.e. those companies that were truly having an impact on business outcomes through the use of HR data. This research was happening at a time when HR itself was heavily focused on the prevailing thought leadership of Dave Ulrich around HR and business alignment and other leading work by Mark Huselid, Brian Becker, Richard Beatty, John Boudreau, and others. A big part of being a business partner and a business driver was the use of data and evidence-based decision making. Maturity models enter the mainstream Interestingly, around a similar timeframe, Gartner was building its own model for how companies could be more data-driven, and the use of business analytics across an organization [the earliest reference to this I can find is from 2009]. Gartner’s framework described this in the form of a four-phase model describing increasing difficulty for companies to move from descriptive analytics up to being able to deliver prescriptive analytics for the highest value. Bersin & Associates (since acquired by Deloitte) published this model around 2010. As you can see lots of similarities to what has come before and presents a maturity scale of using people analytics starting from operational reporting through advanced reporting, advanced analytics and up to predictive analytics. Defining success While these models have helped companies and people analytics teams assess where they are and the opportunities to make more of a difference, I have a problem with all of these models. The problem is that they set up prescriptive or predictive analytics as the main destination everyone should be striving for and if you're not doing predictive analytics, then you're not really doing anything of worth and setting up an expectation that is hard to reach and not necessarily the right destination. Something I'd recommend considering is how you see success and what matters to your organization is the most important thing, how you get there is just a part of the journey. Build a sustainable capability and avoid the key person dependency risk So, what do we need to do? Many of you, myself included, may have had a role that could be characterized as “the Excel ninja” in your organization, or HR team. You are able to crunch through data, get data from lots of places, massage it, put it together, create some amazing reports and dashboards, and share them around. But then if someone wanted to see that data cut differently, that became a pile of work and maybe your weekend. This is all great for job security and feeling important and needed, but before long you get bored or burnt out, or both. And then you leave or move on to another role. You may have written some great handover notes, but there is an immense amount of tacit knowledge locked up in your brain and everyone likes to do things their own way, so the next person would invariably reinvent everything. In between times, it is probably hard to fill the role, because people with the right skills are scarce in HR. Basically, relying on the Excel Ninja isn’t a great idea for any company as at some point all their people's analytics capability is going to walk out the door and they have to start again. Data Scientists are amazing, but you need to build a broader people analytics team The lesson there is around building sustainable capability, not just relying on a single person. Now, get ready for a feeling of déjà vu. We are in a very similar position today with the role of the data scientist. Everyone wants to be at the top of the maturity scale right? So, how to get there, just hire a Data Scientist! But, you are actually creating a much worse problem than you had with the Excel Ninja. The data scientist is definitely a superhero and is able to do amazing things. But, as before, if you rely on only one person, you're at risk of not creating a sustainable capability for your organization. It is compounded here too, because 80% of the time the Data Scientist is cleaning and aligning datasets and curating predictive models. Most of the time this work is not repeatable and is designed for specific investigations, which can result in great insights, but pretty soon they get fed up and move on and you are left with a massive hole in your people analytics capability yet again. https://www2.deloitte.com/us/en/pages/human-capital/articles/people-analytics-and-workforce-outcomes.html Deloitte has been doing some nice work around evolving this thinking to be more focused on capability creation, as opposed to an escalating pathway of sophistication. Peter Howes webinar discussing this and other related topics: https://www.onemodel.co/events/peter-workforce-planning-webinar Reviewing all of this material I was reminded of a webinar I hosted in 2019 with Peter Howes. As many of you know, Peter is a giant in the industry. He was the founder of Infohrm and a pioneer in strategic HR, and HR systems, a speaker, educator, and author - a true thought leader in every sense of the term. Peter created this model in around 1980. His core principles are all still valid today and remain probably one of the best characterizations of people analytics done well that I have seen. Essentially, if your team is wrapped up in administrative tasks, you should aim to shift the mix to include professional and strategic activities for greater business impact. You still need to do operational and tactical reporting, that never goes away. Getting greater efficiency and automation for these activities frees you up to do work with greater business impact. The biggest challenges with People Analytics It is pretty clear that the challenges for adoption of people analytics have been around for a couple of decades now, and while technology has caught up with our desires, there is a lot more to success in harnessing technology and developing a sustainable people analytics capability. 1. Data is spread across multiple systems Even if your company has purchased an HRIS suite, you will still have issues pulling data together from across those different applications and invariably you will also have data in lots of different systems. You spend 80% of your time assembling data and probably no more than 5% of your time doing true insight generation. 2. Data is not trusted by leaders If someone doesn't like the message they are hearing from HR, they're going to attack the data. If you have any data quality issues, then that's going to show and it will undermine everything. Even if the inaccuracy is minor and doesn’t affect your message, it is an opening - a weakness. People will start generating their own data, and use different definitions, resulting in a lack of consistency and trust. 3. Analytical tools are not being adopted If your tools are too complex, then they won't be used. This is why many tools don't get used in most organizations, not just people analytics products. There are too many options, too many things to click, and that is a barrier to adoption. Focusing the solution on the real needs of the different users and personas is critical. More is not better in this case, focused insights and fewer options for the end-user is what will bring success. 4. Data security & privacy is really complex Obviously, in HR, security and privacy are critically important, and often a major reason why people data is not shared around organizations. Think back to the life of the Excel Ninja, they are probably generating hundreds of different spreadsheets and emailing them to managers. This is a lot of work, but it is also inherently risky. 5. High expectations for Data Science and AI/Machine Learning Machine Learning (ML) and Artificial Intelligence (AI) is seen as being too futuristic for most despite the incredible amount of hype. “How do we even get started?” is an all too common refrain. 6. Data and predictive models in HR apps are very “black box” Predictive models and even basic data transformation models are often locked in the head of your Excel Ninja, or in a black box from your software vendor. This means you have a lack of transparency in understanding if there are any quality issues in the movement of data and calculations, or if you have an inherent bias, or how reliable and trustworthy those models are. Back to where we began this discussion, if you are making decisions that impact people’s lives, you need to have good reliable evidence. Solving these challenges is necessary for success. So how do we actually do that? Let's talk through some tactics and ideas. Solving the People Analytics challenges STEP ONE - Bring your data together Naturally, bringing your data together is step one. Ideally, into a single data model, or if not, at least a repeatable process for merging your data together so you don't have hands involved in the process. This is really important, because if you have any manual processes you are again spending time on less value adding work taking you away from insight generation, and it's opening up opportunities for errors. STEP TWO - Create a set of key metrics and definitions Creating a set of metrics and a set of definitions is really important, because then you've got consistency. You can then drive reliability and quality through that process. STEP THREE - Deploy simple, guided storyboards/dashboards & data exploration tools Then with a set of defined metrics and storyboards (or dashboards, or whatever you call it) that are consistent and easily understood, you are able to start driving adoption. People get familiar with the frame you are presenting, the terms and the language, and the definitions. This brings a baseline of shared understanding and learning and the ability to then start adding to that through time. STEP FOUR - Wrap everything in role based security from the start In terms of security, you should think about security through the concept of user personas for which you construct roles, not thinking about security for individuals. Think about your Executives, GMs, People Leaders, HR Business Partners (HRBPs) etc. and what the different roles are, what data they need to see and then craft the security around the roles. This allows you to set your data free using security as a way of deploying content, not holding it back. Drowning in spreadsheets is often seen as a problem for data consistency, effort etc. but it is also a major security issue that can be avoided by taking this approach STEP FIVE - Leverage technology and skills to enable the use of ML/AI & predictive insights The technology issues around ML/AI are completely solvable. There is lots of technology available and it is not really a technology problem anymore. It is more an issue of capability and understanding. The key is to leverage technology in a scalable way and not fall into the key person dependency trap. STEP SIX - No magic allowed - make everything fully transparent & explainable This leads into the last point, which is don't allow the use of black magic and closed systems. Make sure that everything is explainable and understandable when it comes to metrics and predictive models or whatever kind of analysis that you're doing. Some practical examples of People Analytics in practice Let me share a couple of quick examples. Here is a storyboard that is structured around a specific topic and has the key questions your audience would be asking and these leading them through the data. So, it’s really easy to understand what's going on with layered complexity from the high level summary trends through to the details. Everything is interactive, you can click and drill. We are leading people through the topic pre-empting the questions that commonly arise when consuming this content. Below is an example using more of a classic KPI style Storyboard. Here you can assemble and browse through the KPIs from the simple to the more advanced, but the layout is consistent and easy to track from the big headline number through the trends and the detailed breakdowns. At any point, you can click and drill. One of the most important features here is this pervasive library of formulas, definitions and explanations. As important is the ability to drill into the details and see who the people are for this analysis (naturally all this is seamlessly controlled by role based security). The ability to drill down lets you validate the information, but also gets you into action. You are able to quickly dig into key employee segments, identify risks and target interventions. These are just a couple of these examples of what you can do to get started fairly simply, but quickly make a big difference in your organization. Building on the previous examples, in the scatterplot below we have added a correlation, which is normally something scary for the average non-statistician, but if you look at the text above the chart you can see an automatically generated written interpretation of the results using simple business language. Instead of just providing the numbers and expecting people to understand what a correlation coefficient is, or how to interpret significance, be explicit and explain whether something is significant or not – this goes a long way. Here is a zoomed in view so you can see this more clearly - the chart heading is in the form of a question and the text is directly answering this question. A Summary of Tactics to Build your People Analytics Capability The slide above summarizes some of the tactics we have covered, with a few additions to help you build a people analytics capability in your organization. If you don’t have the skills in HR, borrow from other disciplines, find the experts in the organization who can help you. Reach out to the broader people analytics community. There are lots of resources, networks and people ready to help. The People Analytics practice and network is bigger now than it has ever been. Also remember that it's not always just about the data, you're in HR, let's talk to people, be sure to check your findings, go around the organization and build your own network to better understand what's actually happening. Some final thoughts By way of some final thoughts. Focus on the questions that matter to your business, start with a small set of things that are repeatable and build trust. This will then give you time to do the more interesting stuff, find opportunities to drive success, and then market your successes. You can build a groundswell of people wanting to get analytics as opposed to you forcing it upon them. And again, it's about insight, not necessarily just about the data, but the actions you can take and the impact you can make. People Analytics is one of the hottest areas that organizations are looking to hire into internationally. The above framework is designed to help you put all this into practice. You need to deal with the job of data orchestration to get all of your data into one place and one logical construct. Focus on Storytelling, not just generating Dashboards. Blend Predictive Analytics into this and some think of it as an add-on. Answer the questions that matter If you are interested, One Model has heaps of assets that we can share with you. For example, contact us if you want some inspiration around the questions that matter. We have a great library of these and this is a really engaging way to talk to people in your organization about people analytics in a non-technical way. We also have an e-book titled "Explore the Power of People Analytics" that’s a great resource to get started.
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15 min read
Chris Butler
The public sector is rapidly evolving, is your people analytics strategy fit for purpose and can it meet the increasing demands of a modern public sector? In this blog, we will highlight the unique challenges that public sector stakeholders face when implementing a people analytics strategy. In light of those challenges, we will then outline how to best design and implement a modern people analytics strategy in the public service. When it comes to people analytics, the public sector faces a number of unique challenges; The public sector is the largest and most complex workforce of any employer in Australia. A workforce that bridges everything from white collar professionals to front line staff and every police officer, teacher and social worker in between. Public sector workforces are geographically dispersed with operations across multiple capital cities in the case of the Commonwealth Government, or a mix of city and regional staff in the case of both state and federal governments. The public service operates a multitude of HR systems acquired over a long time, leading to challenges of data access and interoperability. Important public service HR data may also be held in manual non-automated spreadsheets prone to error and security risk. A complex industrial relations and entitlements framework, details of which are generally held in different datasets. Constant machinery of government (MoG) changes demand both organisational and technological agility by public servants to keep delivering key services (as well as the delivery of ongoing and accurate HR reporting). The public sector faces increased competition for talent, both within the public service and externally with the private sector. Citizen and political pressure for new services and methods of government service provision is at an all time high - so not only are your critical stakeholders your customers, they are your voters as well. Cyber security and accessibility issues that are unique to the public sector. This all comes under the pressure of constant cost constraints that require bureaucracies to do more with limited budgets. As a result - understanding and best utilising limited human capital resources is crucial for the public sector at both a state and federal level. Now that we have isolated the unique people analytics challenges of the public sector, how do HR professionals within the public service begin the process of implementing a people analytics strategy? 1. Data Orchestration “Bringing all of your HR data together.” The first stage of any successful people analytics programme is data orchestration, without having access to all of your relevant people data feeds in one place, it is almost impossible to develop a universal perspective of your workforce. Having a unified analytical environment is critical as it allows HR to; Develop a single source of truth for the data you hold on employees. Cross reference employee data within and between departments to adequately benchmark and compare workforces to drive team-level, department-level and public service wide insights. Establish targeted interventions and not one-size-fits-all solutions. For example, a contact centre is going to have very different metric results than your corporate groups like Finance or Legal. Blend data between systems to uncover previously hidden insights. Uncover issues such as underpayments that develop when different systems don’t communicate. Using people analytics to mitigate instances of underpayment is covered extensively in this blog. Provide a clean and organised HR data foundation from which to generate predictive insights. Have the capacity to export modelled data to an enterprise data warehouse or another analytical environment (PowerBI, Tableau etc). Allow HR via people analytics to support the Enterprise data mesh - covered in more detail in this blog post. People data orchestration in the public sector is complicated by the reliance on legacy systems, as well as the constant changes in structure driven by machinery of government reforms. Successful data orchestration can only be achieved through an intimate knowledge of the source HR systems and a demonstrated capacity to extract information from those systems and then model that information in a unified environment. This takes significant technology knowledge, such as bespoke API integrations for cloud based systems and proven experience working with on premise systems. It also requires subject matter expertise in the nuances of HR data. It can not be easily implemented without the right partners. Ideally, the end solution should be a fully flexible open analytics infrastructure to future proof the public sector and allow for the ingestion of data from new people data systems as they arise (such as new LMS or pulse survey products) while also facilitating the migration of data from legacy systems to more modern cloud based platforms. 2. Data Governance “Establishing the framework to manage your data.” Now that all of your data is in one place, it is important you develop a robust framework for how to manage that data - in our view this has two parts - data definition and data access. Data Definition Having consolidated multiple sources of data in one environment, the next step is metric definition, which is critical to being able to convert the disparate data sets that you have assembled into coherent, understandable language. It is all well and good to have your data in one place, but if you have 5 different definitions of what an FTE means from the five different systems you are aggregating then the benefits you receive from your data orchestration phase will be marginal. Comprehensive metric definitions with clear explanations are needed to ensure your data is properly orchestrated and organisation-wide stakeholders have confidence that data is standardised and can be trusted. Data Access HR data is some of the most complex and sensitive a government holds, so existing HR data management practices based on spreadsheets that can be easily distributed to non-approved stakeholders both inside and outside of your organisation are no longer fit for purpose. Since your people analytics data is coming from multiple systems you need to provide an overarching security framework that controls who gets access to what information and why. This framework must based on logical rules, aligned to broader departmental privacy policies and flexible enough to accommodate organisational change and to scale to your entire department or agency regardless of its size. Critically, there needs to be a high level of automation and scalability to use role based security as a mechanism for safely sharing data to decision makers. Today’s spreadsheet based world relies on limiting data sharing, which also limits effective data driven decision making. Finally, these role based security access frameworks need to be scalable so each new user or change in structure doesn’t require days of manual work from your team to ensure both access and compliance. 3. Secure People Analytics Distribution “Delivering people analytics content to your internal stakeholders.” The next step, once you have consolidated your data and established an appropriate data governance framework, is to present and distribute this data to your internal stakeholders. This is what we refer to as the distribution phase of your people analytics implementation. We established in the last section that for privacy and security reasons, different stakeholders require access to varying levels of information. The distribution phase goes one step further and places access within the prism of what individual stakeholders need in order to successfully do their jobs. For example, the information and insights necessary for a Departmental Secretary and a HR business partner to do their jobs are wildly different and therefore should be tailored to their particular needs. So, organisation wide metrics and reports in the case of the Departmental Secretary and team or individual level metrics for the HR BP or line manager. This is further complicated by disclosure requirements and reporting unique to the public service. This includes; Media requests regarding public servant pay and conditions Statutory reporting requirements for annual state of the public service reports Submissions to and appearances before parliamentary committees Disclosure to independent oversight inquiries or agencies As a result, public sector HR leaders are required to walk a tightrope of both breadth and specificity. So how do we recommend you do this? Offer a baseline of standardised metrics for the whole organisation. Tailor that baseline based on role-based access requirements, so stakeholders only see information that is relevant to drive data driven decision making. Deliver those insights at scale - the wider the stakeholder group consuming your outputs the better. Ensure those outputs are timely and relevant - daily or weekly updates are recommended. Be able to justify your insights and offer access to raw data, calculations and metric definitions. Continually educate your stakeholders about best practice people analytics. Increase reporting sophistication based on the people analytics maturity of your stakeholders - simple reporting for entry level stakeholders, more complicated predictive insights for the more advanced. To get the most out of your people analytics strategy you need to deliver two things; Role based access to the widest stakeholder group across your department, the wider the group of employees that have access to detailed datasets the easier it will be to deliver data driven decision making. Support your team with a change management programme to grow their analytical capability over the course of time. 4. Extracting Value from your Data “Using AI + Data Science to generate predictive insights.” Now we get to the fun part - using data science to supercharge your analysis and generate predictive insights. However, to quote the great theologian and people analytics pioneer - Spiderman - “With great power comes great responsibility.” Most data science work today is performed by a very small number of people using arcane knowledge and coding in technologies like R or Python. It is not scalable and rarely shared. The use of machine learning capabilities with people data requires a thoughtful approach that considers the following; Does your AI explain its decisions? Could the decisions your machine learning environment recommends withstand the scrutiny of a parliamentary committee? Do you adhere to ethical AI frameworks and decision making? What effort has been made to detect and remove bias? Does harnessing predictive insights require a data scientist or can it be used by everyday stakeholders within your department? Will your use of AI adhere to current or future standards, such as those recently proposed by the European Commission? To learn more about the European Commission proposal regarding new rules for AI, click here. In integrating the use of machine learning into your people analytics programme, you must ensure that models are transparent and can be explained to both your internal and external stakeholders. 5. Using People Analytics to Support Public Sector Reform “Public sector HR driving data-driven decision making.” A people analytics strategy does not exist in isolation, it is a crucial aspect of any departmental strategy. However, in speaking to our public sector HR colleagues - they often feel that their priorities are sidelined or they don’t have the resources to argue for their importance. A lot of this has to do with the absence of integrated datasets and outputs to justify HR prioritisation and investment. We see people analytics and the successful aggregation of disparate data sets as the way that HR can drive their people priorities forward. If HR can present an integrated and trusted dataset, that allows comparison and cross validation with data from other verticals including finance, community engagement, procurement and IT. This gives HR the capability to be central to overall decision making and support broader departmental corporate strategies from the ground up. We have written extensively about the importance of data driven decision making in HR and using people analytics to support enterprise strategy, this content can be found on our blog here - www.onemodel.co/blog Why you should invest in people analytics and what One Model can do to help. The framework of a successful public sector people analytics project outlined above is the capability that the One Model platform delivers. From data orchestration to predictive insights, One Model delivers a complete HR Analytics Capability. The better you understand your workforce, the more ambitious the reform agendas you can fulfil. One Model is set up to not only orchestrate your data to help the public service understand the challenges of today, but through our proprietary OneAI platform - to help you build the public service of the future. One Model’s public sector clients are some of our most innovative and pragmatic, we love working with them. At One Model, we are constantly engaging with the public sector about best practice people analytics - last year, our Chief Product Officer - Tony Ashton (https://www.linkedin.com/in/tony-ashton/) - himself a former Commonwealth HR public servant appeared on the NSW Public Service Commission’s The Spark podcast to discuss how the public sector can use people data to make better workforce decisions. That podcast can be found here. Let’s start a conversation If you work in a public service department or agency and are interested in learning more about how the One Model solution can help you get the most out of your workforce, my email is patrick.mcgrath@onemodel.co
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4 min read
Nicholas Garbis
Our team recently published a whitepaper which explains the "how and why" of our approach to getting data out of Workday. In it we share a lot of challenges and a heap of technical detail regarding our approach. There are also a couple of embedded videos within the paper (unless you print it!). We produced this whitepaper to share the knowledge and experiences we have gained working with our customers, many of whom have Workday as their core HCM. With these customers, we use our proprietary 'connectors' to extract the relevant data through Workday's APIs (adding in data from RaaS reports where needed). But that is just the beginning, because, while the extraction is critical, what comes out of it is essentially 'dull data' that lacks analytical value in its pre-modeled state. We don't stop there. One Model's unique expertise kicks in at this point, converting the volumes of data from Workday (and other HR and non-HR systems) it into what we like to call an "analytics-ready data asset". So, that begs the questions, "What exactly is an 'analytics-ready data asset'?" and "How does One Model create this data asset from Workday data?" So, here's a definition ... DEFINITION of an "Analytics-Ready Data Asset" A structured set of data, purpose built to support a variety of analytics deliverables, including: Metrics that are pre-calculated, can be updated centrally, and have relevant metadata Queries that can range from simple to complex Reports that contain data in table format (rows and columns) with calculations Dashboards and Storyboards that deliver data in compelling visuals that accelerate insights Data science such as predictive modeling, statistical significance testing, forecasts, etc. Integration of data from multiple sources (HR and non-HR) leveraging the effective-dated data structure Data feeds that can be set up to supply specific data to other systems (eg, data lakes) Security model that enables controls over who can see which parts of the organization AND which data fields they will see (some of them at summary, others at employee-level detail) One of the key elements of building such a data asset from Workday is the conversion of the source data into an effective-dated structure which will support views that trend over time (without losing data or creating conflicting data points). This is much more difficult than you'd expect, given that we are conditioned to think of HR data as representative of the employee lifecycle, and many systems of the past were architected with that in mind. This is not a knock on Workday -- not at all -- it's a great HCM solution that has transformed the HR tech industry with it's focus on manager and employee experience. They are not a huge success story on accident! However, delivering a great experience in a transactional HR system does not directly translate into an analytics capability that is powerful enough to support the people analytics needs of companies today (and for the future). To accelerate your people analytics journey, and to ensure you don't run out of runway, you need a solution like One Model to bring your Workday data to life. Download the whitepaper to get the full story. Go to www.onemodel.co/workday ABOUT ONE MODEL One Model’s industry-leading, enterprise-scale people analytics platform is a comprehensive solution for business and HR leaders that integrates data from HR systems with financial and operational data to deliver metrics, storyboard visuals, and predictive analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which One Model simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co
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1 min read
Chris Butler
One Model has announced its appointment to the Australian Government’s Digital Transformation Agency Cloud Marketplace, a digital sourcing arrangement of cloud computing offerings for Australian government. One Model’s globally recognised and award-winning People Analytics platform, is now available via the Cloud Marketplace to all Australian federal, state, and territory government agencies seeking to reimagine and accelerate their People Analytics journey. One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates, models and unifies data from the myriad of HR technology solutions through the out-of-the-box metric library, storyboard visuals, and advanced analytics using a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Many public sector departments and organisations around the world realised the power of One Model and selected One Model as their partner to success, including the Australian Department of Health, the Australian Civil Aviation Safety Authority (CASA), and Tabcorp to name just a few. The Cloud Marketplace can be accessed via the DTA’s BuyICT platform.
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6 min read
Nicholas Garbis
WATCH THE VIDEO! Conversation with our Chief Product Officer, Tony Ashton, on the topic of insight generation and he shows how One Model’s new insight function works. Insight Generation I believe that a key element of People Analytics should be on insight generation, reducing the time and cognitive load for HR and business leaders to generate insights that lead to actions. Many people analytics teams have made this a priority from a service offering, some of them even including "insights" in the naming of their team. With artificial intelligence, higher quality and faster insight generation can be driven across an organization. An organization with a mature people analytics capability should be judged on the frequency and quality of insight generation away from the center. Why I Stopped Liking Maturity Models Humor me for a moment while I share a very short rant and a confession. I have grown to despise the “maturity curves” that have been circulating through people analytics for over a decade. My confession is that I have not (yet!) been able to come up with a compelling replacement. My main issues? The focus is on data & technology deliverables, not on actions and outcomes. They are vague and imply that you proceed from one stage to the next, when in reality all of them can (and should) be constantly maturing and evolving without any of them ever being “done” or “perfect.” Too many times I have heard (mostly newer) people analytics leaders saying that they need to get their data and basic reporting right before they can consider any analytics. I personally don’t believe that to be true -- things will get easier, faster, and better with your analytics but you do not have to wait to make progress at any of the stages. Action Orientation For example, getting to “predictive” -- being able to foresee what is likely to happen -- is shown in many maturity models. It is easy to imagine, and you may have examples, where very mature predictive analytics deliverables have had little or no impact on the business. In my opinion, true maturity is not about the deliverable, but about the insights generated and the corresponding actions that are taken to drive business outcomes. Going further, getting to “prescriptive” means you have a level of embedded, artificial intelligence that is producing common language actions that should be considered. This would assume the “insight” component is completely handled by the AI which then proceeds into selecting or creating a recommended action. This is still quite aspirational for nearly all organizations, yet it is repeated often. Focus on Designing for Insight Generation at the “Edges” People analytics teams are typically centralized in a COE model, where expertise on workforce data, analytics, dashboard design, data science, insight generation, and data storytelling can be concentrated and developed. The COE is capable of generating insights for the CHRO and HR leadership team, but what about the rest of the organization? What about the HR leaders and managers farther out at the edges of the org chart? The COE needs to design and deliver content to the edges of the organization that enable them to generate insights without needing to directly engage the COE in the process. A storyboard or dashboard needs to be designed with specific intention to shorten the time between a user seeing the content and them having an accurate insight. A good design will increase the likelihood of a “lightbulb" moment. Humans and Machines Turning on “Lightbulbs” Together We need to ensure that the HR leaders and line managers are capable of generating insights from the people analytics deliverables (reports, dashboards, storyboards, etc). This will require some upskilling in data interpretation and data storytelling. With well-designed content, they will generate insights faster and with less effort. Human-generated insights will never be fully replaced. Instead, they will be augmented with machines in the form of AI and machine learning. With the augmentation of AI, the humans will get a boost and together the human-machine combination is a powerful force for insights and then actions. When we have augmentation of AI, we can stop trying to teach everyone statistical regression techniques which they will never use. The central PA team can manage the AI toolset and ensure it is delivering valid interpretations and then focus on enabling insight generation and storytelling by the humans, the HR leaders and line managers. One Model Lights Up Our Customers’ Data Visualizations One Model has just introduced a “lightbulb” feature that is automatically enabled on storyboard tiles that contain metrics that would benefit from forecasting or statistical significance tests. This is not just limited to the content our team creates, it is also automatically scanning the data within storyboards created by our customers. This is far more than basic language attached to a simple regression model. By integrating features of our One AI machine learning module into the user interface we are automatically interpreting the type & structure of the data in the visual and then selecting the appropriate statistical model for determining if there is a meaningful relationship which is described in easy-to-interpret language. Where a forecast is available it is based on an ARIMA model and all the relevant supporting data is just a click away. With this functionality built directly into the user interface, each time you navigate into the data, filtering or drilling into an organization structure, the calculations will automatically reassess the data and generate the interpretations for you. With automated insights generated through AI, One Model accelerates your people analytics journey, moving you from data to insights to actions. About One Model One Model’s industry-leading, enterprise-scale people analytics platform is a comprehensive solution for business and HR leaders that integrates data from HR systems with financial and operational data to deliver metrics, storyboard visuals, and predictive analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which One Model simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co One Model’s new Labor Market Intel product delivers external supply & demand data at an unmatched level of granularity and flexibility. The views in LMI help you to answer the questions you and your leaders need answers to with the added flexibility to create your own customized views. Learn more at www.onemodel.co/LMI
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3 min read
Nicholas Garbis
There are whole books written about Workforce Planning. I read them and enjoy them (maybe even more than I would like to admit). I will include a short list below for your reference. So, what can be added to the body of thought leadership on this topic? My former SWP colleague, Phil Mische and I got together (in person!) to discuss some elements of SWP and decided to create a video hitting on a handful of topics in rapid succession. We decided to call it "Lightning Round Learning." As background, Phil and I worked together on successfully designing and implementing SWP at scale at a global financial services firm. It was an intense experience but was the greatest test of everything that I had wanted SWP to be. Nothing is perfection, especially in year one of a multi-year journey, but it was world-class SWP and we each have an abundance of learnings to share. We listed out several topic ideas and then selected these in real-time, then hit each of them for a few minutes each: Operationalization of SWP Technology (data, tools, models) Granularity of Skills data Change Management Strategic v. Operational workforce planning There is way more depth on each of these elements -- we could easily have filled most of a day unloading our experiences -- and there are many other elements of SWP that we didn't cover. So, sit back and check out the video below. Then do these 2 things: Schedule time to chat with me on SWP, People Analytics, or One Model more generally. https://meetings.hubspot.com/nicholas-garbis Let me know what SWP elements you think we should cover in Part 2! You can message me here or post your comment to LinkedIn here. SCHEDULE TIME TO CHAT! Workforce Planning Book List: Agile Workforce Planning, by Adam Gibson Strategic Workforce Planning, by Ross Sparkman Strategic Workforce Planning: Guidance and Back-Up Plans, by Tracey Smith ... and one day I may write an SWP book to add to this list! .... :)
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14 min read
Chris Butler
If people analytics teams are going to control their own destiny they're going to need to need to support the enterprise data strategy. You see, the enterprise data landscape is changing and IT has heard its internal customers. You want to use your own tools, your own people, and apply your hard won domain knowledge in the way that you know is effective. Where IT used to fight against resources moving out of their direct control they have come to understand it's a battle not worth fighting and in facilitating subject matter experts to do their thing they allow business units to be effective and productive. Enter the Enterprise Data Architecture The movement of recent years is for IT to facilitate an enterprise data mesh into their architecture where domain expert teams can build, consume, and drive analysis of data in their own function...so long as you can adhere to some standards, and you can share your data across the enterprise. For a primer on this trend and the subject take read of this article Data Mesh - Rethinking Enterprise Data Architecture The diagram heading this blog shows a simplified view of a data mesh, we'll focus on the people analytics team's role in this framework. What is a Data Mesh? A data mesh is a shared interconnection of data sets that is accessible by different domain expert teams. Each domain team manages their data applying its specific knowledge to its construction so it is ready for analytics, insight, and sharing across the business. When data is built to a set of shared principles and standards across the business it becomes possible for any team to reach across to another domain and incorporate that data set into their own analysis and content. Take for example a people analytics team looking to analyze relationships between customer feedback and front-line employees' attributes and experience. Alternatively, a sales analytics team may be looking at the connection between learning and development courses and account executive performance, reaching across into the people analytics domain data set. Data Sharing becomes key in the data mesh architecture and it's why you've seen companies like Snowflake do so well and incumbents like AWS bring new features to market to create cross-data cluster sharing. There are two ways to share data across the enterprise: Cross Cluster / Data Warehouse sharing - each domain operates its own schemas or larger infrastructure for allowing other business units to access. AWS has an example here https://aws.amazon.com/redshift/features/data-sharing/ Feeding domain Analytics-Ready data into a centralized enterprise data architecture - This is more typical today and in particular is useful if the organization has a data lake strategy. Data lakes are generally unstructured and more of a data swamp, in order to be useful the data needs to be structured, so providing Analytics Ready data into either a data lake or data warehouse that adheres to common principles and concepts is a much more useable method of sharing value across data consumers. One Model was strategically built to support your HR data architecture. If you'd love to learn more, check out our people analytics enterprise products and our data mesh product. How can people analytics teams leverage and support the HR data architecture? The trend to the mesh is growing and you're going to be receiving support to build your people analytics practice in your own way. If you're still building the case for your own managed infrastructure then use these points for helping others see the light and how you are going to support their needs. Identify the enterprise data strategy I'm sure you've butted heads against this already but identify if the organization is supportive of a mesh architecture or you'll have to gear up to show your internal teams how you will give them what they need while taking away some of their problems. If they're running centralized or in a well-defined mesh, you will have different conversations to obtain or improve your autonomy. Supporting the enterprise data mesh strategy People analytics teams are going to be asked to contribute to the enterprise data strategy if you are not today. There are a number of key elements you'll need to be able to do this. Extract and orchestrate the feeds from your domain source systems. Individual systems will have their nuances that your team will understand that others in the enterprise won't. A good example is supervisor relationships that change over time and how they are stored and used in your HRIS. Produce and maintain clean feeds of Analytics-Ready data to the enterprise. This may be to a centralized data store or the sharing of your domain infrastructure across the business. Adhere to any centralized standards for data architecture, this may differ based on the tooling used to consume data. Data architected for consumption by Tableau is typically different (de-normalized) from a model architected for higher extensibility and maintenance (normalized) which would allow for additional data to be integrated and new analyses to be created without re-architecting your core data tables. You can still build your own nuanced data set and combinations for your domain purpose but certain parts of the feed may need to follow a common standard to enable easy interpretation and use across the enterprise. Define data, metrics, and attributes and their governance ideally down to the source and calculation level and document for your reference and for other business units to better understand and leverage your data. The larger your system landscape is the harder this will be to do manually. Connect with other domain teams to understand their data catalogues and how you may use them in your own processes. Why should people analytics care? This trend to the data mesh is ongoing, we've seen it for a number of years and heard how IT thinks about solving the HR data problem. The people analytics function is the domain expertise team for HR, our job is to deliver insight to the organization but we are the stewards of people data for our legacy, current, and future systems. To do our jobs properly we need to take a bigger picture view of how we manage this data for the greater good of the organization. In most cases, IT is happy to hand the problem off to someone else whether that's an internal team specialized in the domain or an external vendor who can facilitate How does One Model support the Data Mesh Architecture for HR It won't surprise you to hear but we know a lot about this subject because this is what we do. Our core purpose has been understanding and orchestrating people data across the HR Tech landscape and beyond. We built for a maturing customer that needed greater access to their data, the capability to use their own tools, and to feed their clean data to other destinations like the enterprise data infrastructure and to external vendors. I cover below a few ways in which we achieve this or you can watch the video at the end of the article. Fault Tolerant Data Extraction Off the shelf integration products and the front end tools in most HRIS systems don't cater for the data nuances, scale of extraction, or maintenance activities of the source system. Workday for example provides snapshot style data at a point in time and it's extraction capabilities quickly bog down for medium and large enterprises. The result is that it is very difficult to extract a full transactional history to support a people analytics program without arcane workarounds that give you inaccurate data feeds. We ultimately had to build a process to interrogate the Workday API about dozens of different behaviors, view the results and have the software run different extractions based on its results. Additionally most systems don't cater for Workday's weekly maintenance windows where integrations will go down. We've built integrations to overcome these native and nuance challenges for SuccessFactors, Oracle, and many other systems our customers work with. An example of a workday extraction task is below. Data Orchestration and Data Modelling Our superpower. We've built for the massive complexity that is understanding and orchestrating HR data to enable infinite extension while preserving maintainability. What's more it's transparent, customers can see how that data is processed and it's lineage and interact with the logic and data models. This is perfect for IT to understand what is being done with your data and to have confidence ultimately in the resulting Analytics-Ready Data Models. Data Destinations to the Enterprise or External Systems Your clean, connected data is in demand by other stakeholders. You need to be able to get it out and feed your stakeholders, in the process demonstrating your mastery of the people data domain. One Model facilitates this through our Data Destination's capability, which allows the creation and automated scheduling of data feeds to your people data consumers. Feeds can be created using the One Model UI in the same way as you may build a list report or an existing table and then just add it as a data destination. Host the Data Warehouse or Connect Directly to Ours We've always provided customers with the option to connect directly to our data warehouse to use their own tools like Tableau, Power BI, R, SAC, Informatica, etc. Our philosophy is one of openness and we want to meet customers where they are, so you can use the tools you need to get the job done. In addition to this a number of customers host their own AWS Redshift data warehouse that we connect to. There's capability to run data destinations to also feed to other warehouses or use external capability to sync data to other warehouses like Azure SQL, Google, Snowflake etc. A few examples Snowflake - https://community.snowflake.com/s/article/How-To-Migrate-Data-from-Amazon-Redshift-into-Snowflake Azure - https://docs.microsoft.com/en-us/azure/data-factory/connector-amazon-redshift Data Definitions and Governance With One Model all metric definitions are available for reference along with interactive explanations and drill through to the transactional detail. Data governance can be centralized with permission controls on who can edit or create their own strategic metrics which may differ from the organizational standard. HR Specific Content and Distribution We provide standard content tailored to the customers own data providing out of the box leverage for your data as you stand up your people analytics programs. Customers typically take these and create their own storyboards strategic to their needs. It's straightforward to create and distribute your own executive, HRBP, recruiting, or analysis project storyboards to a wide scale of users. All controlled by the most advanced role based security framework that ensures only the data permissioned can be seen by the user while virtually eliminating user maintenance with automated provisioning, role assignment, and contextual security logic where each user is linked to their own data point. Watch the two minute video of what One Model does
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10 min read
Phil Schrader
Thanks for stopping by the blog to check out our work on integrating Workday, Greenhouse, and Engagement Survey data. Along with a video walking through the exact insights you can get, we use this blog to dive into key considerations when combining HCM, recruiting, and engagement surveys. If you want to chat through any of the ideas here feel welcome to schedule a time on my calendar. I'd love to chat: Why We're Even Talking about Workday Greenhouse Integrations with Survey Data. We started noticing about a year ago. Ryan and I would get a cool new lead that came in from a really exciting company to talk to, often based on the West Coast, often in tech. During our initial conversation, they would talk about workforce growth, diversity, and engagement. Then we’d ask about their system mix, and they’d say, “Well, we switched to Workday a couple of years ago, but we use Greenhouse for recruiting, and we have Culture Amp for surveys (or Glint or Qualtrics).” Jump to video Ryan and I started joking about how this was happening all the time-- to the point where we’d sometimes try to autocomplete “Culture Amp” for the person after they mentioned Greenhouse. (This totally failed on a recent call so we’ll stop doing that now.) Over the winter and into the spring Ryan and I’d periodically throw some time on the calendar to talk about this batch of companies we kept running into. We’d talk about the type of storyboards and views we might put together to focus specifically on them. Then the conversation would drift over into our mutual interests like land, soil, gardening, and regenerative agriculture. Video: Insights from Greenhouse, Workday and Culture Amp Eventually we were able to get some initial versions of these ideas built out in a demo One Model site-- and felt really excited that the inspiration we were finding out among the trees (Ryan in Vancouver) and fields (me in Texas) fit really well with the story we wanted to tell about how organizations grow over time. For me personally it was just so satisfying to take the analytic side of my world and have it elevate, rather than reduce the more organic, intangible and relationship oriented lessons I learn as a parent, a cook, and a gardener. (I also play tons of Call of Duty so don’t go feeling like you have to be some sort of woodland saint to appreciate this stuff.) In the video above we introduce some of these ideas for looking at your workforce, anchoring around the idea of treating hiring cohorts as organizational growth rings. In other words, starting with data from Workday (or whatever core HR system) and grouping headcount by the year they joined the company. For example, everyone from what you might call “the hiring class of 2015”. Reviewing Your Growth Rings for Real Workday & Greenhouse BI When you lay the data out like that it’s just flat-out interesting to look at. It gives you (or me at least) a cool hybrid-style view. It makes me think of the way that people invariably slow down and pause to appreciate the growth rings you see on a cross-cut section of the tree. On one hand, you get a definite feeling of growth and movement and activity. On the other, you get a sobering perspective on long-time scales. You need this appreciation when thinking about how human beings cooperate together and change as they do the work of your organization. This second feeling is a great counterweight to the action-oriented, get-it-done-now energy that we also must bring to our work. As we looked at these growth rings, Ryan and I started to deepen our appreciation of how much human experience is represented in those layers. How much somebody who has been around for 5 or 10 years has seen and learned-- all the things about the organization that are usually intangible and difficult to measure. We thought that it was a humble and human perspective on what our analytic minds would call human capital, but what we could just call out as accumulated human experience. From the growth ring analogy, you can start to mix in other people analytics perspectives like diversity. You can see that maybe your current headcount is trending in a more diverse direction but you're going to see (and your newer hires might directly experience) a lagging effect where all that accumulated human experience takes longer to become more diverse. So much of it has already been accumulated in prior years. In fact, that gap might give you more appreciation for inclusion efforts in your workforce because you can start to visualize the gap between a diverse headcount and an organization that has grown, developed, and incorporated a diversity of experience. And then we thought, “This would be the perfect place to layer in engagement data from Glint or Culture Amp or other surveys because you could see both the engagement of your people but also get that visual sense of the engagement of all that accumulated human experience. Ryan and I felt like that really boils a lot of people analytics down into something pretty simple. If someone comes into (or logs onto) work to start the day, and they’ve got 5 or 10 or more years of experience with your company’s products, services, customers, culture, networks, systems, coworkers, etc. AND they’re engaged and eager to dive back into that work-- well then you can’t really go wrong with that. What more could you ask for? You can’t really artificially assemble that. You’ve got to grow it. If you pull together some thinking on how a resilient ecosystem handles disruption and then think about what a wild, disruptive period we’ve been going through, then you just get filled with this desire to grow a diverse, resilient workforce to match. And we also started seeing how the work that talent acquisition does can be informed by and elevated by this view. Recruiting is often seen as the fast-paced (time to fill), process-driven (time in status) side of HR. But now we have a view that emphasizes the long term consequences of that frenetic activity. And we have a view that guides us in our analysis of that data. Greenhouse and Survey Data Adds Insight from the Beginning to the End of your People's Journey. Greenhouse is both perfectly named and well designed for this type of thinking. Instead of leaving all that scorecard data (for example) behind at the point of hire, why not look back on past growth rings and ask-- what did we learn from the interview process that might help us predict if a certain candidate will really take root and become part of the deep-tissue of our organization? Did we focus too much on the immediate skills they would bring, when it turns out that communication and adaptability were the things that really mattered? And so, what resulted from all these great conversations was the beginning of some new views on people data-- woven together from Workday, Greenhouse, and Engagement Surveys. We’ve captured this thinking in the video above. Please check it out if you haven’t done so already. As a final note, think of all the questions you could answer with a Workday and Greenhouse integration with survey data like: Are our employees happy with their work-life balance? This took me less than an hour to bring the data together and build out some initial visuals. Are you asking all the right questions? Read about our People's Analytics Challenge! Don't let our communication stop here! It’s already been rewarding for me personally-- and I hope that there are many more conversations to come that grow these ideas further. If you’ve got some of those next ideas or if you’ve got some questions about the views we put together-- grab some time to chat with me here:
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11 min read
Chris Butler
This week One Model was delighted to participate with an elite group of our industry peers in the HR Tech Alliances, Virtual Collaboration Zone - Best New People Analytics Solution competition. I'm excited to share some detail on what the judges saw to justify the outcome. This wasn't an empty competition either and had some significant companies in the field. The overall scores were as below: 1st - One Model - 4.28 2nd - activ8 intelligence 4.06 3rd - Visier - 3.93 Given how proud I am of our team for winning this award, I thought I would share our presentation. Before I do that, I would like to acknowledge how far the pure play people analytics space has come in recent time. As an industry, this is something that we should celebrate as we continue on a path of innovation to deliver better products and better outcomes for our clients. People analytics is an exciting place to be as 2020 comes to its (merciful) conclusion! We'll take a quick tour through the highlights of our presentation and demonstration. Who are we? One Model provides its customers with an end to end people analytics platform that we describe as an infrastructure. We call it an infrastructure, because from the ground up - One Model is built to make everything we do open and accessible to our most important stakeholder - you the customer. Everything from our data models to our content catalogues, right down to the underlying data warehouse is transparent and accessible. One Model is not a black box. Over the last five years, we have been guided by the principle that because of One Model’s transparency and flexibility - our customers should feel as if this is a product that they built themselves. Our History For those of you who are unfamiliar with the history of One Model, the core of our team is derived from workforce analytics pioneers InfoHRM. InfoHRM was acquired by Successfactors in 2010 and subsequently SAP in 2012. During our extraordinary ride from humble Australian business to integral part of one of the world’s largest software companies - our team learned that while our solution gave low maturity users what they needed in terms of the what, why and how of measuring their workforce. Our solution remained an inflexible tool that customers outgrew as their own capabilities increased. With an increased sophistication, customers were asking new and more complicated questions and the solution simply couldn't evolve with them. Five years later and sadly, this is what we continue to see from other vendors in our space. Meeting our customers where they are on their people analytics journey and supporting them through their evolution is fundamental to the One Model platform. Be Open; Be Flexible; Don't put a ceiling on your customers capabilities. One Model takes care of the hard work of building a people analytics infrastructure. We built One Model to take care of both low maturity users, who need simple and supported content to understand the power of people analytics. At the same time, we need to deliver an experience that customers grow into and higher maturity users can leverage world-leading One AI data science and statistical engine. Furthermore, if they want to use their own tools or external data science teams - their people analytics platform should enable this - not stand in its way. One Model’s Three Pillar People Analytics Philosophy Pillar 1: Data Orchestration People data is useless if you can’t get access to it. Data orchestration is critical to a successful people analytics program. At One Model - Data Orchestration is our SUPERPOWER! Many thousands of hours have been invested by our team in bespoke integrations that overcome the native challenges of HR Tech vendors and provide full, historic and transactional extracts ready for analytics. This isn’t easy. Actually, it’s terrifyingly hard. Let’s use Workday as an example; To put it mildly, the data from their reporting engine and the basic API used to download these reports is terrible. It's merely a snapshot that doesn't provide the transactional detail required for analytics. It's also impossible to sync history as it changes over time - an important feature given the nature of HR data. You have to go to the full API to manage a complete load for analytics. We are 25,000 hours in and we're still working on changes! To power our data orchestration, we built our own Integrated Development Environment (IDE) for managing the enormous complexity of people data and to house our data modelling tools. Data quality and validation dashboards ensure we identify and continue to monitor data over time for correction. Data destinations allow us to feed data out to other places, many of our customers use this to feed data to other vendors or push data to other business units (like finance) to keep other business units up to date. Unlike garden variety Superpowers (like flying), our data orchestration capability did not develop by serendipity or luck. It developed and continues to develop by the hard work and superior skills of our team. Pillar 2: Data Presentation Most other vendors in our space exist here. They don't provide open and flexible toolsets for Data Orchestration or Value Extraction / Data Science. When we started One Model, we hadn't planned on a visualization engine at all. We thought we could leverage a Tableau, Looker, or Birst OEM embedded in our solution. After much evaluation, we just couldn't deliver the experience and capability that analyzing and reporting on HR data requires. Generic BI tools aren't able to deliver the right calculations, with the right views across time, in a fashion that allows wide distribution according to the intense security and privacy needs of HR. We had to build our own. Ultimately our vertical integration allows unique user security modelling, integration of One AI into the frontend UI, all while not limiting us to the vagaries of someone else's product. Our implicit understanding of how HR reports, analyzes, and distributes data required us to build a HR specific data visualization tool for One Model. Pillar 3: Data Science / Value Extraction - One AI I like to describe the third pillar of our people analytics philosophy as our 'Value Extraction' layer. This layer is vertically integrated on top of our data models, it allows us to apply automated machine learning, advanced statistical modelling, and to augment and extend our data with external capabilities like commute time calculations. Predictive capabilities were our first target and we needed to build unique models at scale for any customer, regardless of their data shape, size, or quality. A one size fits all algorithm that most other vendors in the HR space provide wasn't going to cut it. Enter automated machine learning - Our One AI capability will look across the entire data scope for a customer, it will introduce external context data, select it's own features, train potentially hundreds of models and permutations of those models and select the best fit. It provides a detailed explanation of the decisions it made, enough to keep any data scientist happy. The best of all these models can be scheduled and repeated so every month it could be set to re-learn, re-train, and provide an entirely different model as a fit to your changing workforce. This unbelievable capability doesn't lock out an experienced team, but invites them in should they wish to pull their own levers and make their own decisions. The One AI engine is now being brought to bear in a real time fashion in our UI tacking forecasting, bayesian what if analyses, bias detection, anomaly detection, and insight detection. We have barely scratched the surface of the capability and our vertical integration with a clean, consistent data model allows these advanced tools to work to deliver the best outcomes to customers. Labor Market Intelligence One Model has the world’s best understanding of your internal HR data set; we do wonderful things with the data you already have - but we were missing the context of the external labor market and how that impacted our customer's workforces. As a result, we have developed a proprietary Labor Market Intelligence (LMI) tool. LMI is being released in January 2021 as a standalone product providing labor market analytics to our customers. LMI retains the functionality that you love about our people analytics platform - the ability to flexibly navigate data, build your own storyboard content, and drill through to granular detail. Importantly what LMI will allow for One Model enterprise customers is the ability to link external market data to internal people data. Delivering outcomes like identifying persons paid lower than the market rate in their region, identifying employees in roles at risk of poaching due to high market demand and turnover, and helping you understand if your talent are leaving for promotions, or lateral moves. Collaboration with the HR Tech Ecosystem Finally, One Model understands the power of collaboration in the HR Tech ecosystem. We are already working with leading consultancies like Deloitte and are embedded in HCM vendors helping consume and make sense of their own data to deliver people analytics and extract value for their customers. At the end of the day, our vision is to understand the entire HR Tech ecosystem at the data layer, to help customers realize their investment in these systems, and to provide a data insurance policy as they transition between systems. Analytics is a by-product of this vision and thankfully it also pays the bills ;)
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3 min read
Nicholas Garbis
As part of a recent People Analytics course from the Future Workplace, Nicholas Garbis joined forces with course leader Heather Whiteman, PhD to co-author an eBook on People Analytics called "Explore the Power of People Analytics: A Guide for Business and HR Leaders". While the book was specifically aimed at a general HR and business leader audience, we quickly found that a number of well-accomplished People Analytics leaders were getting value out of it as well. Whereas some of the HR and business leaders may be entering this content for the first time, the more mature people analytics leaders are always searching for that same introductory content that can help them to increase understanding and adoption of their team's work. We are here to accelerate you people analytics journey. As titled, the aim of the eBook is to "explore" the topic of People Analytics. In terms of a journey, this is a guidebook that highlights various "points of interest" that make the journey interesting and worth pursuing. Download the eBook (.pdf) Explore the Power of People Analytics We hope this eBook sparks ideas for how you can apply people analytics in your organization and makes it more accessible for your teams. We invite you to start building greater capability in this area so you can take advantage of the opportunities people analytics makes possible. Paperback edition is available on Amazon.com. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co. One Model’s new Labor Market Intel product delivers external supply & demand data at an unmatched level of granularity and flexibility. The views in LMI help you to answer the questions you and your leaders need answers to with the added flexibility to create your own customized views. Learn more at www.onemodel.co/LMI.
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6 min read
Joe Grohovsky
As a result of my blogs and customer conversations, I receive a variety of interesting comments and feedback from my contacts in the People Analytics space. A common topic is that different stakeholder groups within a People Analytics project have vastly different ideas as to what is acceptable in a People Analytics tool. This often leads to disappointment, failed initiatives and wasted budget. Examples provided are that LOB (Line of Business) and general HR professionals tend to be attracted to and satisfied with "Convenience Analytics". Convenience Analytics is a term referring to simplistic, easy to digest metrics or reports. They are typically generated without much effort, often by the source system, but are limited in breadth, depth, and growth possibilities. The appeal of Convenience Analytics may be their low-cost of entry and their non-threatening nature to the decision makers that use them, but they are extremely inflexible. Significant challenges occur when Convenience Analytics are deployed to an organization expecting deep insights, growth of use-cases, or the addition of new data sources. The People Analytic and HRIS (Human Resource Information System) professionals supporting these Convenience Analytics projects ultimately suffer from a lack of long-term data quality and a capability to drive future insight that is uniquely strategic to their organization and not a pre-canned report. One Model recognizes that a properly constructed People Analytics infrastructure has a system agnostic HR Data Strategy, and this has driven our industry leading Data Orchestration capabilities. Data Orchestration is a process that takes siloed data from multiple locations, combines it, and makes it available for data analysis. One Model breaks Data Orchestration into 4 activities/phases: Data Ingestion – This phase is the process of removing data from source systems and delivering it into One Model. We take a flexible approach and accommodate strategies ranging from API extraction, to file based transfer over SFTP, to manual uploading of data through the One Model interface. Data Modeling – After data is ingested, it is combined into a single, interconnected data model that supports a broad range of analytics. Taken together with the ingestion phase these activities constitute ETL (extract, transform and load) activities. This results in what is recognized as a fact and dimension star schema style of data model. Data Quality – This phase is driven by rules and logic. As a result, quality issues in source data begin to surface. These issues are captured and resolved during this time. Data Destinations – This phase is the scheduling of data exports out of the One Model system and delivering them to SFTP sites, Amazon S3 buckets, and/or other destinations. This reflects the vision of our company; not to be the ultimate destination for your data but a data asset existing amid your analytics infrastructure feeding downstream system and tools. Data Accessibility is a noteworthy benefit of One Model's Data Orchestration process. A customer is not restricted to accessing their data only through our query engine. Access is also provided to your orchestrated data directly in the data warehouse hosted on AWS. This allows the usage of your own tools like Tableau, Looker, Qlik, etc. for presentation purposes. Additional benefits include being able to run your own integrations or internal application development against a clean, comprehensive data set to solve challenges specific to your organization. Let us look at two of the most popular HRIS systems and some of the data orchestration advantages One Model offers. Workday - Workday uses point-in-time (snapshot) based reporting. This snapshot reporting is recognized as being limited and brittle in accommodating backdated changes and other HR analytic scenarios. External data is difficult to connect with and pulling and maintaining snapshots from Workday is a pain. One Model avoids all issues with snapshot reporting by rebuilding a data schema that is effective dated and transactional in nature. The result is a dataset perfect for delivering accurate, flexible reporting and analytics. We support both full and incremental refreshes of data from Workday. SAP SuccessFactors - One Model has pre-built data processing logic that can be used to transform data from various SuccessFactors objects into a well-organized, effective-dated structure that supports a wide range of analytic use cases. The SAP SuccessFactors API allows us to identify customizations in your SuccessFactors configuration -- and our data model readily supports the inclusion of those custom fields in the resulting data model. We support both full and incremental refreshes of data from the SuccessFactors API. One Model has perfected data orchestration so well that we are often included in searches for integration partners. Our tailored solution enables the accurate transfers of large files of complex data from existing tools such as an ATS into new, replacement tools. This creates tremendous possibilities for efficiency in migrations and adoption of new technology. If you are interested in receiving full value from your People Analytics investment, please click here to reach out to One Model to schedule an in-depth discussion. Listed below are links to various articles that provide further insight into this topic. The end Snapshot Reporting for People Analytics The need to build Structural Views of SAP SuccessFactors Data People Analytics for SAP SuccessFactors Using People Analytics to support system migration About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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9 min read
Chris Butler
Following our blog last month about how systems issues can open the door to staff underpayment, a number of our stakeholders have asked if we might be able to go deeper into how a people analytics solution and specifically, how One Model can solve this problem. We are nothing if not obliging here at One Model, so here we go! We thought we would answer this question by articulating the most common system-derived problems associated with people data and how One Model and an integrated people analytics plan can help resolve these issues. PROBLEM NUMBER ONE - PEOPLE DATA IS STORED IN MULTIPLE NON-INTEGRATED SYSTEMS As discussed previously, our experience is that most large organisations have at least 7 systems in which they store people data. In some larger organisations - that number can be more than 20! Data silos present a major risk to HR governance. Silos create the risk that information may be different between systems or updated in one system and then not updated in others. If information in one non-integrated system is wrong or out of date, it becomes very hard - firstly to isolate the issue and remediate it and secondly, if the error was made months or years in the past to understand which system controls the correct information. At One Model, we are consistently helping our customers create a single source of truth for their people data. Blending data together across siloed systems provides a great opportunity for HR to cross-validate the data in those systems before it becomes an issue. Blended data quickly isolates instances of data discrepancy - allowing HR to not only resolve individual data issues, but to uncover systemic problems of data accuracy. Often when people are working across multiple systems they will take shortcuts to updating and managing data; this is particularly prevalent when data duplication is involved. If it isn’t clear which system has priority and data doesn’t automatically update in other systems - human error is an inevitable outcome. With One Model, you can decide which systems represent the most accurate information for particular data and merge all data along these backbone elements resulting in greater trust and confidence. The data integration process that is core to the One Model platform can, in effect, create a single source of truth for your people data. This presentation by George Colvin at PAFOW Sydney neatly shows how the One Model platform was used by Tabcorp to manage people data silo issues. PROBLEM NUMBER TWO - LIMITED ACCESS TO DATA IN OLD AND NON-SUPPORTED SYSTEMS Further to the issue of data spread across multiple systems, our experience tells us that not only are most large organisations running multiple people data systems - at least one of those systems will be running software that is either out-of-date or no longer supported by the vendor. So even if you do wish to integrate data between systems, you may be unable. It is always best if you can identify data issues in real time to minimise exposure and scope of impact, but this isn’t always possible and you may have to dig into historical transactional data to figure out the scale of the issue and how it impacts employees and the company. If that wasn’t challenging enough - most companies when changing or upgrading systems for reasons of cost and complexity end up not migrating all of their historical data. This means that you are paying for the maintenance of your old systems or to manage an offline archived database. Furthermore, when you need to access that historical data, running queries is incredibly difficult. This is compounded when you need to blend the historical data with your current system. It is, to put it mildly, a pain in the neck! One Model’s cloud data warehouse can hold all of your historical data and shield your company from system upgrades by managing the data migration to your new system, or housing your historical data and providing seamless blending with the data in your current active systems. If you are interested in this topic and how One Model can help - have a read of this blog that covers in more detail how One Model can mitigate the challenges associated with system migration. PROBLEM NUMBER THREE - ACCESS TO KEY HR DATA IS LIMITED TO THE CENTRAL HR FUNCTION. Either as a result of technology, security, privacy and/or process, HR data in many large organisations is only accessible by the central HR department. As a result, individual line managers don’t have the autonomy or capability to isolate and resolve people data issues before they develop. Data discrepancies are more likely to be identified by the people closest to the real-world events reflected in the transactional system. Managers and HR Business Partners are your first line of defence in identifying data issues, as well as any other HR issue. Of course, line managers need good people analytics to make better decisions and drive strategy, but a byproduct of empowering managers to oversee this information is that they are able to provide feedback on the veracity of the data and quickly resolve data accuracy issues. Sharing data widely requires a comprehensive and thoughtful approach to data sensitivity, security, and privacy. One Model has the most advanced people analytics permissions and role based security framework in the world to help your company deploy and adopt data-driven decision making. PROBLEM NUMBER FOUR - EVEN IF I RESOLVE A HISTORICAL UNDERPAYMENT, HOW DO I ENSURE THIS DOESN’T HAPPEN AGAIN? One of the consistent pieces of feedback we received from the initial blog was that many stakeholders were comfortable that once an issue had been identified they would be able to resolve it - either internally or with the support of an external consulting firm. However, those stakeholders were concerned about their ability to uncover other instances of underpayment in their business or ensure that future incidents did not occur. There is no silver bullet to this problem, however, our view is that a combination of the following factors can ensure organisations mitigate these risks; integrated people data - having a one-stop single source of truth for your people data is crucial. access to historical data - to understand when and how issues developed is also very important. empowerment of line managers to isolate and resolve issues - managers are your first line of defence in understanding and resolving these issues and you need to enable them to fix problems before they develop. People analytics and the One Model product give organisations the tools to resolve all of these problems. If you are interested in continuing this conversation, please get in touch. PROBLEM NUMBER FIVE - A COMPLEX INDUSTRIAL RELATIONS SYSTEM AND A LACK OF PEOPLE HR RESOURCES Previously, most back office processes had a lot of in-built checks and balances. There were processes to cross-check work between team members, ensure transactions totaled up and reconciled correctly and supervisors who would double-check and approve changes. Over the last 20 years large enterprises have been accelerating ERP adoption, in order to realise ROI from that investment, many back office jobs in payroll and other functions were removed with organisations and management expecting that the systems would always get it right. Compounding this and despite many attempts over the years to simplify the industrial relations system, the reality is that managing employee remuneration is incredibly complex. This complexity means that the likelihood of making payroll system configuration, interpretation or processing mistakes is high. So what to do? Of course you need expertise in your team, or be able to access professional advice as needed (particularly for smaller companies). In addition, successful companies are investing in people analytics to support their team and trawl through the large volumes of data to find exceptions, look for anomalies, and track down problems. Our view at One Model is that organisations need to develop metrics to identify and detect issues early. It's what our platform does. We have developed data quality metrics to deal with the following scenarios; Process errors Data inconsistency Transactions contrary to business rules Human error A combination of quality metrics, system integrations, and staff empowered to isolate and resolve issues before they become problems are key to minimising the chances of an underpayments scandal at your business. Thanks for reading. If you have any questions or would like to discuss how One Model can help your business navigate these challenges, please click the button below to schedule a demo or conversation.
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7 min read
Tony Ashton
Following recent media reports that another significant Australian institution has been involved in an underpayments scandal, we thought it appropriate to write a blog about the technical and systemic risk of underpayments for large organisations with disparate HR and payroll systems. While some underpayments are deliberate actions by employers, our experience from over twenty years of working with people data for large organisations is that the overwhelming majority of staff underpayments are driven not by malice. In fact, most HR and payroll professionals care deeply about the people in their organisations. Our experience is that the complexity of the industrial relations system coupled with technology and process mistakes are much larger drivers of ongoing mispayment of employees than deliberate intent. That is why HR and payroll teams need support to prevent these issues from developing. It is important to note that the intent of the mispayment is irrelevant to regulators, your customers, the media, and most importantly - your employees. Whether your organisation, the HR team, or the specific line managers involved intentionally or unintentionally underpaid their staff doesn’t justify the underpayment or limit the risk of such a scandal to your organisation. This is a serious issue and the reputation and employee satisfaction risks to any organisation of an underpayments scandal are immense. Worst still, as in the case of the Made Establishment restaurant chain, if the underpayments are systemic and ongoing, they can become existential for that business and lead to collapse and liquidation. Ensuring your employee payments system works and integrates into other parts of your HR technology ecosystem is crucial to the success of your business. So, how can a minor systems or process issue manifest itself into a broader underpayments scandal? Let’s take an employee who was recently promoted. Information as to their performance and resulting promotion is held in their employer’s learning and development and HR systems. However, a process oversight means that although that individual is listed in a higher pay band in one HR system, their promotion either hasn’t translated into the payroll platform or the information was entered into the payroll system manually and a mistake was made during this process. Let’s also assume that the increased salary is about $2,500 a year - so $50 a week, and it comes during a period of change in that organisation’s payroll practices (they moved from monthly to fortnightly pay cycles) so the employee doesn’t readily notice the absence of their increased salary. If you are an organisation of 2,500 employees and this situation occurred to just 5% of your workforce during a yearly performance review, by the end of the second year your underpayments would total almost one million dollars. In large organisations these relatively minor employee changes occur thousands if not tens of thousands of times a year. Extrapolate that out over multiple years and what initially appears as a minor reporting mistake can quickly become an underpayment scandal involving millions of dollars. In the example given above, a $100 per pay cycle oversight becomes a one million dollar problem. This example is deliberately simple in order to illustrate how quickly these minor issues can multiply. In reality, the employee remuneration frameworks in most large organisations are infinitely more complex and individualised, making it much harder to identify and isolate problems before they spiral into a major scandal. This is compounded when organisations are using outdated software or an amalgamation of different non-integrated IT systems to manage this process. Why should you invest in preemptively isolating and resolving issues of underpayment? Firstly, paying your staff their correct salary is not only the right thing to do, it is the law. Secondly, underpayment and wage theft scandals cause untold damage to an organisation's reputation. Thirdly, underpayment is expensive - not only in wage repayment - but in potential fines from regulators and fees to external consulting/accounting/law firms to understand and resolve your underpayments issues. Underpayment at best is expensive, time consuming, and distracting to your organisation. At worst, it could kill your organisation. Finally, to your employees - your most important assets - underpayments represent a crucial failure in the mutual obligation you both have to do the best for one another. How can an organisation expect the best out of its staff if it can’t pay them properly? How can I use people analytics to anticipate and resolve underpayments issues before they become major scandals? Our experience is that large organisations have at a minimum seven different systems in which they store employee information. In larger and group structured organisations that total can be much higher - in some cases more than twenty! Many of the high profile instances of underpayment, especially those where the organisation has self-reported, indicate that the organisation was unaware of the issue until it was too late. This further indicates that they didn’t have the internal capability to understand what was occurring with their people data and aggregate it in a meaningful way. Part of the reason we created One Model is to fix this exact problem. One Model enables our customers to aggregate their disparate people data into one system (hence the name - One Model), so that they can more comprehensively understand their organisation and avoid systemic issues like underpayments. The ability to compare data in different systems and flag any discrepancies is a core feature of the One Model platform and a feature that is required to avoid the technology and process issues outlined above. Every conversation we have with a customer or prospective customer begins in the same place - organisations are worried about the quality and accessibility of their people data. Sadly, this is often used as an excuse not to invest in people analytics. However, if we have learned anything from these underpayment scandals, it is that you need to take control of your systems, processes, and data. Implementing a people analytics capability helps you achieve this. Internally, we often talk about the importance of One Model aggregating your data to become a single source of truth for all of the people data in your organisation. It's essential to trust the information that is presented to you and make confident decisions based on accurate information. Underpayments are something that your organisation needs to get right and we think accurate people analytics is one of the tools that you can use to get it right. One Model wants to work with enterprises to make sure that these issues are discovered and resolved before they turn into existential threats to your business. If you would like to continue this conversation and learn how One Model can help, or have any feedback on this blog, please comment below or click here to schedule a chat.
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10 min read
Nicholas Garbis
SUMMARY: June was great at 4.8 million new jobs. July will be over 6 million. The June 2020 employment report from the Bureau of Labor of Statistics showed an increase of 4.8 million jobs. It was welcomed as good news, but asterisks were quickly added based on the recently surging Covid infection numbers. July, it is feared, will shown softening in the market. Contrary to many, our data indicates that July jobs report will be better than June, likely exceeding 6 million new jobs. Our analysis is based on the positive signs we have been tracking in the Hospitality and Retail industries over the past several weeks through our Covid Job Impacts site, where we show new job postings on a daily basis by industry, state, and job family. Within the site’s commentary we have been commenting on the progress in these two industries specifically over the past several weeks. The BLS highlighted Hospitality and Retail in their June comments. These industries made up ~60% of the new jobs (2.8 million of the 4.8 million) while they make up ~20% of the total employment. Both were severely impacted in the early stages of the pandemic downturn and are now working their way back toward normal staffing activities. We see Hospitality and Retail combining to create over 4 million new jobs in July, as their job listing activity continues to surge. Most other industries are also showing increased hiring activity, so we estimate they will contribute another 2 million jobs. The August jobs numbers are a bit more difficult to estimate at this point. As states pause and reverse their opening plans, market uncertainties will drive job listings downward though to what extent and how quickly. At the early stages of the pandemic, companies’ reaction times were a bit slower. Perhaps now they have built up more rapid reflexes. Job Listings as a Leading Indicator A company’s decision to advertise a job opening is a clear indicator of their business outlook. If they were not confident at some level, they would delay or cancel the decision to hire. This is true for new, growth hiring as well as backfilling of current positions vacated by resignations or illness. The aggregate of these decisions to hire, as seen in the new job listings data on our Covid Job Impacts site, is therefore a clear and leading indicator for the economy overall. The site provides views of new job postings by industry, state, and job family, indexed to the job listing levels of March 1st, providing a high-resolution lens on the impacts of Covid-19 on the labor market and overall economy. Some views of the Covid Job Impacts site are below as reference: Fig. 1: Overall view of new job listings across all US industries. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Fig. 2: Industry view of new job listings across all US industries. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Trends in Hospitality and Retail industries New job listings in Hospitality hit a low in mid-April at around -80% verus March 1st , and have been on a steady path upward since. This is big progress, albeit far below pre-pandemic levels and still around -45% off versus March 1st. In Retail, the drop was also significant in March, regaining some ground in April and May, then demonstrating strength in June. It is the only industry that, even if for just a moment, has crossed into the positive terrain, exceeding the new job postings figures from March 1st on June 21. Fig. 3: Hospitality and Retail industry trends show continuing improvement. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) BLS summary of Hospitality and Retail in June In the June BLS report, Hospitality and Retail combined to create 2.84 million of the 4.8 million increase in employment. This is roughly 60% of the added jobs coming from two industry sectors that comprise about 20% of the workforce (roughly 10% each). From the June BLS report: Estimating the July Figures for Hospitality and Retail Emerging alongside this good news from BLS are escalating concerns regarding the employment impacts of states’ policy responses to recently increasing infection rates. These actions will certainly have a downward pressure on job creation across industries, Hospitality and Retail notwithstanding. However, the trend data for new job listings for Hospitality and Retail indicates that they will further increase employment in the month of July, at least during the period which will be covered in the next BLS report (which for a single week, generally the week including 12th day of the month). To demonstrate this, we are showing the Hospitality and Retail industry job listing trends along with timing windows to support our estimates for the July report. Fig. 4: View of Hospitality and Retail industries and June BLS reporting week. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) The very positive results from this particular week in June (Fig. 4) would not be from new job listings within that specific week, since job listings take some time to fill and for a new hire to begin working (and therefore be captured in the BLS data). While higher paying jobs can require a few months or more to fill, the jobs in Hospitality and Retail that were so significant in the June the report are relatively lower paying, so we would expect that they are requiring only a few weeks to fill. We have added a box to indicate the period of job listing activity that we assume to comprise most of the new jobs in the June report (Fig. 5, Box A). Fig. 5: Hospitality and Retail industries, with Box A indicating period aligned to June BLS data. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Looking at a similar time window of job listing activity that will correspond to the employment levels on July 12th (Fig. 6, Box B) provides two key observations: the job listings that will be related to new jobs in that week have, for the most part, already been created, and many of them filled; and the volume of job listings in the time period that will be reflected in the July report are considerably higher than the levels that drove the very large job numbers in the June report. Fig. 6: Hospitality and Retail industries, with Box B indicating period aligned to July BLS data. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) Our estimate of 4 million jobs in Hospitality and Retail in the July BLS report is based on the analysis of the job listing volumes in these two industries, focusing on Box A (June report) versus Box B (July report). We can see far greater volumes in the later period. Where Hospitality gained 2.1 million new jobs in June based on the period in Box A, we estimate that the new figures will be around 3 million. Similarly, where Retail created 740,000 new jobs in June, the increased job listings figures in Box B versus Box A lead us to estimate that this industry will create over 1 million new jobs in the July report. Fig. 7: Hospitality and Retail industries, with average lines inserted in Boxes A and B. (Source: Covid Job Impacts site from Greenwich.hr/One Model, data through July 1, 2020) The next few weeks will be critical to watch As state policies regarding Covid-19 are adjusted over the next couple of weeks, we will be closely watching the changes in businesses’ hiring plans as seen through their job listing activities. A closer look at state-by-state results on the Covid Job Impacts site will provide a leading indicator and a way to gauge the August BLS report well before it arrives. About One Model, Inc. One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co
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6 min read
Jamie Strnisha
Over the years I have worked with several operational and strategic (analytic) reporting tools. I have found challenges with both types of reporting tools. Most tools I have worked with focus on solving only one of these reporting challenges, either operational or strategic. Fortunately, One Model’s flexibility and openness in the data model allows us to solve both for our customers. The Challenges: One of the biggest challenges in an operational reporting tool is working with hierarchical (i.e. structured) data. It is extremely challenging to build out the structural relationship of data, such as the Region to Country to State to Work Location relationship. Even though the data relationships exist in the base system, it is almost impossible to use those relationships in reporting and visualization. Even if the relationships can be built out, the structure is typically only available as different columns and there is no way to connect the hierarchical relationship for effective visualization. While these relationships are often defined in strategic reporting tools like SAP SuccessFactors Workforce Analytics, such tools are limited by the data brought in and structured. If a customer has an operational reporting need, they do not have an easy way to bring that data in and use the pre-built structural relationships that exist in the data. (Side note: One Model alleviates this issue and allows customers to bring in any data or data source relatively easily.) One often significant challenge, especially with SAP SuccessFactors Workforce Analytics, is that most of the data is limited to data stored in SAP SuccessFactors. For obvious reasons, this can be frustrating for your team. Perhaps you want to use the data modeled and structured in SAP SuccessFactors to connect with other non-SAP SuccessFactors data sources (e.g. Survey, Facilities, Finance). One Model can make that happen. Overview of SAP SuccessFactors Data Objects Available in the Employee Central API One Model typically sources data from SAP SuccessFactors Employee Central via the OData API. SAP SuccessFactors makes three types of data available in the API: Employee Objects. Personal and employment details for employees, referred to as Person Objects and Employment Objects. Foundation Objects. Organization, pay, and job structure details. Metadata Framework (MDF) Objects. When the standard delivered foundation objects do not meet requirements, existing foundation objects are migrated to the MDF framework (becoming generic objects in the process). New MDF objects are also available. While data from the Employee Objects are critical for reporting, the focus of this blog is the structural relationships defined in the Foundation and Metadata Framework (MDF) Objects, as discussed in more detail below. Foundation and Metadata Framework (MDF) Objects Foundation and Metadata Framework Objects are used to set up data that can be shared across the entire company, such as job codes, departments, or business units. SAP SuccessFactors’ Foundation Objects can be used to populate data at the employee level. For example, if a job code is assigned to an employee, that employee’s record is then populated with all information based on the attributes of the job code. Starting with the November 2014 release, Foundation Objects were migrated to the Metadata Framework (MDF). Source: SAP SuccessFactors Employee Central OData API: Reference Guide Associations in Foundation Objects and Structural Dimensions SAP SuccessFactors uses Associations to define relationships between Foundation Object. One Model can use these defined Associations to build a hierarchical structure. One model will use the data and defined relationships to build a structural dimension, maybe something as simple as: FOGeozone > FOLocationGroup > FOLocation That structural dimension will then allow a user to navigate and filter on the defined relationship. Example: Below is a chart that shows 3 distinct regions (FOGeozone). A user can hover over any of the region labels and find a hyperlink. When the user clicks on the Americas hyperlink, it will drill down to reveal the Countries (FOLocationGroup) below, which in this case includes a breakout of the USA and Canada. Parent-Child Associations in Foundation Objects and Structural Dimensions SAP SuccessFactors also allows for building a Parent-Child Association in the Foundation Object. This relationship can also be translated in One Model. For example, if a larger department is divided into sub-departments, a parent-child association can be created against the department object. One Model can use the relationship defined in the following area FOBusinessUnit > FODivision > FODepartment to define the higher levels of the structure and then use the Parent-Child Relationship within the Department to create desired visualization and filtering experiences for the end user. This behavior can be replicated and created for any of the Foundation Objects where an Association in SAP SuccessFactors has been configured by the customer: Cost Center Department Division Business Unit Legal Entity Legal Entity Local Job Function Pay Group Job Classification Job Classification Local Foundation and Meta Data Framework Object across SAP SuccessFactors and Non-SAP SuccessFactors These structural relationships can be used for reporting across SAP SuccessFactors data, including Recruiting, as well as non-SAP SuccessFactors data. The linking keys will be the IDs used in the Foundation Objects or the employee identifier. If you have questions about how this may work for your organization, we would be happy to chat and share more information. Find success with SuccessFactors. Click here to watch our recorded webinar.
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10 min read
Joe Grohovsky
To help understand why some People Analytics professionals are more successful than others I undertook a worldwide request for insight. I have long held the opinion that 3 basic core competencies were prevalent in successful People Analytics professionals; but to generate a complete profile, I wanted accompanying information on their professional backgrounds, career aspirations, and the organizations who gave them their first People Analytics role. The core competencies referred to are: Close familiarity with the organizations needs and culture Strong people skills An open mind Ultimately my request would suggest the importance of 2 additional factors: Familiarity with data and HR (context) An identified focus (definition of success) Respondent Profile Most respondents were currently working within HR when they assumed their role, though their specific task at the time is unknown. HR was and continues to be the organizational home for most People Analytic roles. Almost half indicated their first People Analytics role emerged gradually from a previous role rather than being specifically created. It was an even split between the first role being a Team of One or not. Two thirds had no specific career path in mind and the same portion feel their careers’ next step will remain within HR. However almost 100% envision People Analytics (PA) being part of their future career, in or out of HR. The greatest self-reported strengths attributable to receiving the People Analytics role were familiarity with data and HR, with technology and math skills also being significant. Lessons Learned If we use SUCCESS and EMBRACING RESULTS separately for scoring, there are 3 areas where lessons can be learned in building our profile: Employee background Availability of People Analytics resources Identification of a specific business problem These lessons are inter-related, but they raise two new questions that are not fully answerable from these results. We can discuss these in our recommendations, but the questions are: Can core competencies overcome deficiencies in the ideal profile? Can a People Analytics role that fails to influence an organization be considered a success? Employee Background No link could be identified between a specific background attribute and success. However, there is a definite link between their background and having their results being embraced. Those respondents who did not have results embraced heavily attributed data familiarity as a strength but had no reported HR strength. Perhaps this was a contextual issue pointing to a weakness in understanding what is important to the company, the correct perspective on HR data, or poor people skills (core competencies). Availability of People Analytic Resources Resource availability seemed to have no impact on success. Slightly more than half of successful respondents were given specific tools, but 40% of successful respondents were provided no team, budget, tools, or other resources. This seems to be another area suggesting the need for core competencies. An open mind may allow the focus to remain on the problem to be solved instead of viewing it from the perspective of an available solution to be used. People skills can empower a professional to leverage resources from other areas of the organization. Identification of a Specific Business Problem Unsuccessful roles usually lacked an identified business problem to address. Stated another way, there was no stated focus. It is my sense that defining focus is the biggest improvement opportunity for both organizations new to People Analytics as well as those who have been practicing for a while. We have already drawn a link between an employee’s background and results not being embraced. Almost none of those situations had a specific business problem to address, and neither were they considered successful. In addition to pre-identifying business problems, many organizations do find value in exploring data to uncover unknown areas for improvement (focus) and following the insights provided. Predictive modeling is a common example of this in People Analytics. In these circumstances business value is found in both historic metrics such as turnover as well as predictive metrics such as attrition risk. Conclusions If we construct a candidate profile of a successful People Analytics professional whose work was embraced, they would be working within HR and have a well-rounded familiarity with HR, data, technology, and math. Their employer provides a clear definition of success by defining a problem on which to focus. Core competencies they possess allow them to overcome the dearth of any resource need as well as the ability to deftly convey their insights back to their organization in an effective, appreciable manner. It is important to note that these core competencies could possibly exist within a single individual or be spread amongst a team. In initiatives that were not embraced, there are several identifiable trouble spots to address. The most visible is the lack of focus/defined business problem. It is not uncommon to expect data to tell you where to focus, but perhaps this is a distinct skillset beyond the stated core competencies. Another concern is highlighted by unembraced initiatives involving People Analytic professionals who reported strength in data familiarity but no strength in HR. Core competencies may provide the people skills to appropriately share insights. However, the nuance of people data and the HR process seems to be lacking in this subset. This possibly points to the need for some HR functional context or guidance on conveying their message. To summarize, ingredients for a successful People Analytics professional producing results that will be embraced by the organization seem to be: 1) Presence of the stated core competencies Close familiarity with the organizations needs and culture Strong people skills An open mind 2) Familiarity with data and HR (context) 3) An identified focus (definition of success) Recommendations The lack of core competencies in an individual does not necessarily doom a People Analytics initiative, or that individual’s participation in it. This situation can be overcome by using formal or informal teams to ensure each skill set is available. It is also advisable to ensure proper context is in place. This involves more than simply examining how the defined business problem is impacting the organization. The People Analytics professional(s) involved may not have a full awareness of the nuances and breadth of the HR function itself. Perhaps an “HR 101” course could be used to explain the relevance of Recruiting, Learning, Total Rewards, Performance, etc. and why those employee processes and data are unique. An alternative to this could be ensuring an HR expert closely reviews all results before they are shared with the business. Perhaps the most significant recommendation is having a definition of success: an identified business problem was a strong component of successful initiatives. There is also a place for exploring your data to find areas of improvement. Caution should be used, and this is where strong people skills will come into play; without a defined focus, the People Analytics professional will have found a problem that was previously unidentified. Calling attention to it and providing suggestions on its resolution can be interpreted as criticizing an organizational leader and telling them how to do their job. The two questions raised but unanswerable by the provided insights were: Can core competencies overcome deficiencies in the ideal profile? Can a People Analytics role that fails to influence an organization be considered a success? Core competencies are true skills and reflect an ability to get things done. This ability powers People Analytics professionals to find resource alternatives, ideal communication techniques, and relevant focus topics. It is my opinion these competencies do a tremendous job of overcoming any inherent shortcomings in a defined role. We must not settle for simply being right but also strive to be effective. People Analytics cannot be successful when results are unembraced by the organization. The goal of any decision support role is to empower better decision making and provide our data-consumers with relevant insights in a meaningful way. Effective People Analytic professionals base their insights on trustworthy data and irrefutable metrics. This is especially relevant with the burgeoning use of artificial intelligence and predictive modeling. People Analytic professionals would do well to remain skeptical of any predictive model that is not fully transparent, cannot be explained, and is verifiably void of hidden bias. Insight Purpose & Process My insight request occurred as a survey shared among social media and industry websites so as broad an audience as possible could be captured. Participants responded from all global regions and the intent was to create a snapshot in time reflecting circumstances when they undertook their first People Analytics role. These circumstances were then compared with both their success in that role and whether their organization embraced their results. The quest was not driven by simple curiosity but a desire to help identify a replicable profile. My work In the People Analytics technology space involves helping my customers succeed in their role and build a practiced embraced by their organization. This resulting profile will be shared with my customers and used to identify areas where I can help them improve. Where are you in your People Analytics Career or Journey? One Model can provide guidance around all the above profile ingredients, and create a path for you to establish yourself as a People Analytics leader as you move forward. Step 1: One Model can help you define your organization's critical metrics and understand how to present them to various layers of decision makers. Step 2: Our team of data engineers can solve your problem of HR data portability and quickly integrate all relevant customer data sources into one platform. Step 3: Our Machine Learning/Artificial Intelligence platform will equip you with a suite of easy-to-use predictive pipelines and data extensions that allow your organization to build, understand, and predict workforce behaviors. If you would like further information on this study or to learn more about One Model, please reach out to me at: Joe Grohovsky | joe.grohovsky@onemodel.co About One Model: One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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31 min read
Chris Butler
The first in a series of posts tackling the individual nuances we see with HR technology systems and the steps we take in overcoming their native challenges to deliver a comprehensive people analytics for SuccessFactors program. Download the White Paper on Delivering People Analytics from SAP SuccessFactorsQuick Links A long history with SuccessFactors Embedded Analytics won't cut it, you have to get the data out World leading API for extraction Time to extract data Full Initial Load Incremental Loads Modelling Data Both SuccessFactors and External SF Data Modelling Analytics Ready Fact Tables Synthetic Events Core SuccessFactors Modules MDF Objects Snowflake Schema Inheritance Metrics - Calculations - Analytics Delivered Reporting and Analytics Content Creating and Sharing your own Analytics Content Using your own Analytical Tools Feed Data to External Vendors What About People Analytics Embedded? What About SAP Analytics Cloud? What About SuccessFactors Workforce Analytics? The One Model Solution for SAP SuccessFactors A long history with SuccessFactors I'm starting with SuccessFactors because we have a lot of history with SuccessFactors. SF acquired Infohrm where many of our team worked back in 2010 and the subsequent acquisition by SAP in 2012. I personally built and led a team in the America's region delivering the workforce analytics and planning products to customers and ensuring their success. I left SAP in 2014 to found One Model. Many of One Model's team members were in my team or leading other global regions and, of course, we were lucky enough to bring on a complete world-leading product team from SAP after they made the product and engineering teams redundant in 2019 (perfect timing for us! Thanks SAP they're doing a phenomenal job!). So let's dive in and explore SuccessFactors data for people analytics and reporting. Embedded Analytics won't cut it, you have to get the data out. It's no secret that all vendors in the core HR technology space espouse a fully integrated suite of applications and that they all fall short to varying degrees. The SF product set has grown both organically and via acquisition, so you immediately have (even now) a disconnected architecture underneath that has been linked together where needed by software enhancements sitting above. Add in the MDF framework with an almost unlimited ability to customize and you quickly have a complexity monster that wasn't designed for delivering nuanced analytics. We describe the embedded reporting and analytics solutions as 'convenience analytics' since they are good for basic numbers and operational list reporting but fall short in providing even basic analytics like trending over time. The new embedded people analytics from SF is an example where the data set and capability is very limited. To deliver reporting and analytics that go beyond simple lists and metrics (and to do anything resembling data science), you will need to get that data out of SF and into another solution. World leading API for data extraction One Model has built integrations to all the major HRIS systems and without a doubt SuccessFactors has the best API architecture for getting data out to support an analytics program. Deep, granular data with effective dated history is key to maintaining an analytics data store. It still has its issues, of course, but it has been built with incremental updates in mind and importantly can cater for the MDF frameworks huge customizability. The MDF inclusion is massive. It means that you can use the API to extract all custom objects and that the API flexes dynamically to suit each customer. As part of our extraction, we simply interrogate the API for available objects and work through each one to extract the full data set. It's simply awesome. We recently plugged into a huge SuccessFactors customer of around 150,000 employees and pulled more than 4,000 tables out of the API into our warehouse. The initial full load took about a week, so it was obviously a huge data set, but incremental loads can then be used for ongoing updates. Some smaller organizations have run in a matter of minutes but clearly the API can support small through to enormous organizations, something other vendors (cough, cough ... Workday) should aspire to. To give you a comparison on level of effort we've spent on the One Model API connectors, approximately 600 hours has been spent on SuccessFactors versus more than 12,000 hours on our Workday connector. Keep in mind that we have more stringent criteria for our integrations than most organizations including fault tolerance, maintenance period traversal, increased data granularity, etc., that go beyond what most individual organizations would have the ability to build on their own. The point is, the hours we've invested show the huge contrast between the SF and Workday architectures as relates to data access. Time to Extract data Obviously, the time needed to extract the data depends on the size of the organization but I’ll give you some examples of both small and huge below. Figure 1: Data extraction from SAP SuccessFactors using APIs Full Initial Loads In the first run we want everything that is available -- a complete historical dataset including the MDF framework. This is the most intense data pull and can vary from 20 minutes for a small organization of less than 1,000 employees to several days for a large organization above 100,000 employees. Luckily, this typically only needs to be done once during initial construction of the data warehouse, but there are times where you may need to run a replacement destructive load if there are major changes to the schema, the extraction, or for some reason your synchronization gets out of alignment. API’s can behave strangely sometimes with random errors, sometimes missing records either due to the API itself or the transmission just losing data, so keep this process handy and build to be repeatable in case you need to run again in the future. The One Model connectors provide an infrastructure to manage these issues. If we're only looking for a subset of the data or want to restrict the fields, modules, or subject areas extracted, we can tell the connector which data elements to target. Figure 2: Configuring the connector to SF in One Model platform Incremental Updates With the initial run complete we can switch the extraction to incremental updates and schedule them on a regular basis. One approach we like to take when pulling incrementals is to take not just the changes since the last run but also take a few extra time periods. For example, if you are running a daily update you might take the last two to three days worth of data in case there were any previous transmission issues, this redundancy helps to ensure accuracy. Typically we run our incremental updates on a daily basis, but you want to run more often than this you should first need to consider: How long your incremental update takes to run. SF is pretty quick, but large orgs will see longer times, sometimes stretching into multiple hours How long it takes your downstream processes to run an update any data If there’s a performance impact to updating data more regularly, typically if you have a level of caching in your analytics architecture this will be blown away with the update to start over again. Impact on users if data changes during the day. Yes, there can be resistance to data updating closer to real-time. Sometimes it's better to educate users that the data will be static and updated overnight. Whether or not the source objects support incremental updates. Not all can, and with SF there’s a number of tables we need to pull in a full load fashion, particularly in the recruiting modules. Modelling data both SuccessFactors and External Okay, we have our SF data and of course we have probably just as much data from other systems that we're going to need to integrate together. SF is not the easiest data set to model, as each module operates with its own nuances that, if you're not experienced with, will send you into a trial and error cycle. We can actually see a lot of the challenges the SF data can cause by looking at the failures the SF team themselves have experienced in providing cross-module reporting over the years. There have been issues with duplicates, incorrect sub domain schemas, and customer confusion as to where you should be sourcing data from. A good example is pulling from employee profile versus employee central. The SAP on premise data architecture is beautiful in comparison (yes really, and look out soon for a similar post detailing our approach to SAP on premise). Modeling the SF Data At this point we're modelling (transforming) the raw source data from SF into analytics-ready data models that we materialize into the warehouse as a set of fact and dimension tables. We like to keep a reasonable level of normalization between the tables to aid in the integration of new, future data sources and for easier maintenance of the data set. Typically, we normalize by subject area and usually around the same timescale. This can be difficult to build, so we've developed our own approaches to complete the time splicing and collapsing of records to condense the data set down to where changes occurred. The effort is worth it though, as the result is a full transactional history that allows the most flexibility when creating calculations and metrics, eliminating the need to go back and build a new version of a data set to support every new calculation (something I see regularly with enterprise BI teams). This is another example of where our team's decades of experience in modelling data for people analytics really comes to the fore. During the modelling process there's often a number of intermediate/transient tables required to merge data sets and accommodate modules that have different time contexts to each other, but at the end of the day we end up materializing them all into a single analytics-ready schema (we call it our One schema) of tables. Some of what you would see is outlined below. Analytics Ready Fact Tables One.Employee - all employee effective dated attributes One.Employee_Event - all employee events, equivalent to action/reason events (e.g. Hire, Termination, Transfer, Supervisor change, etc.). Usually you'll need to synthetically create some events where they don't exist as action/reason combinations. For example, many customers have promotions that aren't captured in the system as a transaction but are logically generated where a pay grade occurs alongside a transfer or any similar combination of logic. One.Requisitions - all Requisition's and events One.Applications - all application events One.Performance_Reviews - all performance review events ... the list goes on Dimension Tables One.dim_age - age breakout dimension with levelling One.dim_gender - gender breakout dimension typically a single level One.organizational_unit - The multi-level organization structure … we could go on forever, here's a sample below of fields Figure 3: Examples of tables and fields created in the One Model data schema Synthetic Events A core HRIS rarely captures all events that need to be reported on, either because the system wasn't configured to capture it or the event classification is a mix of logic that doesn't fit into the system itself. These are perfect examples of why you need to get data out of the system to be able to handle unsupported or custom calculations and metrics. A frequently recurring example is promotions, where an action/reason code wasn't used or doesn't fit and for reporting a logic test needs to be used (e.g. a change in pay grade + a numeric increase in salary). We would implement this test in the data model itself to create a synthetic event in our Employee_Events model. It would then be seen as a distinct event just like the system-sourced events. In this fashion you can overcome some of the native limitations of the source system and tailor your reporting and analytics to how the business actually functions. Core SuccessFactors Modules Employee Central - Aligns with our Employee, Employee Event tables and typically includes about 100+ dimensions as they're built out. The dimension contents usually come from the foundation objects, picklist reference tables, an MDF object, or just the contents of the field if usable. This is the core of the analytics build and virtually all other modules and data sets will tie back to the core for reference. Recruiting - Aligns with our Applications, Application_Event, and Candidates fact tables covering the primary reporting metrics and then their associated dimensional tables. Succession - Aligns with Successor and associated dimensions Performance - Performance Reviews (all form types) and associated dimensions Learning - Learning Events, Courses, Participants Goals - Goals, Goal_Events MDF objects MDF objects are generally built into the HRIS to handle additional custom data points that support various HR processes. Typically we’ll see them incorporated into one of the main fact tables aligning with the date context of the subject fact table (e.g. employee attributes in One.Employee). Where the data isn’t relevant to an existing subject, or just doesn’t align with the time context, it may be better to put the data into its own fact table. Usually the attribute or ID would be held in the fact table and we would create a dimension table to display the breakout of the data in the MDF object. For example, you might have an MDF object for capturing whether an employee works from home. Captured would be the person ID, date, and the value associated (e.g. ‘Works from Home’ or ‘Works from Office’). The attribute would be integrated into our Employee fact table with the effective date and typically a dimension table would also be created to show the values allowing the aggregate population to be broken out by these values in reporting and analysis. With the potential for a company to have thousands of MDF objects, this can massively increase the size, complexity, and maintenance of the build. Best to be careful here as the time context of different custom objects needs to be handled appropriately or you risk impacting other metrics as you calculate across domains. Inheritance of a snowflake schema Not to be confused with Snowflake the database, a snowflake schema creates table linkages between tables that may take several steps to join to an outer fact or dimension table. An example is that of how we link a dimension like Application Source (i.e., where a person was hired from) to a core employee metric like Headcount or Termination Rate which has been sourced from our core Employee and Employee Event Tables. An example of this is below, where to break out Termination Rate by Application Source and Age we would need to connect the tables below as shown: Figure 4: Example of connecting terminations to application source This style of data architecture allows for a massive scale of data to be interconnected in a fashion that enables easier maintenance and the ability to change pieces of the data model without impacting the rest of the data set. This is somewhat opposite of what is typically created for consumption with solutions like Tableau which operate easiest with de-normalized tables (i.e., giant tables mashed together) which come at the cost of maintenance and flexibility. Where one of our customers wants to use Tableau or similar solution we typically add a few de-normalized tables built from our snowflake architecture that gives them the best of both worlds. Our calculation engine is built specifically to be able to handle these multi-step or matrix relationships so you don’t have to worry about how the connections are made once it’s part of the One Model data model. Metrics - Calculations - Analytics When we get to this point, the hardest work is actually done. If you've made it this far, it is now relatively straight forward to build the metrics you need for reporting and analytics. Our data models are built to do this easily and on the fly so there isn't a need for building pre-calculated tables like you might have to do in Tableau or other BI tools. The dynamic, on the fly nature of the One Model calculation engine means we can create new metrics or edit existing ones and be immediately using them without having to generate or process any new calculation tables. Creating / Editing Metrics Figure 5: Example of creating and editing metrics in One Model Delivered Reporting and Analytics Content With an interconnected data model and a catalogue of pre-defined metrics, it is straight forward to create, share and consume analytics content. We provide our customers with a broad range of pre-configured Storyboard content on top of their SuccessFactors data. A Storyboard library page allows a quick view of all subject areas and allow click through to the deeper subject specific Storyboards beneath. This content is comprehensive covering off the common subject areas for analytics and reporting such as workforce profile, talent acquisition, turnover, diversity, etc. There is also the ability to create dashboards for monitoring data quality, performing data validations, and viewing usage statistics to help manage the analytics platform. Figure 6: Sample of standard Storyboard content in One Model Creating and Sharing your own Analytics Content Every one of our customers adds to the pre-configured content that we provide them, creating their own metrics and storyboards to tell their organization's people story, to support their HR, business leaders, and managers, and to save their people analytics team time by reducing ad-hoc requests for basic data. Our customers make the solution their own which is the whole point of providing a flexible solution not tied to the limitations of the underlying source system. Content in One Model is typically shared with users by publishing a storyboard and selecting which roles will have access and whether they can edit or just view the storyboard itself. There's a number of other options for distributing data and content including: Embedding One Model Storyboards within the SuccessFactors application itself Embedding One Model Storyboards within Sharepoint, Confluence, or any other website/intranet (e.g. the way we have used frames within this site: https://covidjobimpacts.greenwich.hr/#) Pushing data out to other data warehouses (what we call a "data destination") on a scheduled basis, something that works well for feeding other tools like Tableau, PowerBI, SAP Analytics Cloud, and data lakes. Sharing Storyboards Embedding Storyboards Example of embedded storyboard COVID Job Impacts site - https://covidjobimpacts.greenwich.hr/# Figures 7, 8, 9: Storyboard sharing and embedding Using your own Analytical Tools We want to ensure you never hit a ceiling on what you can achieve or limit the value you can extract from your data. If you wish to use your own tools to analyse or report on your data, we believe you should have the power to do so. We provide two distinct methods for doing this: Direct Connection to the One Model Data Warehouse. We can authorize specific power users to access the data warehouse directly and read/write all the raw and modeled tables in the warehouse. If you want to use Tableau or PowerBI in this way, you are free to do so. You can write your own queries with SQL or extract directly from the warehouse in your data science programs such as Python or R. The choice is yours. At this point, it is essentially your warehouse as if you created it yourself, we have just helped to orchestrate the data. Data Destinations. If you need to feed data to an enterprise data warehouse, data lake, or other data store, then our data destinations functionality can send the selected data out on a scheduled basis. This is often used to integrate HR data into an enterprise data strategy or to power an investment in Tableau Server or other where teams want the HR data in these systems but don't want to build and run the complex set of APIs and data orchestration steps described above. In both of these scenarios, you're consuming data from the data model we've painstakingly built, reaping the productivity benefits by saving your technical team from having to do the data modelling. This also addresses a perennial issue for HR where the IT data engineering teams are often too busy to devote time to understanding the HR systems sufficiently to deliver what is needed for analytics and reporting success. Feed data to external vendors Another use for the data destinations described above is to provide data to external vendors, or internal business teams with the data they need to deliver their services. Many of our customers now push data out to these vendors rather than have IT or consultants build custom integrations for the purpose. We, of course, will have the complete data view, so you can provide more data than you did in the past when just sourcing from the HRIS system alone. A good example of this is providing employee listening/survey tools with a comprehensive data feed allowing greater analysis of your survey results. Another use case we've also facilitated is supporting the migration between systems using our integrations and data models as the intermediate step to stage data for the new system while also supporting continuity of historical and new data. (Reference this other blog on the topic: https://www.onemodel.co/blog/using-people-analytics-to-support-system-migration-and-innovation-adoption) Scheduled Data Destinations Figure 10: Example of data destinations in One Model What About People Analytics Embedded? This solution from SF is great for what we call 'convenience analytics' where you can access simple numbers, low complexity analytics and operational list reports. These would provide basic data aggregation and simple rates at a point in time without any historical trending. In reality, this solution is transactional reporting with a fancier user interface. Critically, the solution falls down in its inability to provide the below items: Trending across time (an analytics must have) Limited data coverage from SF modules (no access to data from some core areas including learning and payroll) Challenges joining data together and complexity for users in building queries No ability to introduce and integrate external data sources No ability to create anything of true strategic value to your organization. What About SAP Analytics Cloud? SAC has shown some great promise in being able to directly access the data held in SF and start to link to some external source systems to create the data integrations you need for a solid people analytics practice. The reality, however, is the capability of the product is still severely limited and doesn't provide enough capacity to restructure the data and create the right level of linkages and transformations required to be considered analytics-ready. As it is today, the SAC application is little more than a basic visualization tool and I can't fathom why an organization would take this path rather than something like Tableau or PowerBI which are far more capable visualization products. SAP Analytics Cloud has not yet become the replacement for the Workforce Analytics (WFA) product as it was once positioned. The hardest parts of delivering a robust people analytics software has always been the ongoing maintenance and development of your organizational data. The SF WFA's service model provided this with an expert team on call (if you have the budget) to work with you. With SAC, they have not even come close to the existing WFA offering, let alone something better. The content packages haven't arrived with any depth and trying to build a comprehensive people analytics suite yourself in SAC is going to be a struggle, perhaps even more than building it on your own in a more generic platform. What About SuccessFactors Workforce Analytics? Obviously, our team spent a lot of time with SuccessFactors' WFA product even predating the SF acquisition. The WFA product was a market and intellectual pioneer in the people analytics field back in the day and many members of our team were there, helping hundreds of organizations on their earliest forays into people analytics. The WFA solution has aged and SF has made little to no product improvements over the last five years. It is, however, still the recommended solution for SF customers that want trending and other analytics features that are relatively basic at this point. Several years ago, we started One Model because the SF WFA product wasn't able to keep pace with how organizations were maturing in their people analytics needs and the tool was severely limiting their ability to work the way they needed to. It was a black box where a services team (my team) had to deliver any changes and present that data through the limited lens the product could provide, all for a fee of course. Organizations quickly outgrew and matured beyond these limitations to the point I felt compelled to tackle the problem in a different fashion. One Model has become the solution we always wanted to help our customers become successful and to grow and mature their people analytics capability with data from SAP SuccessFactors and other systems. We provide the integrations, the analytical content, the data science, the transparency, scalability, and configurability that our customers always wished we could provide with SF WFA. We built our business model to have no additional services cost, we keep all aspects of our data model open to the customer, and our speed and delivery experience means there's no limit to which modules or data sets you wish to integrate. The One Model Solution for SAP SuccessFactors Direct API Integration to SuccessFactors Unlimited data sources Daily data refresh frequency Unlimited users Purpose built data models for SAP and SF No additional services costs People analytics metrics catalogue Create your own metrics and analytics Curated storyboard library for SuccessFactors Operational reporting Embed and share storyboards HR's most advanced predictive modelling suite Access all areas with a transparent architecture Use your own tools e.g. Tableau, PowerBI, SAC Take a tour in the video below We are happy to discuss your SuccessFactors needs.
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Featured
5 min read
Tony Ashton
In the last One Model product update post I talked about our new user experience and hinted at some exciting new developments on the horizon. In this post I want to share some more information on those future designs. Thanks again to our customers for sharing their time and collaborating with us on our UX developments and everyone in One Model, but I want to make a special mention of the powerhouse behind One Model’s product designs - Nicole Li - a true UX unicorn! While the new user experience showcases Nicole’s incredible work, the new stuff is where things get super exciting. This content is being shared to provide an insight into future product developments planned by One Model. This should not be interpreted as a commitment to deliver any particular functionality or to any defined timeline and may be changed at any point by One Model at its discretion. Purchasing decisions should be made based on current product capability only. Having said this, we are super excited to share what we are working on and actively engage with you regarding product innovation and the future of people analytics. At the risk of overusing some classic cliches; startups run on pizza and business runs on PowerPointTM (well slides anyway). The slides phenomenon has been prevalent for the last couple of decades and when I ask almost any company how they share information with managers, executives, boards, or in general meetings the answer over 90% of the time is “slides”. Storyboards & Slides When we recently announced One Model’s new Storyboard capability we hinted at a broader vision and here is part of that vision starting to unfold. The new Storyboards will have two modes, one where you have a fairly traditional tile based layout and the other where you are in presentation mode. Online interactive use of One Mode is growing rapidly, but much of the content from One Model still ends up in a presentation at some point, so we want to reduce the effort to create and maintain this content. Storyboards are then acting as both a modern interactive storytelling dashboard and interactive slide based presentation without the need for any rework. This will save a massive amount of time and also pre-positions the content for the most common destination to meet the consumers where they are. So, how does this work? The Storyboard view lets you arrange tiles on a forever vertically scrolling space with control over layout, size etc. You can then flip to Slides view to get an auto-arranged presentation with one tile per slide and controls to optimize the display for presenting to a group of people. Within the Slides view you can manage layout, decide which tiles to show or hide from the presentation, combine slides together etc. To help you create a narrative for your presentation you can open the outline view and craft the flow of your story. Being able to present online from within the One Model platform is powerful and provides you the ability to interact with the data in real-time to really engage with your audience. And, “yes”, to the question you are starting to form in your mind… you will be able to export this to PowerPointTM to blend with other presentations you are creating offline :) Telling a Story Using Narrative Insights Having assembled a compelling set of data isn’t sufficient to drive action. You also need to engage your audience and the best way to do this is through storytelling. The next major feature to our Storyboards vision is the ability to add a narrative to any tile that describes what is going on in straight-forward business language. Initially this capability will include information from One Model’s metric library and your own narrative, but over time we will incorporate insights powered by the One Ai machine learning platform. Captions can be rearranged as elements within a tile, or a separate, linked, tile with controllable positioning and layout depending on how you want to arrange your storyboard. The Storyboards vision is incredibly exciting and customers we have engaged in the design thinking behind these innovations can’t wait to get their hands on this new capability. Neither can we! Stay tuned for more information as the roadmap unfolds. This article has been primarily concerned with the developing technology of Storyboards, but I also want to let you know that One Model has a vast library of content to help you tell the story of how people drive impact in your business. We’ll write some more on this soon, but reach out if you want to learn more about our metric catalogue and ever-growing library of topic storyboards. When combined with OneAi, our Machine Learning platform, you can generate automated insights, future forecasts and identify key risks to answer the most pressing business questions you have today.
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8 min read
Chris Butler
With the continued growth of the Coronavirus pandemic our leaders are going to be asking for regular updates on our employees health and our business’s productivity. This is not going to be a flash in the pan event either. The path back to normal will be long and gradual which means we need to approach data collection, reporting, and analysis with an emphasis on repeatability. To that end there’s a number of questions that HR teams are going to need to answer in order to provide a status of and show the progression of the businesses adaptation to these challenges and how our workforce is coping. What questions are we going to need to answer? What % of our workforce can be switched to work remotely if needed? What % has already shifted to working remotely due to COVID-19? What is the trend as we ramp this ability? What % of our workforce is currently not working due to COVID-19? How are infection rates trending in the countries/states/provinces where we have employees? What is the trend in our employee infection rates and how do they compare to the relevant country/state/province? What is the risk level of our workforce in a given area based on the age distribution and other relevant factors? Do we have any locations that are significantly impacted by COVID-related absences? What is the average duration of employees being unavailable due to COVID-19 - illness or other? What % of our infected workforce has recovered and returned to work? What % of our temporarily remote workforce has returned to working on location? What is our current productive capacity %? How long are impacted employees non productive for? How much productive capacity have we lost? So, what data do we need and how do we organize it to address the questions above? Download our Covid-19 Tracking Worksheet This is where things get tricky and HR needs to be collecting additional data beyond what they have today. This is likely going to need to come from manager input with HR acting as the central collation point. Ideally this information can be captured and held within your HRIS, but most likely this is going to start out as a spreadsheet as your HRIS may not have the required fields for what we need to measure beyond traditional absence & availability information. My view is that shortly we're going to be asking managers to provide information to HR when their employees move into quarantine, infection, and start/stop work (when remote) because of illness. This may or may not also be in association with an HRIS event recording absence or similar. As this data is collated you'll want to make sure you can collect a few key data points as per the below Ability to work from home Currently working from home Date employee stopped working Date employee returned to work Date employee in a Quarantine status Date employee in a Infected status Date employee cleared of Quarantine/Infected status This data can then be merged with the following HRIS information Location information: Country, State/Province, City/Location (for site-level metrics and comparison to global/national statistics) Personal information: age, gender (optional - for risk assessment and forecasting, data would not include name of employee ID number) Employment: employee type (regular, temp/FTC, contractor), Full/Part time A combination of this event-related data alongside the HRIS data will create the ability to track over time the status of our workforce so we can report and analyze the trend and impact on the business. Some of these data points can be inferred from your existing systems It’s going to be a challenging job to collect and keep collated the above data so if you have already or can get data from some additional systems like facilities and IT access you can infer some of these data points. Below are some examples of business logic some of the organizations we have been talking to are using. Ability to work from home = Have access to a VPN, Have a laptop. Currently working from home = Are accessing the VPN, have not badge swiped into an office/facility. Not working due to infection = A leave of absence record with no recent vpn access or badge swipe. How would we present this information? Note: the above is mock data Workforce Composition & Employee Health Overall metrics on the current workforce showing the total population, working status, remote working rates, and infection rates. We also want to show this trending over time so we have an idea of the growth and ultimately the recession of infection rates. Key Metrics Headcount, Headcount % - Quarantine, Infected, Recovered statuses Currently Working % Working from Home % High Risk Populations Comparison against daily statistics by country/state/province produced by health organizations will enable you to compare your infection trend to the prevailing trend in the relevant geographical area. If the area is seeing an acceleration of cases, you should anticipate similar risks for your workforce. If the area has hit an inflection point and is leveling off, the risk to that part of your workforce should be on the wane as well. Beyond geographic risk, age is the biggest factor on the impact to the employee and we're going to see longer infection periods and mortality rates for older employees than we will for other populations. Obviously any steps the organization can take to protect higher risk populations should be fought for. Key Metrics Regional Infection Rates (where available) Active, Quarantine, Infected, Recovered by Age, Location, Department Productive Status The absence of employees will reduce the productivity of your workforce, potentially impacting customers and creating financial risks such as over-ordering of supplies and raw materials, over-estimating orders and revenues, or committing to delivery dates that cannot be achieved because of workforce impacts beyond the view of the manufacturing location’s management. Questions we need to answer include how many of our employees are currently working, whether from home or their normal location? How many hours have we lost due to infection-related absences? While productivity isn't a major concern when lives are at stake, many of the actions we may take in protecting our employees will show up in either infection rates or in absences and we should be measuring to see what was effective and what wasn't. Key Metrics Currently Working % - Active, Quarantine, Infected Ability to work from home % Lost productive hours (key here are the dates for when people stop and return to work) Duration of Quarantine and Infection. As we plan for how our employees are impacted we need to be analyzing how long our quarantined and infected employees are unable to work. Many employees may still be able to work from home while under quarantine and or infection but there will be periods when they are infected and unwell that they won’t be able to work while when their symptoms are significant or while they are recovering. Questions we need to answer include how long is the infection period for our workforce? What is our forecast for our currently infected employees being back to work? Key Metrics Days in Quarantine Days from Infection to Recovered Number of Reinfections, Reinfection Rate Extension opportunities The above metrics and questions are a bare bones set of pertinent information that you could provide to leaders even if you don’t have fully integrated HR systems. Of course, there are many more attributes available that leaders may want to view. These will be specific to each business’ strategy so my advice is to include a number of common HRIS fields into the data collection process when you build your initial data set so you can segment later as needed. Suggested other data elements to consider adding Succession – can we tap into the successors for persons in critical roles? How impacted are these successors? Critical roles – prioritize remote work arrangements or advise early preventive quarantine measures for specific roles in certain areas Skills data – capture potential temporary backfills for employees that are unable to work Set-up expense tracking – spending to facilitate remote working capabilities Re-infection rates – tracking of persons previously cleared
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4 min read
Chris Butler
The SuccessFactors Workforce Analytics (platform pictured above) is soon to be sunset. If you haven't heard already, the SuccessFactors Workforce Analytics and Planning teams were made redundant yesterday. Product, Support, and Engineering teams for the platform (pictured above) have been given notice leaving a handful of services to maintain existing customer deployments. A lot of talented friends and pioneers in people analytics are now looking for new jobs. If and when formal word comes out of SAP, I am sure it will be along the lines of "Workforce Analytics (WFA) is not dead but moving to SAP Analytics Cloud (SAC)" with no specific timeline or plan for doing so let alone whether equivalent capability will be available (it won't be). Luckily, if you're up and running on WFA you've done all the hard work to get there. Your data is flowing and your business logic is defined. I'm here to offer all WFA customers a transition to One Model with no cost and a promise you'll be up and running with a more capable solution in a matter of days. Simply switch your existing data feeds to One Model, provide us your WFA data specification, and we'll do the rest. Literally - we'll have you up and running in a matter of days. And we can do more in more in a single day than SuccessFactors Workforce Analytics used to be able to provide in six weeks. What's awesome about One Model: Experience an all-inclusive platform: access all your data with no limits, no modules, and no implementation fees. Leverage our experience, models, and content catalogues. Don't deal with extra charges: no paid services for building metrics, dimensions, and building new modules. Daily data refreshes. Get a real HR Data Strategy built for the future of people analytics that will fully support your evolving technology landscape. Gain full access to the data warehouse, data modeling, and full exposure for user transparency. Plug in your own tools like Tableau, Excel, SAC. Truly system agnostic. Access automated machine learning to build custom predictive models relevant to you. Use the worlds most advanced Role Based Security and overcome the challenges you currently have providing secure data views to the right users. Embed within SuccessFactors using a SF extension built by one of our partners. Embed within Portals like Sharepoint, and Confluence. Feed external systems and vendors with clean, consolidated data and use us as part of any system migration to maintain history and configure data for the new system. Way too much more to list here... The Offer: Switch to One Model with no implementation fee. Redirect your feeds. Provide your Workforce Analytics data specification. Receive a people analytics infrastructure and toolkit built to support your growth in maturity and capability. Bonus: One Model will match the SF WFA subscription price if our subscription is higher. HR Analytics should flow as a by-product of how you manage your people data. "This is the way data will be managed." "OneModel’s approach is significantly different from the rest of the pack. It understands the dynamic nature of organizations and provides monitoring and maintenance capacity for the inevitable moment in which a data model ceases to be effective." - John Sumser, HR Examiner About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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4 min read
Josh Lemoine
Software companies today aren't exactly selling the idea of "lovingly crafting you some software that's unique and meaningful to you". There's a lot more talk about best practices, consistency, and automation. It's cool for software capabilities to be generated by robots now. And that's cool when it comes to things like making predictions. One Model is a leader in that space with One AI. This post isn't about machine learning though. It's about modeling your company's people data . The people at One Model work with you to produce a people data model that best suits your company's needs. It's like having your own master brewer help guide you through the immense complexity that we see with people data. Why does One Model take this hands-on approach? Because the people employed at your company are unique and your company itself is unique. Organizations differ not only in structure and culture but also in the combinations of systems they use to find and manage their employees. When you consider all of this together, it's a lot of uniqueness. The uniqueness of your company and its employees is also your competitive advantage. Why then would you want the exact same thing as other companies when it comes to your people analytics? The core goal of One Model is to deliver YOUR organization's "one model" for people data. A Data Engineer builds your data model in One Model. The Data Engineer working with you will have actual conversations with you about your business rules and logic and translates that information into data transformation scripting. One Model does not perform this work manually because of technical limitations or an immature product. It's actually kind of the opposite. Affectionately known as "Pipeo", One Model's data modeling framework is a major factor in allowing One Model to scale while still using a hands-on approach. Advantages of Pipeo include the following: It's fast. Templates and the "One Model" standard models are used as the starting point. This gets your data live in One Model very quickly, allowing for validation and subsequent logic changes to begin early on in the implementation process. It's extremely flexible. Anything you can write in SQL can be achieved in Pipeo. This allows One Model to deliver things outside the realm of creating a standard data model. We've created a data orchestration and integrated development environment with all the flexibility of a solution you may have built internally. It's transparent. You the customer can look at your Pipeo. You can even modify your Pipeo if you're comfortable doing so. The logic does not reside in a black box. It facilitates accuracy. Press a validation button, get a list of errors. Correct, validate, and repeat. The scripting does not need to be run to highlight syntax issues. OMG is it efficient. What used to take us six weeks at our previous companies and roles we can deliver in a matter of hours. Content templates help but when you really need to push the boundaries being able to do so quickly and with expertise at hand lets you do more faster. It's fun to say Pipeo. You can even use it as a verb. Example: I pipeoed up a few new dimensions for you. The role the Data Engineer plays isn't a substitute for working with a dedicated Customer Success person from One Model. It's in addition to it. Customer Success plays a key role in the process as well. The Customer Success people at One Model bring many years of industry experience to the table and they know their way around people data. They play a heavy role in providing guidance and thought leadership as well as making sure everything you're looking for is delivered accurately. Customer Success will support you throughout your time with One Model, not just during implementation. If you'd like to sample some of the "craft people analytics" that One Model has on tap, please reach out for a demo. We'll pour you a pint right from the source, because canned just doesn't taste as good.
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11 min read
Chris Butler
About ten years ago, as the pace of HR technology migration to the cloud started to heat up, I started to have a lot more conversations with organizations that were struggling with the challenges of planning for system migration and what to do with the data from their old systems post-migration. This became such a common conversation that it formed part of the reason for One Model coming into existence. Indeed much of the initial thought noodling was around how to support the great cloud migration that was and still is underway. In fact, I don't think this migration is ever going to end as new innovation and technology becomes available in the HR space. The pace of adoption is increasing and more money is being made than ever by the large systems implementation firms (Accenture, Deloitte, Cognizant, Rizing etc). Even what may be considered as a small migration between two like systems can cost huge amounts of money and time to complete. One of the core challenges of people analytics has always been the breadth and complexity of the data set and how to manage and maintain this data over time. Do this well, though, and what you have is a complete view of the data across systems that is connected and evolving with your system landscape. Why then are we not thinking in a larger context about this data infrastructure to be able to support our organizations adoption of innovation? After all, we have a perfect data store, toolset, and view of our data to facilitate migration. The perfect people analytics infrastructure has implemented an HR Data Strategy that disconnects the concept of data ownership from the transactional system of choice. This has been an evolving conversation but my core view is that as organizations increase their analytical capability, they will have in place a data strategy that supports the ability to choose any transactional system to manage their operations. Being able to quickly move between systems and manage legacy data with new data is key to adopting innovation and organizations that do this best will reap the benefits. Let's take a look at a real example, but note that I am ignoring the soft skill components of how to tackle data structure mapping and the conversations required to identify business logic, etc., as this still needs human input in a larger system migration. Using People Analytics for System Migration Recently we were able to deploy our people analytics infrastructure with a customer to specifically support the migration of data from Taleo Business Edition to Workday's Recruiting module. While this isn't our core focus as a people analytics company, we recently completed one of the last functional pieces we needed to accomplish this, so I was excited to see what we could do. Keep in mind that the below steps and process we worked through would be the same from your own infrastructure but One Model has some additional data management features that grease the wheels. To support system migration we needed to be able to Extract from the source system (Taleo Business Edition) including irregular data (resume files) Understand the source and model to an intermediate common data model Validate all source data (metrics, quality, etc) Model the intermediate model to the destination target model Push to the destination (Workday) Extract from the destination and validate the data as correct or otherwise Infinitely and automatically repeat the above as the project requires. Business logic to transform and align data from the source to target can be undertaken at both steps 2 and 4 depending on the requirement for the transformation. Below is the high level view of the flow for this project. In more detail The Source There were 132 Tables from Taleo Business Edition that form the source data set extracted from the API plus a separate the collection of resume attachments retrieved via a python program. Luckily we already understood this source and had modeled them. Model and Transform We already had models for Taleo so the majority of effort here is in catering for the business logic to go from one system to another and any customer specific logic that needs to be built. This was our first time building towards a workday target schema so the bulk of time was spent here but this point to point model is now basically a template for re-use. The below shows some of the actual data model transformations taking place and the intermediate and output tables that are being created in the process. Validation and Data Quality Obviously, we need to view the data for completeness and quality. A few dashboards give us the views we need to do so. Analytics provides an ability to measure data and a window to drill through to validate that the numbers are accurate and as expected. If the system is still in use, filtering by time allows new data to be viewed or exported to provide incremental updates. Data Quality is further addressed looking for each of the data scenarios that need to handled, these include items like missing values, and consistency checks across fields Evaluate, Adjust, Repeat It should be immediately apparent if there are problems with the data by viewing the dashboards and scenario lists. If data needs to be corrected at the source you do so and run a new extraction. Logic or data fills can be catered for in the transformation/modelling layers including bulk updates to fill any gaps or correct erroneous scenarios. As an automated process, you are not re-doing these tasks with every run - the manual effort is made once and infinitely repeated. Load to the Target System It's easy enough to take a table created here and download it as a file for loading into the target system but ideally you want to automate this step and push to the system's load facilities. In this fashion you can automate the entire process and replace or add to the data set that is in your new system even while the legacy application is still functioning and building data. On the cutover day you run a final process and you're done. Validate the Target System Data Of course, you need to validate the new system is correctly loaded and functioning so round-tripping the data back to the people analytics system will give you that oversight and the same data quality elements can be run against the new system. From here you can merge your legacy and new data sets and provide a continuous timeline for your reporting and analytics across systems as if they were always one and the same. Level of Effort We spent around 16-20 hours of technical time (excluding some soft skills time) to run the entire process to completion which included Building the required logic, target to destination models for the first time Multiple changes to the destination requirements as the external implementation consultant changed their requirements Dozens of end to end runs as data changed at the source and the destination load was validated Building a python program to extract resume files from TBE, this is now a repeatable program in our augmentations library. That's not a lot of time, and we could now do the above much faster as the repeatable pieces are in place to move from Taleo Business Edition to Workday's Recruiting module. The same process can be followed for any system. The Outcome? "Colliers chose One Model as our data integration partner for the implementation of Workday Recruiting. They built out a tailored solution that would enable us to safely, securely and accurately transfer large files of complex data from our existing ATS to our new tool. They were highly flexible in their approach and very personable to deal with – accommodating a number of twists and turns in our project plan. I wouldn’t hesitate to engage them on future projects or to recommend them to other firms seeking a professional, yet friendly team of experts in data management." - Kerris Hougardy Adopting new Innovation We've used the same methods to power new vendors that customers have on-boarded. In short order, a comprehensive cross-system data set can be built and automatically pushed to the vendor enabling their service. Meanwhile the data from your old system is still held in the people analytics framework enabling you to merge the sets for historical reporting. If you can more easily adopt new technology and move between technologies you mitigate the risks and costs of 'vendor lock-in'. I like to think of this outcome as creating an insurance policy for bad fit technology. If you know you can stand up a new technology quickly, then you can use it while you need it and move to something that fits better in the future without loss of your data history then you will be more likely to be able to test and adopt new innovation. Being able to choose the right technology at the right time is crucial for advancing our use of technology and ideally creating greater impact for our organization and employees. Our Advice for Organizations Planning for an HR System Migration Get a handle and view across your data first -- if you are already reporting and delivering analytics on these systems you have a much better handle on the data and it's quality than if you didn't. The data is often not as bad as you expect it to be and cleaning up with repeatable logic is much better than infrequently extracting and running manual cleansing routines. You could save a huge amount of time in the migration process and use more internal resources to do what you are paying an external implementation consultant to deliver. Focus more time on the differences between the systems and what you need to cater for to align the data to the new system. A properly constructed people analytics infrastructure is a system agnostic HR Data Strategy and is able to deliver more than just insight to your people. We need to think about our people data differently and take ownership for it external to the transactional vendor, when we do so we realize a level of value, flexibility and ability to adopt innovation that will drive the next phase of people analytics results while supporting HR and the business in improving the employee experience. About One Model One Model delivers a comprehensive people analytics platform to business and HR leaders that integrates data from any HR technology solution with financial and operational data to deliver metrics, storyboard visuals, and advanced analytics through a proprietary AI and machine learning model builder. People data presents unique and complex challenges which the One Model platform simplifies to enable faster, better, evidence-based workforce decisions. Learn more at www.onemodel.co.
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4 min read
Stacia Damron
This summer, One Model opens new Data Center in Sydney, Australia. It's been a busy period for One Model, especially for our growing Australia office. If you can scroll past this gorgeous teaser photo without getting sidetracked and planning a vacation, we are going to provide some updates on what exactly the team has been up to. To begin with, the team has just opened a new state-of-the-art, enterprise grade infrastructure in its Sydney, Australia AWS hosted Data Center. The Australian infrastructure, which meets strict security standards, joins One Model’s fabric of existing infrastructure in the United States and Europe, all of which are designed to provide a local, robust, secure, and high-performance environment for its customers’ people and business data. This is our first data center in Australia. The data center opening comes shortly after the acquisition of our newest Melbourne-based, customer. Our newest customer, an Australian wagering and one of the world’s largest gaming companies, selected One Model as the company of choice for their people analytics platform in Q2 of 2019. Our team is thrilled to be a foundational element to their employee experience strategy and we plan to provide a number of key benefits including improved insight into our people, increased efficiency, and strategic value to key stakeholders. Our people analytics infrastructure's fast speed of deployment will help this new customer shift away from a reliance on legacy ways of working and technologies. “With an Australian founding team and a sizeable part of the One Model Engineering and Product Management teams being based in Brisbane, the team’s local knowledge and proximity represents a unique opportunity for customers in the Asia Pacific region. It allows One Model to be an active part of our global product innovation compared to traditional analytics software vendors.” says Tony Ashton, Chief Product Officer for One Model. These additional data centers play a crucial role in the company’s ability to better serve its current and future Australian and Asia Pacific region customers, as well as ensuring business continuity as the company continues to grow within the Australian market. Earlier this year AWS received PROTECTED and IRAP certification ensuring security compliance for working with the Australian Government and large enterprise. “The opening of this new data center is inline with One Model’s commitment to expand where our customers need us and to provide local infrastructure and personnel for data security and delivery of support services. An additional data center is already planned for delivery in Canada to support our Canadian customers in Q4 of 2019”, says Chris Butler, One Model CEO. One Model looks forward to welcoming additional internationally-based companies into it's family of customers as we continue to expand to serve these additional markets. In Australia? Want to meet the One Model team in person? Join us for the annual Australian HR Institute (AHRI) Convention in Brisbane this September 16-19th, where we'll be exhibiting at stand #64. The exhibition hall is open to visitors free of charge. Let us know if you plan to stop by! About One Model One Model delivers our customers with a people analytics infrastructure that provides all the tools necessary to directly connect to source technologies and deliver the reporting, analysis, and ultimately prediction of the workforce and it's behaviors. Use our leading out-of-the-box integrations, metrics, analytics, dashboards, and domain expert content, or create your own as you need to including the ability to use your own tooling like Tableau, Power BI, R, Python as you need. We provide a full platform for building, sustaining, and maturing a people analytics function delivering more structure information, measurement, and accountability from your team. Learn more at onemodel.co.
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10 min read
Tony Ashton
Here at One Model, we are incredibly excited to have Tony Ashton join us from SAP SuccessFactors as our first Chief Product Officer and there is no better way to introduce the company’s first Chief Product Officer than for Tony to share his thoughts directly below. Why One Model? I'm incredibly excited to join the One Model team as the Chief Product Officer. I'm writing this blog to share my enthusiasm for One Model and also a bit about my background to hopefully serve as a guide to how we drive product innovation going forward. So why One Model? Simply put, One Model is doing the most exciting, innovative work in the people analytics space today. People Analytics is one of the most complex analytical domains due to the variety and complexity of the data. Even in systems purporting to provide a complete suite of integrated HR solutions the underlying data models for all of the different functional areas remain varied and complicated, if not impenetrable. Just think of the underlying data models within the core HRIS or Recruiting, Performance, Succession, Payroll, Benefits, Learning et.al. Then overlay concepts like date effectiveness, position management, multiple occupancy, changing organizational structures! I could go on. The One Model team has decades of experience in dealing with this specialized HR data domain and is the best company in the world at transforming all this data into one unified data model. No other company is going as deep and innovating as fast as One Model on the data modelling side and this is essential for success in People Analytics. Good clean data is critical for data science and most data scientists have to spend over 80% of their time assembling, organising and cleansing data. One Model solves this problem and provides scalability for data science. Beyond this, One Model is also leading innovation in the areas of Artificial Intelligence and Machine Learning in the People Analytics space. AI & ML are massively over-hyped, particularly when applied to the Human Resources domain. In large part this is because most data science in HR is based on one-off projects and not built to scale. The One AI platform One Model has built - it isn’t a generic toolkit, it is purpose built for developing insights for People Analytics. The introduction of Artificial Intelligence, Machine Learning & Robotics are now starting to drive real change and this has an incredible impact on how work gets done in business today. Analytics provides understanding and through the use of advanced technologies like One AI you are able to model the future, build alternate scenarios, understand the things that are driving change and take control of the future of work. I’ll talk more about “Why One Model?” at the end of the blog, but now want to turn to how I see the world of people analytics product management. Customer centricity and deep understanding. When building products you always need to think from the outside-in to understand the real problems you are trying to solve for your customers and this is my philosophy. The contemporary term for this is the "jobs-to-be-done theory", which has been around for a while and basically says you should focus on the task someone is trying to perform, or the outcome they are striving for and then design your solution to help achieve that outcome. When you say it out loud it is incredibly obvious, but then most of the best ideas are. Here's a great quick primer for you that's also a fun read: https://hbr.org/ideacast/2016/12/the-jobs-to-be-done-theory-of-innovation. (I have hired many donuts in my time - this will make sense when you read the article 🙂 ) I'm excited by ideas. Big ideas and concepts are important. I studied philosophy and history (with a focus on the history of innovation) at university and this passion nicely intersected with my business life when I read Clayton Christensen's quintessential book "The Innovator’s Dilemma". This book was ground-breaking in 1997 and the concept of the “Innovator’s Dilemma” is now part of popular parlance, but the principles are still impacting business today, so I recommend you have a read if you haven't already been there. Going further back, I was also influenced by Thomas Khun's seminal book "The Structure of Scientific Revolutions", where he coined the phrase "paradigm change" before it was hijacked for pop-psychology purposes. We are in a time of revolutionary change right now and understanding this is critical for success. Making something great. A mantra I borrowed from my good friend Philip Haine when we would work on new product designs together was this phrase he would often use: "What would be amazing?". You can run a detailed design thinking process and generate lots of ideas and this is a great structured way to involve people in designing a solution based on empathy, etc. but standing back and thinking about a problem from the perspective of the person at the center and simply asking “what would be amazing” for them is an awesome way to cut through and quickly generate truly ground-breaking solutions. Another mentor along the way was Dmitri Krakovsky, who would always ask this simple question of any project: "Is it great?". Mid-way through a product development cycle, if you sit back and ask yourself "is it great?" and it isn't then you should seriously think about what you are working on and why. Applied Technology & Innovation. Building on the 'making something great' discussion above, the best technology becomes seamlessly integrated with your work/life and genuinely helps you get things done. It should also be cool and fun to use. Have a think about what apps you like to use. Some I use everyday include Pocket, Flipboard, Slack, Evernote, Dropbox, Google Maps. What do all these apps have in common? They have a focus, they do what they do incredibly well and don't try to be something they aren't. Why isn't enterprise software like this? Why are all analytics products just like using a big spreadsheet, or so complicated you need a degree in statistics? Some products look pretty and appear simple to use, but often when you dig into it they just don’t deliver the goods. It doesn't need to be this way. The Art & Practice of People Analytics. When I found myself working in People Analytics I felt I found my calling. Building my skills along the way I recall a seminal event was when I attended an HR technology conference and saw an amazing presentation by Peter Howes on multidimensional analysis in HR using what was ground-breaking technology at the time. I then went to one of his workshops on how to measure the Return on Investment (ROI) of HR Interventions. Not long after I met Peter's business partner Anastasia Ellerby through a public sector project measuring and benchmarking the effectiveness of the Human Resources function. Through this project I started using their company's products as a customer. The company was Infohrm. With the help of serendipity I started working for Infohrm and the company built the most impactful workforce analytics and workforce planning products, practice and community in the world - it was cool. This then continued through two acquisitions, first by SuccessFactors and then by SAP. What always keep me engaged was working on the cutting edge of innovation in the field, working with companies all over the world and working with a great team of passionate people. We managed to build some innovative stuff, but I found myself in a 90,000+ employee company and it was becoming increasingly more difficult to deliver focused people analytics and planning innovation for customers and I wanted to get back to my passion. Everything I have discussed above dovetails perfectly with what One Model is all about. We are passionately creating the world’s most amazing technology specifically designed to help you deliver people analytics insights that accelerate decision making and drive positive outcomes for your business and a workforce planning capability that helps you plan, forecast and built a talent strategy for today and tomorrow. I’ve now written a much longer blog that originally intended, but hopefully it shows how enthusiastic I am for this domain and for this new role. The One Model team is incredible and the product is awesome. We have shared history and shared values stemming from the original Infohrm company and we do whatever it takes to make our customers successful. I'm grateful for the opportunity and super excited by the innovations we are cooking up and can't wait to share these with you soon. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. Learn more at onemodel.co. About One AI - Trailblazer Trailblazer is One Model's newest way of helping leaders incorporate workforce analytics and distill big data into every HR decision for recommendations that are smarter, faster, and more efficient. The Trailblazer program does this by giving HR teams access to the only openly configurable, HR-focused, automated machine learning engine in the world: One AI. Introductory Offer: Try Trailblazer out for a month - $1,000 Visit onemodel.co/trailblazer-program to learn more.
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3 min read
Stacia Damron
The One Model team is excited to announce that Tony Ashton has moved from Vice President of Product Management at SAP SuccessFactors to be the Chief Product Officer at One Model. One Model is an Austin-based HR technology company, with offices in the United States, United Kingdom, and Australia. Tony will join our Brisbane, Australia office, which headquarters our rapidly growing engineering team. With over seventeen years of experience leading the people analytics product team at SAP SuccessFactors and before that, Infohrm (acquired by SuccessFactors), Tony brings a wealth of product leadership experience to the quickly-growing HR technology startup. “One Model is doing the most exciting, innovative work in the people analytics space today,” asserts Ashton. “No other company in the world is going as deep or innovating as fast as One Model in HR data modeling and the application of machine learning and artificial intelligence to the field of people analytics.” As One Model’s Chief Product Officer, Tony will play an instrumental role in driving One Model’s product innovation strategy and bringing the company's vision to life across our People Analytics Infrastructure, One AI, and Trailblazer offerings. “This strategic hire will support One Model as it continues to remain a market leader in product innovation, development, and people analytics strategy on a global scale,” says Stacia Damron, Senior Marketing Manager. “Scaling our team is the next step; the right hires will be instrumental in the creation and evolution of our offerings, and in our commitment in the alignment of those offerings with both current and future customers needs.” One Model CEO, Chris Butler, is thrilled with this addition to the team. “Tony is without doubt the highest calibre and most experienced product leader in the people analytics domain. I am incredibly excited about the capability that Tony brings to drive our product forward and focus on the success of our customers" says Butler. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. Learn more at onemodel.co. About One AI Tony is instrumental in leading the One AI team. Making HR machine learning transparent and accessible to all is a key differentiator between One Model and other People Analytics tools on the Market. Tony's passion for building the community is unprecidented. Learn more about One AI.
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13 min read
Phil Schrader
As the people analytics leader in your organization, you are responsible for transforming people data into a unique competitive advantage. You need to figure out what it is about your workforce that makes your business grow, and you need to get leaders across the organization on board with using data to make better employee outcomes happen. How will you do that in 2019? Will you waste another year attempting to methodically climb up the 4 or 5 stages of the traditional analytics maturity model? You know the one. It goes from operational reporting in the lower left, up through a few intermediate stages, and then in the far distant upper right, culminates with predictive models and optimization. Here’s the Bersin & Associates one for reference or flip open your copy of Competing on Analytics (2007) for another (p. 8). The problem with this model is that on the surface it appears to be perfect common sense while in reality, it is hopelessly naive. It requires you to undertake the most far-reaching and logistically challenging efforts first. Then in the magical future, you will have this perfect environment in which to figure out what is actually important. If this were an actual roadmap for an actual road it would say, “Step 1: Begin constructing four-lane highway. … Step 4: Figure out where the highway should go.” It is the exact opposite of the way we have learned to think about business in the last decade. Agile. The Lean Startup. Etc. In fact it is such a perfect inverse of what you should be doing that we can literally turn this maturity model 180 degrees onto its head and discover an extremely compelling way to approach people analytics. Here is the new model. Notice the axes. This is a pragmatic view. We are now building impact (y axis) in the context of increasing logistical complexity (x axis). Impact grows as more people are using data to achieve the people outcomes that matter. But, as more and more people engage with the data your logistical burden grows as well. These burdens will manifest themselves in the form of system integrations, data validation rules, metric definitions, and a desire for more frequent data refreshes. From this practical perspective, operational data no longer seems like a great place to start. It’s desirable because it’s the point at which many people in the organization will be engaging with data, but it will require an enormous logistical effort to support. This is a good time to dispense with the notion that operational data is somehow inferior to other forms of data. That it’s the place to start because it’s so simplistic. Actually, your business runs operationally. Amazon’s operational data, for example, instructs a picker in a warehouse to go and fetch a particular package from the shelves at a particular moment in time. That’s just a row of operational data. But it occurs at the end of a sophisticated analytics process that often results in you getting a package on the very same day you ordered it. Operational data is data at the point of impact. Predictive data also looks quite different from this perspective. It’s a wonderful starting point because it is very manageable logistically. And don’t be put off by the fact that I’ve labeled its impact as lower. Remember that impact in this model is a function of the number of people using your data. The impact of your initial predictive models will be felt in a relatively small circle of people around you, but it’s that group of people that will form your most critical allies as you seek to build your analytics program. For starters, it’s your boss and the executive team. Sometime around Valentines Day they will no doubt start to ask, “Hey, how’s the roadmap coming along?” In the old model, you would have to say, “Oh well you know it’s difficult because it’s HR data and we need to get it right first.” Then you’d both nod knowingly and head off to LinkedIn to read more articles about HR winning a seat at the table. But this year you will say, “It’s going great! We’ve run a few hundred predictive models and discovered that we can predict {insert Turnover, Promotion, Quality of Hire, etc} with a decent degree of recall and precision. As a next step, we’re figuring out how to organize this data more effectively so we can slice and dice it in more ways. After that we will start seeking out other data sets to improve our models and make a plan for distributing this data to our people leaders.” Ah. Wouldn’t that feel nice to say? Next, you begin taking steps to better organize your data and add new data sets. This takes more logistical effort so you will engage your next group of allies: HR system owners and IT managers. Because they are not fools, they will be a little skeptical at first. Specifically, they’re going to ask you what data you need and why it’s worth going after. If you’re operating under the old model, you won’t really know. You might say, “All of it.” They won’t like that answer. Or maybe you’ll be tempted to get some list of predefined KPIs from an article or book. That’s safer, but you can’t really build a uniquely differentiating capability for your organization that way. You’re just copying what other people thought was important. If you adopt our upside down model, on the other hand, you’ll have a perfectly good answer for the system owners and IT folks. You’ll say, “I’ve run a few hundred models and we know that this manageable list has the data elements that are the most valuable. These data points help us predict X. I’d like to focus on those. “Amen,” they’ll say. How’s that for a first two months of 2019? You’re showing progress to your execs. Your internal partners are on board. You are building momentum. The more allies you win, the more logistical complexity you can take on. At this stage people have reason to believe in you and share resources with you. As you move up the new maturity model with your IT allies, you’ll start to build analytic data sets. Now you’re looking for trends and exploring various slices. Now is the time for an executive dashboard or two. Now is the time to start demonstrating that your predictive models are actually predictive. These dashboards are focused. They’re not a grab bag of KPIs. They might simply show the number of people last month who left the company and whether or not they were predicted by the model. Maybe you cut it by role and salary band. The point is not to see everything. The point is to see what matters. Your execs will gladly take three pieces of meaningful data once per month over a dozen cuts of overview data once a day. Remember to manage your logistical commitment. You need to get the data right about once a month. Not daily. Not “real time.” Finally, you’re ready to get your operational data right. In the old world this meant something vague like being able to measure everything and having all the data validated and other unrealistic things. In the new world it means delivering operational data at the point of impact. In the old world you’d say, “Hey HRBP or line manager, here are all these reports you can run for all this stuff.” And they would either ignore them or find legitimate faults with them. In the new world, you say, “Hey HRBP or line manager, we’ve figured out how to predict X. We know that X is (good | bad) for your operations. We’ve rolled out some executive dashboards to track trends around X. Based on all that, we’ve invested in technology and process to get this data delivered to you as well. X can be many things. Maybe it’s a list of entry-level employees likely to promote two steps based upon factors identified in the model. Maybe it’s a list of key employees at high risk of termination based. Maybe it’s a ranking of employee shifts with a higher risk of a safety incident. Whatever it is for your business, you will be ready to roll it out far and wide because you’ve proven the value of data and you’ve pragmatically built a network of allies who believe in what you are doing. And the reason you’ll be in that position is because you turned your tired old analytics maturity model on it’s head and acted the way an agile business leader is supposed to act. Yeah but… Ok Phil, you say, that’s a nice story but it’s impossible. We can’t START with prediction. That’s too advanced. Back when these maturity models were first developed, I’d say that was true. The accessibility of data science has changed a lot in ten years. We are all more accustomed to talking about models and predictive results. More to the point, as the product evangelist at One Model I can tell you with first-hand confidence that you can, in fact, start with prediction. One Model’s One AI product offering ingests sets of data and runs them through a set of data processing steps, producing predictive models and diagnostic output. Here’s the gory details on all that. Scroll past the image and I’ll explain. Basically there’s a bunch of time consuming work that data scientists have to do in order to generate a model. This may include things like taking a column and separating the data into multiple new columns (One Hot Encoding) or devising a strategy to deal with missing data elements, or checking for cheater columns (a column like “Severance Pay” might be really good at predicting terminations, for example). There’s likely several ways to prepare a data set for modeling. After all that, a data scientist must choose from a range of predictive model types, each of which can be run with various different parameters in place. This all adds up to scrubbing, rescrubbing, running and re-running things over and over again. If you are like me, you don’t have the skill set to do all of that effectively. And you likely don’t have a data scientist loitering around waiting to grind through all of that for you. That’s why in the past this sort of thing was left at the end of the roadmap-- waiting for the worthy few. But I bet you are pretty good at piecing data sets together in Excel. I bet you’ve handled a vlookup or two on your way to becoming a people analytics manager. Well… all we actually need to do is manually construct a data set with a bunch of columns that you think might be relevant to predicting whatever outcome you are looking for. Then we feed the data into One AI. It cycles through all the gnarly stuff in the image above and gives you some detailed output on what it found. This includes an analysis of all the columns you fed in and also, of course, the model itself. You don’t need to be able to do all the stuff in that image. You just need to be able to read and digest the results. And of course, we can help with that. Now, the initial model may not have great precision and recall. In other words, it might not be that predictive but you’ll discover a lot about the quality and power of your existing data. This exercise allows you to scout ahead, actually mapping out where your roadmap should go. If the initial data you got your hands on doesn’t actually predict anything meaningful in terms of unique, differentiating employee outcomes-- then it’s damn good you didn’t discover that after three years of road building. That would be like one of those failed bridges to nowhere. Don’t do that. Don’t make the next phase of your career look like this. Welcome to 2019. We’ve dramatically lowered the costs of exploring the predictive value of your data through machine learning. Get your hands on some data. Feed it into One AI. If it’s predictive, use those results to build your coalition. If the initial results are not overly predictive, scape together some more data or try a new question. Iterate. Be agile. Be smart. Sometimes you have to stand on your head for a better view. How can I follow Phil's advice and get started? About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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5 min read
Josh Lemoine
2019 Goals: With it being the dawn of a new year, a lot of us are setting goals for ourselves. This year, I set two goals: To write and publish a blog post To run a marathon As the father of two young children, I'm always looking for ways to maximize time management. As I ran on the treadmill recently, a bizarre idea came to me in between thoughts of "why do I do this to myself?" and "this sucks". I might be able to accomplish the first goal and get a start on the second at the same time. See, on my very first run 6 years ago, I brought my phone and tracked the run using a fitness tracker app. Since then, I never quit running and I never stopped tracking every single run using the same app. I have literally burned 296,827 calories building this data set... ...and this data deserves better than living in an app on my phone. As a Data Engineer, I feel ashamed to have been treating my exciting (to me) and certainly hard-earned data this way. What if I loaded the data into One Model and performed some analysis on it? If it worked, it would provide an excellent use case for just how flexible One Model is. It would also give me a leg up (running pun intended) on marathon training. One Model is flexible! One Model is a People Analytics platform. That said, it's REALLY flexible and very well positioned as the definition of "People Data" becomes more broad. The companies we work with are becoming increasingly creative in the types of data they're loading. And they're increasing their ROI by doing so. One Model is NOT a black box that you load HRIS and/or ATS data into that then spits out some generic reports or dashboards. The flexible technology platform coupled with a team of people with a massive amount of experience working with People Data is a big part of what differentiates One Model from other options. Would One Model be flexible enough to allow for analyzing running data in it? Yes. Not only was it flexible enough, but the data was loaded, modeled, and visualized without using any database tools. Everything you're about to see was done through the One Model front end. One Model has invested substantially over the past year in building a data scripting framework and it's accessible within the UI. This is a really exciting feature that customers will increasingly be able to utilize in the coming year. Years ago, as a customer of a People Analytics provider, I would have given my right arm for something like this. That said, as a One Model customer you also get access to a team of experts to model your data for you. What did I take away and what should you take away from this? Along with gaining a better understanding of my running, this exercise has gotten me more excited about running. Is "excited about running" even a thing? I plan to start capturing and analyzing more complete running data in 2019 with the use of a smart watch. I'll also be posting runs more consistently on social media (Strava). It'll be interesting to watch the changes as I train for a marathon. Aside from running though, it has given me some fresh perspective on what's possible in One Model. This will surely carry over into the work I do on a daily basis. Hopefully you can take something away from it as well. If you're already using One Model you might want to think about whether you have other data sources that can be tied to your more traditional People Data. If you're not using One Model yet but have an interesting use case related to People Analytics, One Model might be just the ticket for you. Without further ado, here's my running data in One Model: "Cool - this is all really exciting. How can I get started?" Did the above excite you? Could One Model help you with your New Year's resolution? I can't guarantee it'll help you burn any calories, but you could be up and running with your own predictive analytics during Q1 of 2019. One Model's Trailblazer quick-start program allows you to get started with predictive analytics now. Want to learn more? About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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10 min read
Stacia Damron
Wouldn't it be incredible to predict the future? Let's ask 63-year-old Joan Ginther. She's arguably one of the luckiest women in the world. This Texas woman defied odds to win million-dollar lottery payouts via scratch cards not once, not twice, but four times over the past decade. Her first lottery win landed her $5.4 million, followed by $2 million, $3 million, and then a whopping $10 million jackpot over the summer of 2010. Mathematicians calculate the odds of this happening as one in eighteen septillion. Theoretically, this should only happen once in a quadrillion years. So how did this woman manage to pull it off? Was it luck? I'd certainly argue yes. Was it skill? Maybe. She did purchase all four scratch off cards at the same mini mart. Most interestingly, did it have something to do with the fact that Joan was a mathematics professor with a PhD in statistics from Stanford University? Quite possibly. We'll never know for sure what Joan's secret was, but the Texas Lottery Commission didn't (and still doesn't) suspect any foul play. Somehow, Joan (pictured to the left) predicted it was the right time and right place to buy a scratch off ticket. All we know for sure is that she's exceptionally lucky. And loaded. Most of us have a hard enough time predicting traffic on our morning commute. We can, however, make some insightful predictions for people analytics teams by running people data through predictive models. So, what is HR predictive analytics? Most specifically - predictive analytics use modeling, or a form of artificial intelligence that uses data mining and probability, to forecast or estimate specific outcomes. Each predictive model is comprised of a set of predictors (variables) in the data that influence future results. When the data set is processed by the program, it creates a statistical model based on the given data set. Translation? Predictive analytics allow us to predict the future based on historical outcomes. Let's discuss predictive analytics in HR examples. So predictive analytics can help HR professionals and business leaders make better decisions, but how? Maybe a company wants to learn where they're sourcing their best sales reps so they know where to turn to hire more top-notch employees. First, they must determine whether their "best" reps have measurable qualities. For the sake of this post, let's say they sell twice as much as the average sales reps. Perhaps all the best reps share several qualities such as referral source (like Indeed), a similar skill (fluency in Spanish listed on their resume) or personality trait (from personality tests conducted during the job interview). A predictive model would weigh all this data and compare it against the outcome: the superior sales quotas being hit. The model references the exploratory data analysis used to find correlations across all your data sources. This allows a company to run job candidates' resumes through the model in an effort to predict their future success in that role. Sounds great right? Now - here are the problems to consider: 1) Predictive models can only predict the future based on historical data. If you don't have enough data, that could be a problem. Download Ethics of AI Whitepaper. 2) Even if you do have enough data, that can still be a problem. Amazon, for example, recently scrapped its resume software (which evaluated resumes of current/previous employees to help screen potential ones) because it discovered the algorithm was biased towards men in engineering roles over women, which disqualified candidates that listed any women's organizations on their resume. (And it's not Amazon's fault. It's the data; historically, most men had been in those roles.) Kudos to them for scrapping that. That's why it's so important to use a human capital predictive analysis tool that is transparent and customized to your data vs. another big-box company in your industry. Check out One Model's One AI. HR predictive analysis is helpful, but it's also a process. Are there more applications? What HR-related problems does it solve? Predictive analysis applications in people analytics are vast. The right predictive models can help you solve anything from recruiting challenges to retention/employee attrition questions, to absenteeism, promotions and management, and even hr demand forecasting. The sky's the limit if you have the right tools and support. Time for a people analytics infrastructure reboot Sure - a people analytics infrastructure reboot isn't as exciting as winning the lottery and buying a yacht, but it's really, really helpful in solving questions large corporations struggle with daily. If you haven't used predictive modeling to solve a burning business problem, this might be a great place for your people analytics team to dive in. For One Model Customers - We recommend you push a couple of buttons and start with an exploratory data analysis. More and more companies are beginning to incorporate machine learning technology into their stack, and there's so much value that can be derived. If you're not sure where to get started, just keep it simple and bite off one piece of the puzzle at a time with One Model. One Model is built to turn your general HR team into people data scientists, no advanced degrees required. One Model provides the people analytics infrastructure - aka - it provides a platform for you to import your workforce data from all sources, transform it into one analytics-ready asset, and build predictive models to help you solve business challenges. Our customers are creating customized models and you can too. It's not as intimidating as you might think. It's super easy to get started: One Model will work with you to pull your people data out of any source that's giving you trouble (for example, your Greenhouse ATS, Workday, or Birst). We'll export it, clean it up, and put it all in the same place. It takes just a few weeks. From there, you can glean some insights from it. To learn more about One Model's capabilities (or to ask us any questions about how we create our predictive models), click the button below and a team member will reach out to answer all of your questions! Let's Talk More About Predictive Analytics for HR. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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13 min read
Stacia Damron
What's machine learning? Is it artificial intelligence? Deep learning? Is it black magic, or better yet, just a phrase the industry's marketing folks say to pique your interest? The answer? Let's crack it open. What is it? Machine learning is an application of artificial intelligence (AI) that uses statistical techniques to give computer systems the ability to automatically learn and steadily improve their performance from their experience with the data - all without being explicitly programmed to do so. Think of it this way: it's a program that's automatically learning and adjusting its actions without any help or assistance from humans. Cool, right? How is it used in data analytics? Machine learning is used to create complex models and algorithms that predict specific outcomes. Thus, it's coined as predictive analytics. The predictive models it creates allow the end users (data scientists, engineers, researchers, or analysts) to "produce reliable, repeatable decisions and results" that reveal otherwise "hidden insights through learning historical relationships and trends in the data." [1] Here's what artificial intelligence (AI) and machine learning are not: 1) Glorified statistics. Sure - both statistics and machine learning address the question "how do we learn from data?" In its most basic definition, "Statistics is a branch of mathematics dealing with data collection, organization, analysis, interpretation and presentation." [2] Statistics is a field of mathematics that addresses sample, population, and hypothesis to understand and interpret data. Machine learning, on the other hand, allows computers to act and make data-driven decisions without being directly programmed to carry out a specific task. It involves predictions and supervised/unsupervised learning. Above, supervised learning is explained with apples. Supervised machine learning is when a program is trained on a pre-defined dataset. It's provided with example inputs (the data) and their desired outputs (results), and the computer's goal is to analyze these to learn the rule that maps these inputs to outputs. It can then apply it's knowledge to the learning algorithm to adjust and improve its future predictions about output values. In the graphic above, you provide a data set that teaches the program, "these are apples. this is what apples look like." The desired output in this case is knowing and recognizing an apple. The program learns from this data, and next time, it will be able to identify apples on it's own. Viola! - it has officially been trained. A real world example of supervised learning is predicting a car sale price based on a given dataset of previous auto sales data for that make, model, and condition in that area. Above: unsupervised learning, explained by some tasty fruit. Unsupervised learning, on the other hand, is when a program automatically recognizes patterns or relationships in a given dataset. The algorithm is essentially on its own finding structure in its input, as it's not provided with classifications or labels ahead of time. Above, the raw data is represented with a selection of fruit. In it goes, where the algorithm finds structure in the data (it notices there are some apples, some bananas, and some oddly shaped oranges). It processes this information and clusters these into groups to be classified. The output is shown above as sorted fruits in neatly defined groups: one for apples, one for bananas, and one for the oranges. Unsupervised learning helps: make inferences regarding the data, which; classify hidden structures within the previously unlabeled data. Since unsupervised learning helps discover and classify hidden patterns in the dataset, a solid example would be a program grouping a variety of documents (the documents are the dataset) by subject with no prior knowledge or training. To summarize: while machine learning certainly utilizes statistics, it's a different way of addressing and solving a problem. It's not some magical version of stats that's going to suddenly provide all the answers. On that note... 2) It's not magic that will solve any problem with any data set with 100% accuracy. Machine learning algorithms can only analyze the data they're provided. For example, a machine learning system trained on a company's current customer data might be limited in that it's only able to predict the needs of new customers that are already in the data, eliminating another type of customer demographic that's not present in the data it was trained on. It can also take over any intrinsic biases that lie in the current data. Machine learning isn't perfect. Take Google for example. The tech giant famously struggled with this in 2015, when its Google photo software exhibited signs of accidental algorithmic racism. It made headlines when the machine learning algorithm mistakenly tagged people of certain ethnicities as gorillas. The company took immediate action and removed all gorilla-based learnings from the training data, and the algorithm was modified. Google Photos will no longer tag any image as a gorilla, chimpanzee, or monkey - including the actual animals. While machine learning can make some extremely helpful and enriching business predictions, it's not always going to make accurate predictions. Machine learning is just that - constantly learning. 3) Marketing buzzwords. At this point, journalists are saying "AI" is on it's way to becoming the meaningless, intangible tech-industry equivalent of "all natural." Yes - there are absolutely some companies that claim to have an AI component when they actually do not, just to hype up their product (and shame on them!). But for every one company that's throwing the term loosely around, there's a few more that just don't know any better. Thus AI isn't well defined. As a result, any piece of software that employs a convolutional neural network, deep learning system, etc. is being marketed as “powered by artificial intelligence." Here's some questions you can ask to evaluate if a company truly is has an AI strategy: a) Is the company using machine learning? Artificial intelligence technology uses machine learning. Can they tell you what machine learning algorithms they're using? If you ask a rep this question and you're met with a blank stare, that's a red flag. b) Ask about the data. What data are you using to train your algorithms? Is there enough of it? According to this source, around 5,000 training examples are necessary to begin generating results. 10 million training examples are needed to achieve human-level performance. Also, ask about a company's claim to reliably produce a certain result. How do they generate that number? How do they prevent overfitting errors? c) Get to know the technology and company itself. Was this technology developed in-house? What was the company doing before? Were they always an AI company specializing in predictive, or were they riding on the bandwagon of whatever was cool and trendy before? No one's an expert in something for a few years back, and then all of a sudden an expert in something totally different that's hot right now. Who founded the company, and where does their industry expertise lie? Learn about the current leadership. If you stick with the check-list above to vet AI technology, you'll be able to dig up some answers pretty quickly - and you'll look pretty freakin' savvy while you're doing it. So, how is machine learning being used in the HR space? Well-informed leaders in the people analytics space are embracing AI and budgeting for the resources to incorporate machine learning technology into their HR strategies for the long-term. Machine learning technology can create a variety of predictive models that help companies gain insights and solve challenges in the following areas: Recruiting - Where are you sourcing your best candidates from? Know where your high performers are coming from and get insights into the KPIs their resumes or career histories have in common. Retention & Employee Attrition - Predictive analytics use a company's historical data to determine potential attrition risks prior to their occurrence, giving leadership otherwise unknown insights and an opportunity to take preventative actions. Absenteeism - The Bureau of Labor and Statistics says that in 2017, the average number of days an employee missed annually was 2.8 days. It doesn't seem like a lot, but if your company has 1,000 employees, then that amounts to 2,800 days per year. According to Circadian, unscheduled absenteeism costs roughly $2,650 each year for salaried employees. That adds up to a whopping 7.42 million a year in absenteeism costs. That's a huge incentive to find a solution. Predictive models can help identify patterns and trends in why employees are absent. Would they have been able to complete their assignments as scheduled if they were able to work from home? Are there are lot of absences under a particular manager? Or is a particular department under a high level of stress? The answers may lie in the data. Promotions and Management - What are some inputs in the company datasets that indicate a higher likelihood of minorities receiving promotions or opportunities? How can we encourage more women to apply for or join X department? Predictive models can analyze the data and provide helpful insights into why. People Spend - Predictive models can forecast the effects of any type of spend toward future workforce productivity, whether that's hiring more employees, increasing training and educational opportunities, or implementing new systems. What that means for today's people analytics leaders More and more companies are beginning to benefit from incorporating machine learning technology that supports their long-term strategy. If you're evaluating different tools to solve your people analytics challenges, add One Model to your list of companies to your list. One Model provides people analytics infrastructure - aka - it provides a platform for you to import your workforce data and build predictive models to help you solve business challenges such as the ones listed above (and many more). Our customers can create customized models or use our out-of-the-box integrations. To learn more about One Model's capabilities (or to ask us any questions about our machine learning algorithms and how we create our predictive models), click the button below and a team member will reach out to answer all of your questions. About One Model One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team. [1] "Machine Learning: What it is and why it matters". www.sas.com. Retrieved 2016-03-29. [2] Dodge, Y. (2006) The Oxford Dictionary of Statistical Terms, Oxford University Press. ISBN 0-19-920613-9
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3 min read
Chris Butler
Earlier this year i joined one of The Learning Forum's workforce analytics peer groups and i wanted to share my experience in attendence and why i came away thinking these groups are a great idea and should be considered by every PA practitioner. There are a number of groups that you can take a look at joining including Insight 222, and The Conference Board, but Brian Hackett from The Learning Forum had asked me earlier this year to come and present to their group about what we were doing at One Model. We had come up in their conversations and peer group emails where members had been asking about different technologies to help them in their building their HR analytics capabilities. The Learning Forum is a group of mostly Fortune 2000 companies with a sizable proportion being Fortune 500 organizations, of course i accepted. Our presentation went well and we had some great questions from the group around how we would tackle existing challenges today and where the platform is heading for their future projects. A great session for us but the real value i took away was in staying for the rest of the day to be a fly on the wall for how the group worked and what they shared with each other. Brian had tabled on the agenda some pre-scheduled discussions on what the attendees were interested in learning about and discussing with their peers. The agenda was attendee curated so all subjects were relevant to the audience and provided some structure and productivity to the event. Following was time for members to be able to present on any recent projects, and work they had been conducting in their teams and any valuable insights, outcomes, and advice they could share with the group. This was awesome to sit in on and listen to how others in our space are working, what their challenges are, how they fared, and to do so in an environment of open confidential sharing. It's the spirit of confidentiality and sharing between peers that i felt most made this group able to help and learn from each other that you just don't receive from a run of the mill conference. Practitioner's were here to share, to learn, and openly seek advice from their more experienced colleagues. Presentations ranged from experience using different vendors, to cobbled together projects using spit, glue, and anything else hands could be laid on. I found the cobbled together solutions to be the most innovative, even where a company of the practitioner's size has significant resources the insight's came from innovative thinking and making use of tools that every company has access to. It's these projects of working smart not hard that make me smile the most, and the best part is that it could be shared in a fashion, and truthfulness that couldn't have occurred at a conference, or a public linkedin post. Peer forums provide an educational opportunity that you won't get elsewhere, i highly recommend for all people analytics practitioners. Thanks Brian Hackett at The Learning Forum for letting me present and learn about how your members are learning from each other.
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3 min read
Stacia Damron
Today, at The HR Technology Conference and Exposition, HRExaminer unveiled its 2019 Watchlist - "The Most Interesting AI Vendors in HR Technology." One Model is one of thirteen companies named, narrowed down from a list of over 200 intelligence tools, only 70 of which were invited to provide a demo. One Model was featured alongside several notable vendors including Google, IBM, Workday, and Kronos. The Criteria HRExaminer, an independent analyst of HRTechnology and intelligence tools, selected two winners across five distinct categories: AI as a Platform Data Workbench Microservices Embedded AI First Suite One Model was named as one of two featured companies in HRExaminer's Data Workbench Category and commended for its management of disparate data from disparate sources - specifically the platform's robust Analytics Integration. “Each of the companies on our 2019 Watchlist is demonstrating the best example of a unique value proposition. While we are in the early stages of the next wave of technology, they individually and collectively point the way," said John Sumser, HRExaminer’s founder and Principal Analyst. "Congratulations are in order for the work that they do. The award is simply a recognition of their excellence." Sumser goes on to state, “There are two main paths to analytics literacy and working processes in today’s market. The first is templated toolkits for specific purposes that can give employers a quick start and repeatable/benchmarkable processes. One Model represents the alternative: a complete set of tools for designing and building your own nuanced analytics, predictions and applications.” One Model is currently exhibiting at The Technology Conference and Exposition in Vegas, September 11th-13th. Attendees can visit booth #851 for more information. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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4 min read
Stacia Damron
One Model is keen on ensuring our customers have an exceptional experience interacting with both our software and team alike. That experience begins the moment we meet. Often, the moment that relationship begins is on our website. One Model's platform helps HR and People Analytics teams simplify the messiest of their workforce data, strewn over multiple systems. Our software makes life easier - and our website needs to reflect that simplicity. It needs to be straightforward, easy to navigate, and provide helpful resources and tools to help you continue to grow your people analytics functions. For months, we have been diligently working to create a site that betters your experience - a place that provides you with tools and resources to support you in your data-wrangling journey. Well, now it's official - at the end of Q2, we launched it! The new site has clearly defined solutions for companies looking to scale their people analytics capabilities at all levels - regardless of company size, including resources to get started for evolving teams, and strategies to leverage for more mature people analytics programs. Namely - our new website will more effectively serve those seeking more information regarding people analytics platforms and data warehousing solutions. One Model helps HR departments better support their people analytics team. The new website contains more materials, including white papers, customer testimonials, videos, and data-sheets. Our blog authors helpful tips, relevant articles, best practices, and useful insights for today's data-driven HR professionals and data scientists. The new website includes: Updated navigation better aligns customers with our offerings and core capabilities, reduces the number of user clicks to navigate the website, and directs users to relevant, meaningful content and solutions. List of integrations and partnerships enable users to easily identify integrations that can add value with their current software or platforms. Updated Blog enables users to quickly find applicable, informative content and industry news regarding workforce analytics, data warehouse management, data science techniques, and people analytics programs. More options to connect with the team via numerous information request forms. Additionally, they include more form variation, allowing users to submit requests for quotes, demos, or discussions. Supplementary materials to aid in decision making provide more materials to view, including white papers, customer testimonials, videos, and data-sheets. Career Opportunities showcase open roles and allow job-seekers to apply directly via that page. As our company continues to grow and expand within the US and UK markets, our new website will better represent One Model as we continue to set the bar for excellence in HR data warehouse management and people analytics team solutions. Visit onemodel.co for a comprehensive breakdown of our workforce data solutions. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Its newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data.
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8 min read
Phil Schrader
Last week I was doodling some recruiting graphs in my notebook, with an eye toward building out some new recruiting efficiency dashboards. I was thinking about how requisitions age over time and I got an idea for a cool stacked graph that counts up how many requisitions you have open each month and breaks them out into age buckets. Maybe some supporting breakouts like recruiter, some summary metrics, etc. Something like this: Phil's Beautifully Hand-illustrated Cholesterol Graph (above) This would be an awesome view. At a glance I could see whether my total req load was growing and I could see if I’m starting to get a build up of really old reqs clogging the system. This last part is why I was thinking of calling it the Requisition Cholesterol Graph. (That said, my teammate Josh says he hates that name. There is a comment option below… back me up here!) But then I got to thinking, how am I actually going to build that? What would the data look like? Think about it: Given: I have my list of requisitions and I know the open date and close date for each of them. Problem #1: I want to calculate the number of open reqs I have at the end of each time period. Time periods might be years, quarters, months, or days. So I need some logic to figure out if the req is open during each of those time periods. If you’re an Excel ninja then you might start thinking about making a ton of columns and using some conditional formulas. Or… maybe you figure you can create some sort of pancake stacks of rows by dragging a clever formula down the sheet… Also if you are an Excel ninja… High Five! Being an Excel ninja is cool! But this would be pretty insane to do in Excel. And it would be really manual. You’d probably wind up with a static report based on quarters or something and the first person you show it to will ask if they can group it by months instead. #%^#!!! If you’re a full on Business Intelligence hotshot or python / R wiz, then you might work out some tricky joins to inflate the data set to include a record or a script count a value for each time the reqs open date is before or within a given period, etc. Do able. But then… Problem #2: Now you have your overall count of reqs open in each period. Alls you have to do now is group the requisitions by age and you’re… oh… shoot. The age grouping of the requisitions changes as time goes on! For example, let’s say you created a requisition on January 1, 2017. It’s still open. You should count the requisition in your open req count for January 2017 and you’d also count it in your open req count for June 2018 (because it’s still open). Figuring all that out was problem #1. But now you want to group your requisitions by age ranges. So back in January 2017, the req would count in your 0 - 3 months old grouping. Now it’s in your > 1 year grouping. The grouping changes dynamically over time. Ugh. This is another layer of logic to control for. Now you’re going to have a very wild Excel sheet or even more clever scripting logic. Or you’re just going to give up on the whole vision, calculate the average days open across all your reqs, and call it a day. $Time_Context is on my side (Gets a little technical) But I didn’t have to give up. It turns out that all this dynamic grouping stuff just gets handled in the One Model data structure and query logic -- thanks to a wonderful little parameter called $Time_Context (and no doubt a lot of elegant supporting programming by the engineering team). When I ran into $Time_Context while studying how we do Org Tenure I got pretty excited and ran over to Josh and yelled, “Is this what I think it is!?” (via Slack). He confirmed for me that yes, it was what I hoped it was. I already knew that the data model could handle Problem #1 using some conditional logic around effective and end dates. When you run a query across multiple time periods in One Model, the system can consider a date range and automatically tally up accurate end of period (or start of period) counts bases on those date ranges. If you have a requisition that was opened in January 2017 and you want to calculate the number of reqs you have open at the end of every month, One Model will cycle through the end of each month, check to see if the req was opened before then and is not yet closed, and add it to the totals. We use this for all sorts of stuff, particularly headcount calculations using effective dates and end dates. So problem one was no problem, but I expected this. What I didn’t expect and what made me Slack for joy was how easily I could also deal with Problem #2. Turns out I could build a data model and stick $Time_Context in the join to my age dimension. Then One Model would just handle the rest for me. If you’ve gotten involved in the database side of analytics before, then you’re probably acquainted with terms like fact and dimension tables. If you haven’t, just think vlookups in Excel. So, rather than doing a typical join or vlookup, One Model allows you to insert a time context parameter into the join. This basically means, “Hey One Model, when you calculate which age bucket to put this req in, imagine yourself back in time in whatever time context you are adding up at that moment. If you’re doing the math for January 2017, then figure out how old the req was back then, not how old is is now. When you get to February 2017, do the same thing.” And thus, Problem #2 becomes no problem. As the query goes along counting up your metric by time period, it looks up the relevant requisition age grouping and pulls in the correct value as of that particular moment in time. So, with our example above, it goes along and says, “Ok I’m imagining that it’s January 2017. I’ll count this requisition as being open in this period of time and I’ll group it under the 0 - 3 month old range.” Later it gets to June 2018 and it says, “Ok… dang that req is STILL open. I’ll include it in the counts for this month again and let’s see… ok it’s now over a year old.” This, my friends, is what computers are for! We use this trick all the time, particularly for organization and position tenure calculations. TL;DR In short, One Model can make the graph that I was dreaming of-- no problem. It just handles all the time complexity for me. Here’s the result in all it’s majestic, stacked column glory: So now at a glance I can tell if my overall requisition load is increasing. And I can see down at the bottom that I’m starting to develop some gunky buildup of old requisitions (orange). If I wanted to, I could also adjust the colors to make the bottom tiers look an ugly gunky brown like in the posters in your doctors office. Hmmm… maybe Josh has a point about the name... And because One Model can handle queries like this on the fly, I can explore these results in more detail without having to rework the data. I can filter or break the data out to see which recruiters or departments have the worst recruiting cholesterol. I can drill in and see which particular reqs are stuck in the system. And, if you hung on for this whole read, then you are awesome too. Kick back and enjoy some Rolling Stones: https://www.youtube.com/watch?v=wbMWdIjArg0.
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6 min read
Chris Butler
A few weeks ago I gave a presentation at the Talent Strategy Institute’s Future of Work conference (now PAFOW) in San Francisco about how I see the long term relationship between data and HR Technology. Essentially, I was talking through my thought process and development that I could no longer ignore and had to go start a company to chase down it’s long term vision. So here it is. My conviction is that we need to (and we will) look at the relationship between our data and our technology differently. That essentially the two will be split. We will choose technology to manage our data and our workflows as we need it. We will replace that technology as often as our strategy and our business needs change. Those that know my team, know that we have a long history of working with HR data. We started at Infohrm many years ago which was ultimately acquired by SuccessFactors and shortly after SAP. Professionally this was fantastic, worlds opened up and we were talking to many more organizations and the challenges they were facing across their technology landscape. How to achieve data portability. Over time I was thinking through the challenges our customers faced, a large one of which was how to help grease the wheels for the huge on-premise to cloud transition that was underway and subsequently the individual system migrations we were witnessing across the HR landscape. The pace of innovation in HR was not slowing down. Over the years hundreds of new companies were appearing (and disappearing) in the HR Tech space. It was clear that innovation was everywhere and many companies would love to be able to adopt or at least try out this innovation but couldn’t. They were being hampered by political, budgetary, and other technology landscape changes that made any change a huge undertaking. System migration was on the rise. As companies adopted the larger technology suites, they realized that modules were not performing as they should, and there were still gaps in functionality that they had to fill elsewhere. The promise of the suite was letting them down and continues to let them down to this day. This failure, combined with the pace of innovation meant the landscape was under continuous flux. Fragmentation was stifling innovation and analytical maturity. The big reason to move to a suite was to eliminate fragmentation, but even within the suites the modules themselves were fragmented and we as analytics practitioners without a method for managing this change only continued to add to this. We could adopt new innovation but we couldn’t make full use of it across our landscape. Ultimately this slows down how fast we can adopt innovation and downstream how we improve our analytical maturity. All HR Technology is temporary. The realization I started to come to is that all of the technology we were implementing and spending millions of dollars on was ultimately temporary. That we would continue to be in a cycle of change to facilitate our changing workflows and make use of new innovation to support our businesses. This is important so let me state it again. All HR technology is temporary. We’re missing a true HR data strategy. The mistake we were making is thinking about our technologies and our workflows as being our strategy for data management. This was the problem. If we as organizations could put in place a strategy and a framework that allowed us to disconnect our data from our managing technology and planned for obsolescence then we could achieve data portability. We need to understand the data at its fundamental concepts. If we know enough to understand the current technology and we know enough about the future technology then we can create a pathway between the two. We can facilitate and grease the migration of systems. In order to do this effectively and at scale you had to develop an intermediate context of the data. This becomes the thoroughfare. This is too advanced a concept for organizations to wrap their minds around. This is a powerful concept in essence and seems obvious, but trying to find customers for this was going to be near impossible. We would have to find companies in the short window of evaluating a system change to convince them they needed to look at the problem differently. Analytics is a natural extension. With the intermediate thoroughfare and context of each of these systems you have a perfect structure for delivering analytics from the data and powering downstream use cases. We could deliver data to vendors that needed it to supply a service to the organization. We could return data from these services and integrate into data strategy. We could write this data back to those core source systems. We could extend the data outside of these systems from sources that an organization typically could not access and make use of on their own. Wrap all this up in the burgeoning advanced analytics and machine learning capabilities and you had a truly powerful platform. We regain choice in the technology we use. In this vision, data is effectively separate from our technology and we regain the initiative back from our vendors in who and how we choose to manage our data. An insurance policy for technology. With freedom to move and to adopt new innovation we effectively buy ourselves an insurance policy in how we purchase and make use of products. We can test; we can prove; we can make the most of the best of breed and innovation that has been growing in our space. If we don’t like we can turn it off or migrate-- without losing any data history and minimizing switching costs. This is a long term view of how our relationship to data and our vendors will change. It is going to take time for this view to become mainstream, but it will. The efficiencies and pace that it provides to change the direction of our operations will deliver huge gains in how we work with our people and our supporting vendors. There’s still challenges to making this happen. Vendors young and old need to provide open access to your data (after all it’s your data). The situation is improving but there’s still some laggards. The innovative customers at One Model bought us for our data and analytical capabilities today, but they know and recognize that we’re building them a platform for their future. We’ve been working with system integrators and HR transformation groups to deliver on the above promise. The pieces are here, they’re being deployed, now we need to make the most of them.
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9 min read
Phil Schrader
We’re back with another installment of our One Model Difference series. On the heels of our One AI announcement, how could we not take this opportunity to highlight it as a One Model difference maker? In preparation for the One AI launch, I caught up with Taylor from our data science team and got an updated tour of how it all works. I’m going to try to do that justice here. The best analogy I can think of is that this thing is like a steam engine for data science. It takes many tedious, manual steps and let’s the machine do the work instead. It's not wizardry. It's not a black box system where you have to point at the results, shrug, and say, “It’s magic.” This transparent approach is a difference in its own right, and I’ll cover that in a future installment. For now though, describing it as some form of data wizardry simply would not do it justice. I think it’s more exciting to see it as a giant, ambitious piece of industrial data machinery. Let me explain. You know the story of John Henry, right? John Henry is an African-American folk hero who, according to legend, challenged a steam-powered hammer in a race to drill holes to make a railroad tunnel. It’s a romantic, heart-breaking story. Literally. It ends with John Henry’s heart exploding from the effort of trying to keep pace. If you need a quick refresher, Bruce Springsteen can fill you in here. (Pause while you use this excuse to listen to an amazing Bruce Springsteen song at work.) Data science is quite a bit easier than swinging a 30 pound hammer all day, but I think the comparison is worthwhile. Quite simply, you will not be able to keep pace with One AI. Your heart won’t explode, but you’ll be buried under an exponentially growing number of possibilities to try out. This is particularly true with people data. The best answer is hiding somewhere in a giant space defined by the data you feed into the model multiplied by the number of techniques you might try out multiplied by (this is the sneaky one) the number of different ways you might prepare your data. Oh, and that’s just to predict one target. There’s lots of targets you might want to predict in HR! So you wind up with something like tedious work to the fourth power and you simply should not do it all by hand. All data science is tedious. The first factor, deciding what data to feed in, is something we’re all familiar with from stats class. Maybe you’ve been assigned a regression problem and you need to figure out which factors to include. You know that a smaller number of factors will probably lead to a more robust model, and you need to tinker with them to get the ones that give you the most bang for your buck. This is a pretty well known problem, and most statistical software will help you with this. This phase might be a little extra tricky to manage over time in your people analytics program, because you’ll likely bring in new data sets and have to retest the new combinations of factors. Still, this is doable. Hammer away. Of course, One AI will also cycle through all your dimensional data for you. Automatically. And if you add factors to the data set, it will consider those factors too. But what if you didn’t already know what technique to use? Maybe you are trying to predict which employees will leave the company. This is a classification problem. Data science is a rapidly evolving field. There are LOTS of ways to try to classify things. Maybe you decide to try a random forest. Maybe you decide to try neural nets using Tensorflow. Now you’re going to start to lose ground fast. For each technique you want to try out, you’ve got to cycle through all the different data you might select for that model and evaluate the performance. And you might start cycling through different time frames. Does this model predict attrition using one year of data but becomes less accurate with two years…? And so on. Meanwhile, One AI will automatically test different types of models and techniques, over different time periods, while trying out different combinations of variables and evaluating the outcomes. In comparison, you’ll start to fall behind pretty rapidly. But there’s more... Now things get kind of meta. HR data can be really problematic for data science. There is a bunch of manual work you need to do to prepare any data set to yield results. This is the standard stuff like weeding out bad columns, weeding out biased predictors, and trying to reduce the dimensionality of your variables. But this is HR DATA. The data sets are tiny and lopsided even after you clean them up. So you might have to start tinkering with them to get them into a form that will work well with techniques like random forests, neural nets, etc. If you’re savvy, you might try doing some adaptive synthetic sampling (making smaller companies appear larger) or principal component analysis. (I’m not savvy, I’m just typing what Taylor said.) So now you’re cycling through different ways of preparing the data, to feed into different types of models, to test out different combinations of predictors. You’ve got tedious work to the third power now. Meanwhile, One AI systematically hunts through these possibilities as well. Synthetic sampling was a dead end. No problem. On to the next technique and on through all the combinations to test that follow. This is not brute force per se-- that actually would introduce new problems around overfitting. The model generation and testing can actually be organized to explore problem spaces in an intelligent way. But from a human vs. machine perspective, yeah, this thing has more horsepower than you do. And it will keep working the models over, month after month. This is steam powered data science. Not magic. Just mechanical beauty. And now that we have this machine for HR machine learning. We can point that three-phase cycle at different outcomes that we want to predict. Want to predict terminations? Of course you do. That’s what everyone wants to predict. But what if in the future you want to predict quality of hire based upon a set of pre-hire characteristics. One AI will hunt though different ways to stage that data, through different predictive techniques for each of those potential data sets, and through different combinations of predictors to feed into each of those models…and so on and so on. You can’t replicate this with human powered data science alone. And you shouldn’t want to. There’s no reason to try to prove a John Henry point here. Rather than tediously cycling through models, your data science team can think about new data to feed into the machine, can help interpret the results and how they might be applied, or can devise their own, wild one-off models to try because they won’t have to worry about exhaustively searching through every other option. This might turn out similar to human-computer partnership in chess. (https://www.bloomreach.com/en/blog/2014/12/centaur-chess-brings-best-humans-machines.html) One AI certainly supports this blended, cooperative approach. Each part of the prediction pipeline can be separated and used on its own. Depending on where you are at in your own data science program, you might take advantage of different One AI components. If you just want your data cleaned, we can give you that. Or, if you already have the data set up the way you want it, we can save you time by running a set of state of the art classifiers on it, etc. The goal is to have the cleaning/preprocessing/upsamping/training/etc pieces all broken out so you can use them individually or in concert. In this way, One AI can deliver value whatever the size and complexity of your data science team, as opposed to an all-or-nothing scenario. In that regard, our human vs. machine comparison starts to break down. One AI is here to work with you. Imagine what John Henry could have done if they’d just given him the keys to the steam engine? Book some time on Phil's calendar below to get your HR data-related questions answered. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Our newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data. Notable customers include Squarespace, PureStorage, HomeAway, and Sleep Number.
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5 min read
Stacia Damron
How did Spring cleaning become a thing, and why do we do it? It’s officially March. Daylight savings has us up an hour earlier, the weather’s teasing us by thinking about getting warmer, and most of us are envious of the students enjoying spring break on a beach somewhere. Supposedly, this odd combination of things gets us in the mood to clean house. But there’s research to back it up: according to the experts, the warm weather and extra light are responsible for giving us the additional boost of energy. What is it about cleaning that gets us so excited? Is it the fresh smell of mopped floors? Is is the sigh of relief when you can actually park your car in the garage instead of using it for storage? Or is it the look of shock on your significant other’s face when they realize their 10-year-old socks (the ones with the huge holes) are gone for good? It's kind of weird. Now, before we get too far in - I hope you didn’t get really excited about reading some “spot-free window cleaning tips” or “how to declutter your closet in 12 easy steps.” After all, 1) this is a software blog, and 2) I haven’t mastered either of those things. Spring cleaning is a way to refresh and reset. It feels GOOD to declutter. This is the premise here. Most people associate Spring cleaning with their home - but what if we went into Spring with that same excitement at work as well? What if we wanted to share that same, cathartic feeling with our teams and coworkers? You can! One Model can help you Spring clean your people analytics data and provide your team with access to more insights within your current workforce analytics data. We’re the experts at pulling data from as many as 40 or so sources. We can place it on a single platform (that will automatically refresh and update), allowing your team can see how it all interacts together - in one place. Say goodbye to the days of exporting data and poking around with Vlookups in excel, only to have to manually create the same report over and over again. Using the One Model platform to manage your HR data is akin to having someone come in and untangle 200 feet of Christmas lights (but instead of lights, it’s untangling data from your workforce analytics systems). And when you use our platform, you won't have to untangle it again. How awesome is that? A work-related spring cleaning is even more satisfying than a spring cleaning at home. Honestly, it is. You’re not going to get a promotion from organizing your cookware cabinet. However, at work, you might be considered for one if you detangle your data and save your team hours of their valuable time and resources on preparing data for analyzation. So, if you suddenly get the itch to clean something - I urge you and your HR team to commit to participating in a workforce data spring cleaning. Call it a day, and contact One Model to sort out your data organization problem for you. Same satisfaction, less scrubbing - I promise. Then, go home and turn your Roomba on, knowing you just conquered spring cleaning on both frontiers. Book a demo. Or just book some time to get your HR data-related questions answered. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Our newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data. Notable customers include Squarespace, PureStorage, HomeAway, and Sleep Number.
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6 min read
Phil Schrader
There will be over 400 HR product and service providers in the expo hall at HR Tech in September. A typical company makes use of 8 - 11 of these tools, some as many as 30. And that is wonderful. I love working in HR Technology. Companies are increasingly free to mix and match different solutions to deliver the employee experience that is right for them. New products come to market all the time. And the entrepreneurs behind these products are pretty consistently driven by a desire to make work better for employees. All that innovation leads to data fragmentation. Better for employees that don't work in HR Operations and People Analytics, that is. Because all that innovation leads to data fragmentation. In your organization, you might recruit candidates using SmartRecruiters in some countries and iCIMS in others. You might do candidate assessments in Criteria Corp and Weirdly. Those candidates might get hired into Workday, have their performance reviews in Reflektive and share their own feedback through Glint surveys. This would not be in the least bit surprising. And it also wouldn't be surprising if your internal systems landscape changed significantly within the next 12 months. The pace of innovation in this space is not slowing down. And the all-in-one suite vendors can’t keep pace with 400 best of breed tools. So if you want to adopt new technology and benefit from all this innovation, you will have to deal with data fragmentation. How do you adopt new innovation without losing your history? What if the new technology isn’t a fit? Can you try something else without having a gaping hole in your analytics and reporting? How will you align your data to figure out if the system is even working? This is where One Model fits in to the mix. We're going to call this One Model Difference your Data Insurance Policy. One Model pulls together all the data from your HR systems and related tools, then organizes and connects this data as if it all came from a single source. This means you can transition between technology products without losing your data. This empowers you to choose which technology fits your business without suffering a data or transition penalty. I remember chatting about this with Chris back at HR Tech last year. At the time I was working at SmartRecruiters and I remember thinking... Here we are, all these vendors making our pitches and talking about all the great results you're going to get if you go with our product. And here's Chris literally standing in the middle of it all with One Model. And if you sign up with One Model, you'll be able to validate all these results for yourself because you can look across systems. For example, you could look at your time to hire for the last 5 years and see if it changed after you implemented a new ATS. If you switched out your HRIS, you could still look backwards in time from new system to old and get a single view of your HR performance. You could line up results from different survey vendors. You'd literally have "one model," and your choice of technology on top of that would be optional. That's a powerful thought. A few months later, here I am getting settled in at One Model. I'm getting behind the scenes, seeing how how all this really comes together. And yeah, it looks just as good from the inside as it did from the outside. I've known Chris for a while, so it's not like I was worried he was BS-ing me. But, given all the new vendors competing for your attention, you'd be nuts if you haven't become a little skeptical about claims like data-insurance-policy-that-makes-it-so-you-can-transition-between-products-without-losing-your-data. So here are a couple practical reasons to believe, beyond the whole cleaning up and aligning your data stuff we covered previously. First off, One Model is... are you ready... single tenant. Your data lives in its own separate database from everyone else's data. It's your data. If you want to have direct database access into the data warehouse that we've built for you, you can have it. Heck, if you want to host One Model in your own instance of AWS, you can do that. We're not taking your data and sticking it into some rigid multi-tenant setup at arms length from you. That would not be data insurance. That would be data hostage-taking. Second, One Model doesn't charge per data source. That would be like one of those insurance policies where everything is out-of-network. With One Model, your systems are in-network. If you add a new system and you want the data in One Model, we'll add the data to One Model. If we don't have a connector, we'll build one. One of our clients has data from 40 systems in One Model. 40 systems. In one single model. In its own database. With no fees per data source. So go wild at HR Tech this fall. It is in Vegas after all. Add all the solutions that are right for your employees. And tell all your new vendors you'll be able to hold them accountable for all those bold ROI-supporting metrics they’re claiming. Because you can put all your data into One Model for all your people analytics. You can see for yourself. And if you swap that vendor out later, you’ll take all your data with you. Just don't wait until then to reach out to us at One Model. We love talking shop. And if you happen to like what you see with One Model, we can have your data loaded well before you get to Vegas. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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3 min read
Stacia Damron
The One Model team is pleased to announce its official launch of One AI. The new tool integrates cutting-edge machine learning capabilities into the current One Model platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data. One Model’s core platform enables its customers to import multiple data sources into one, extensible, cloud-based platform. Organizations are then able to take full control of their people and business data, gaining increased visibility and spotting trends in the data that otherwise, would remain unnoticed. Machine Learning Insights like HR Professionals Have Never Seen Before One AI delivers a suite of out-of-the-box predictive models and data extensions, allowing organizations to understand and predict employee behavior like never before. One AI extends upon the current One Model platform capabilities, so now HR Professionals can access machine learning insights alongside their current people analytics data and dashboards. Additionally, the solution is open to allow customers and their partners to create and run their own predictive models or code within the One Model platform, enabling true support for an internal data science function. “One AI is a huge leap into the future of workforce analytics,” says Chris Butler, CEO of One Model. “By applying One Model's full understanding of HR data, our machine learning algorithms can learn from all of a customer’s data and predict on any target that our customers select.” The new tool offers faster insights: it can create a turnover risk predictive model in minutes, consuming data from across the organization, cleaned, structured, and tested through dozens of ML models and thousands of hyperparameters. It utilizes these to create a unique, accurate model that can provide explanations and identify levers for reducing an individual employees risk of turnover. This ability to explain and identify change levers is a cutting-edge capability. It allows One AI to choose a high accuracy model that’s otherwise unintelligible and explain it’s choices to our users. “The launch of One AI will have a huge impact on current and future customers alike.” says Stacia Damron, One Model’s Senior Marketing Manager. “One AI’s ability to successfully incorporate machine learning insights into an organization’s people analytics strategy is significant. It means it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results. By creating more precise models, and augmenting internal capabilities, an organization can better identify cost-saving opportunities and mitigate risk.” The One Model team looks forward to sharing more information about One AI with this year’s People Analytics World Conference attendees in London on April 11-12. Stop by the One Model booth if you would like to connect and learn more. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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20 min read
Chris Butler
We received a lot of interest from Part 1 of this blog post so if you haven't read it yet head over for a summary view of our observations in Part 1. In Part 2 I'm going to give you a brief walkthrough of setting up and running a turnover risk prediction in AWS' machine learning. At the end of this post, I have some further observations about improving tweaking and improving the performance of the base offering and additionally why we chose to move away from these toolsets and develop our own approach. AWS Machine Learning https://aws.amazon.com/aml/ Step 1 - Sign up for an account If you don't have an AWS account, you can sign up through the above link. Please check with your IT department for guidance on using AWS and what data you can upload to their cloud. You may need authorization or to anonymize your data prior to loading. Cost A quick exploration of expected cost so you know what to expect. Current pricing is below. $0.42c per hour for model processing $0.10c per thousand predictions In my experience for a 5,000 employee company, this results in the below 10 minutes processing per model = $0.07c 5,000 predictions = $0.50c $0.57c per model and set of predictions run. I typically will create historical backtests generating a model each month for at least the last two years so I can gauge expected performance and track any wild divergence in model behavior. So let's call it $15 to run a full test (optional). Step 2 - Prepare your Data We'll need a flat .csv file that we can load, it's best to include a header row otherwise you will need to name your columns later in the interface which is just painful. The data you include will be all the data features we want to process and a field that shows our target that we are trying to predict, in this case, terminated that I have highlighted in yellow below. The data i use in my file is generally the active headcount of current employees and the last 1-2 years of terminations. The actives have a 0 for terminated and the terminated records have a 1. For a 5,000 person company with a 12% turnover rate that means I should have 5,000 active (0) records and around 1,200 terminated (1) records. The data features used are important and as you create different models or try to improve performance you'll likely spend a good chunk of time adding, removing, or cleaning up the data in these features. A couple guiding points you'll want to do as you build your file You can't have null values, it will drop the record if there's an empty value in a column. Instead, replace any nulls either with a placeholder (the ? you can see above) or depending on the data field you may want to insert the median value for the column. The reason being is any placeholder will be treated as a distinct value and used in pattern detection, the median instead will treat the record as no different from other median records. If you can create a range, it's often useful to do so at this step especially if you are writing SQL to extract as it will then be repeatable on each data extraction (although there are options to do this in the aws UI later). I will often use both the actual value and the range itself as individual data features i.e. Tenure (years) we would have the number of years say 3 as a column and the range 3-<5 years as a column as well. One will be treated as a continuous numeric value while the other as a categorical grouping. I like to include hierarchical structures in the data features like department, or supervisor relationships, you don't need the whole tree, the top parts of the structure are often redundant but the middle to leaf levels are quite important. You can spend days building features and creating calculations, my general approach is to start with a basic set of features and expand as I can lay my hands on more data or have time to merge in a new data set. You can then at least test how a basic set of features performs which for some organizations can perform extremely well. Adding features can reduce performance and cause overfitting so having a baseline to compare with is always good. Step 3 - Create a Datasource and ML Model The wizards make the process of creating a datasource and a model ridiculously easy. Select "Datasource and ML model" from the "Create new" menu on the Machine Learning dashboard. You'll need to load your data file into S3 (AWS file storage system) and from there you can provide it's location to the wizard and give the source a name. You will likely have a number of datasources created over time so make the name descriptive so you can tell them apart. You'll notice some information about providing a schema file. I do prefer to provide a schema file (see documentation here) as it means i can skip the next step of creating a schema for the file but if you have included a header row in your file you can tell the wizard to use the first row as the column names. You still, however, will need to provide a data type for column so the engine know how to treat the data. You have a choice of Binary - use this where there are only two possible states, our target status of terminated is either a 0 or 1 so it's a binary. Can also be used for other binary types e.g. true/false, yes/no, etc Categorical - perfect for any of the attribute or dimension style of fields i.e gender, age range, tenure range, department, country, etc. This is the most common selection I use. Numeric - any number will automatically be assigned this value but you will want to check it is applied properly to a numeric range i.e. age is correct as a numeric and will be treated as a discrete series but if you leave a department number as a numeric this is going to be worthless (change it to categorical) Text - you really shouldn't have a set of text values for this type of scenario so ignore for now and use categorical if in doubt. If you hit continue from here you'll get an error that you haven't selected a target so go ahead and select the column that you used for your terminated status then hit continue. You'll need to do the same for your person identifier (usually an employee id) on the next screen. The next Review screen will give some info on the number of types etc but there's nothing else to do here but hit continue and move to our model selections. Name your model (usually I'll match the datasource name with a -model or similar to the name). The same with the evaluation. Your biggest decision here is to use the default training and evaluation settings or to use the custom. With the custom you change the amount of training and evaluation data, the regularization type, the number of passes the engine should run over your data to detect patterns and the size of the model itself. For the most part, I've had the most success using the default settings, don't get into the custom settings until you are really trying to fine tune results as you can spend a lot of time here and have mixed results. So select default and move on. You can see the default settings on the review screen, we're going to have a training/evaluation split of 70/30, it will run 10 passes over the data looking for patterns and apply a regularization method (helps to reduce the number of patterns and avoid overfitting). Hit create, grab a coffee, and in a few minutes, you'll have a data source, a predictive model, and an evaluation demonstrating it's performance. Refresh your screen until the model shows as completed. Once complete you can click on the data source id and go explore some of the data source information, I like to view the correlations of each data feature to our target which helps to decide if I should remove features or change them in some fashion. The big piece of info though is the Evaluation result which in the above tells us that the Area Under the Curve (AUC) was 0.944 which as the next screenshot tells you is extremely good (suspiciously good). Click on the result and you'll see the performance metrics Yes you'll want to explore performance The above information set is pretty impressive, if we set our probability score threshold at 0.5 which is the point where a score above will be predicted as a termination and a score below will be predicted as active then we end up with 90% of our guesses being accurate. You can see the other metrics associated here for false prediction rates and you can play around with the sliders to adjust the trade-off score to different levels. Now, this looks awesome but keep in mind this is an evaluation set of historical data and I had spent a fair amount of time selecting and constructing data features to get to this point. In real life the model didn't perform this well, success was more like 70-75% of guesses being correct which is still great but not as good as what you'll see in the evaluation. My guess here is I still have some overfitting occurring in the model. If your evaluation performs poorly you'll want to go look at the info provided, you may have rows or columns being dropped from the data source (explore the data source id), your features may not be relevant, or some other problem has occurred. If your results are too good AUC = 1.0 then you likely have included a perfect predictor in the data features without realising i.e. an employment status or a placeholder department when somebody terminates or is about to terminate, check for something like this and remove. Step 4 - Generate Predictions When ready to generate some real-life predictions you can go ahead and click the "Generate Batch Predictions". You'll need to load a file to S3 for your predictions, this file will be the same as your input file but you will remove the terminated column (our target column) so it will only be slightly different. The contents will be for the people you wish to predict on, usually the current active headcount or if you are testing historically the active headcount at x point in time (if you do test historically your model obviously needs to be generated using data from x-1 day point in time). Use the "My data source is in S3, and I need to create a datasource" go through the same prompts as you did for your training data source and once finished processing you'll have a predictions file to download. This file gives you each person, their prediction value, and the probability score associated. You can load this into your own database or just view in excel however you may wish to consume. Observations and Tweaking suggestions Data Sources Start with a basic set of features and expand over time so you can evaluate how the new data is affecting your models. Some targets and models for organizations respond better to simple models and others need a lot more data features to find predictive patterns. Review the correlations of your attributes from the data source information after the source is created and processed. These will help you decide if a feature is useful and most importantly if you have a feature that is suspiciously predictive that you may wish to remove so that you don't pollute the model. If you are going to continue to experiment and iterate then definitely create .schema file it will save a bunch of time in avoiding setting UI options and make generating new source/models very fast. Try creating some features combining different fields you think may have some relation to each other e.g. Age-Tenure, 30-35_3-<5 yrs as an example of joining two ranges together. The ML will pick up some patterns like this but I've found creating some of these can help. The amount of data I describe early in the post is a little controversial i.e. using the current active headcount and historical terminations. Many data scientists will have issue here for one reason or another. For these people know that yes i have tested a number of different methods of balancing the data set, of oversampling data, and generally constructing to overcome different problems and through testing found in this example case of turnover the changes haven't reliably produced better real-life results. So my advice for people starting out is to just use a simple data set, and allow the toolset to do it's thing, then evaluation what you are seeing by applying your predictions back to your actual turnover. The amount of termination history can impact how a model performs, if behaviors change and you have a long history of terminations then the model may not adjust fast enough to cater for these new behaviors, it does help sometimes to shorten the amount of history you use if you have changing workforce behaviours. I was additionally creating new models every month for this reason as well. Models Always use the default to start with while you figure out the datasource and features being used. No point playing around with advanced settings when you can extract the most gains from adding or altering data features early on. If you suspect overfitting and you've looked at all your features for anything suspicious then try a higher level of regularization in the advanced settings, you should still be able to leave the other settings at their default. I've not had Evaluations Use them as an indicator that the model is doing its job and not perfectly fitting and not severely underfitting the data. In general aim for a AUC between 0.75 and 0.95 and you will generally do well. Adjust the score threshold to focus on precision if you want to reduce the number of people predicted as going to terminate (see next section). Using Predictions Generally, I'll take my predictions output and ignore the binary terminated/active column and just use the probability score column. With this I can create my own risk categories where I can bucket people into Low, Medium, High Risk categories. The high risk people may be only the top 100 or so people that I have a high confidence are at risk. Particularly if you are going to focus on a group of people you probably want to focus on a smaller group to start with. If creating your own risk buckets i will plot out these scores and the actual results and decide which scores fit into each buckets. To do this you need to test historically to see how the model performs and to help guide your decision. Watch the model and it's results over time, don't do anything about the results just yet but try to understand how it is performing and if you can be confident in what it is predicting. MOST IMPORTANTLY - if you have enough confidence to start putting retention strategies in place with these people at risk, you must record this action. The action or lack of action needs to feed back into the model as it may affect behaviors and it's absence from the model will pollute its accuracy over time. I generally describe this as my back to the future theory of turnover risk, if you take an action and the model doesnt know about it you are effectively changing the past and destroying it's prediction of the future. Why we didn't use these tools ourselves The toolsets available from AWS, Google, Azure are fantastic easy entry points to start using your data in a predictive fashion. For One Model though they did not provide enough levers to pull when data or workforce behaviors don't fit into the out of the box view from these simplified toolsets. We needed a solution that would allow us to roll into any customer, evaluation all data for that customer, test through thousand of models, and build the most effective predictive model for any target. What's more, we wanted to open this capability to our customers whether they wanted to create their own models in a few clicks or if they had their own data science team and they wished to run their own predictive or statistical models in our infrastructure. We couldn't achieve these objectives and we had to build our own approach that gave us this flexibility. One AI the new name for our augmentations is the result, and I obviously am biased but it is truly amazing. One AI is a collection of advanced calculations (feature engineering), data extensions (commute time, stock price, social data, etc), and the application of our automated machine learning frameworks. It can concurrently test thousands of models and select the most accurate model for the target and the customer's data set. One problem it may choose a basic decision tree, for the next it will decide a neural network works best, and it's able to do this in minutes. The customer though still has the ability to adjust, customize, and put their own stamp on the models in use. One of the biggest drawbacks of the black box methods though is that you have very little explanation as to why a prediction is made, this meant we couldn't provide our customers with the reasons why a person was at risk or what to do about it. In One AI we've built an explanation and prescriptive action facility to be able to show for each person the reasons why their prediction was made and what the biggest levers are to change this prediction. We'll be officially announcing One AI shortly and making available collateral on our website in the meantime if you would like to talk about our framework sooner please contact us or
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7 min read
Phil Schrader
People often ask us, "What makes One Model different?" Well...there's a lot we could show and tell. We've decided to respond with a series of blog posts covering each and every reason. Read on for more! You can't do this with Tableau. One Model features the most advanced role-based security system of any analytics application. It has to. People data is often the most complex and most sensitive data in an organization. Through 15 years of experience working with People Analytics teams and knowing how they wish to provide access, we built a security methodology that caters for all scenarios and fills the complex gaps that other vendors ignore. One Model allows administrators to define custom security groups and designate fine-grained application permissions to each: Can these users create dashboards or just view them? Can they even filter dashboards? Can they drill down to detail? Can they use the Explore tool? Can they build their own metrics? Can they access a metric in one population but not access it in another without changing roles? At the data layer, One Model group permissions control access at both the column level (which data elements a user can see) and the row level (which records of the data element a user can access): Can they see a given department’s data? Do they have access to compensation metrics? Can they cut those metrics by Grade or Position? If they drill in to that data, can they see person-level detail? Better still, One Model security roles understand each user’s relationship to the data. Who reports to whom, for example. That means you could grant all the leaders in your organization drill-down access to their own team members with a single contextual data rule that follows them through the organization as their data changes. Done. Zero maintenance required. Multiple roles merging together to provide the exact level of access for each user regardless of whether they're a HRBP, executive, or director with complex reporting lines. This is not something you can achieve with tableau, qlik, or any other vendor in our space. They come close but they don't understand the relationship between a user and the data itself, which results in constant role security maintenance -- if the desired access can be achieved at all. Why it matters Most teams have self-service is part or goal of their People Analytics Roadmap. If you want to deliver self-service with HR data, you’ll need to effectively and sustainably manage fine-grained sets of permissions like the ones described above. Here’s a look at what is possible with the right role based security capabilities. Let’s say that you’ve developed an integrated recruiting effectiveness dashboard. Your business leaders, recruiting managers, and HRBPs all have access to this dashboard. Based on aggregate data, your business leader can see that the new candidate assessment is, in fact, doing a great job predicting high performing new hires across the company. She drills into her own team’s details and scans through a few examples. This builds her confidence both in the assessment tool and in the dashboard. She’s likely to come back and use other dashboards in the future. The recruiting manager, looking at the same dashboard, is excited by the overall results, but wants to see if this assessment result is having a negative impact on protected groups of candidates in the hiring process. Given her role, you’ve given her access to aggregate slices of demographic data. She uses dashboard filters to cut the data by gender, age, and ethnicity without having to request a one-off ad-hoc report. She’s ready when the topic comes up in a meeting later that day. She thanks you the next time you see her. The division’s HRBP has similar ideas but her security clearance is more complex. Because her division is split across countries and, due to local laws in one country, she's not allowed to view performance ratings, or conduct age and gender analyses, which are seamlessly unavailable for this population. With this limitation in place, she wants to take things a step further in the One Model Explore tool and analyse a recent change to recruiting practices. She combines assessment results and termination data along with her most recent employee survey results. The results are so interesting that she reaches out to you. “Hey, my termination rates are down. We think we’re making better hires based on this new assessment tool, and employee satisfaction is up as well. These are all good signs, but can you figure out which results are driving the others?” After a cursory analysis, the next step is to prove there is a correlation and quantify its impact with the built-in OneAI machine learning suite. Awesome. Isn’t this scenario why your company funded the program in the first place? Without advanced role-based permissions? Well, you probably know that story already. It starts with a generic, one-size fits all dashboard. The plot thickens with the arrival of ad hoc reporting requests and vlookups. And the story ends with… well… more ad hoc reporting and vlookups. If this is something that excites you, let's talk. Click the button below to schedule some time with a One Model team member. We can answer any specific questions you may have, or just chat about role-based permissions (if that's what you're into). About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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4 min read
Stacia Damron
Find our team in a city near you, and stop by in person to learn more about our workforce analytics solutions. February 9, 2018 - Austin, TX - The One Model team recently returned from the People Analytics and Future of Work (PAFOW) in San Francisco, where we participated as a key sponsor and speaker. There, our CEO, Chris Butler, was invited to announce a preview of our latest feature: One AI. (Above) One Model CEO, Chris Butler, announces One Model's newest tool: One AI, at PAFOW in San Francisco. One AI is a huge leap into the future of workforce analytics. Finally - there's a tool that makes machine learning readily accessible to HR professionals . By applying One Model's full understanding of HR data, our machine learning algorithms can draw a parallel, predicting any target that our customers select. For example, this means a turnover risk predictive model can be created in minutes; consuming data from across the organization, cleaned, structured, and tested through dozens of ML models and thousands of hyperparameters to select a unique, accurate model that can provide explanations and identify levers for reducing an individual employees risk of turnover. Our Next Stop: London The One Model team will be showcasing One AI at the People Analytics World Conference in London this April. We invite HR professionals, people analytics experts, and partners to join. Come find the One Model team and learn more about our workforce analytics software for HR professionals and data scientists. If you'd like an opportunity to meet the team in person and learn more, we'll be attending the following events later this year: People Analytics Conference - London, England - April 11-12, 2018 HR Technology Conference and Expo - Vegas, NV - September 11-13th, 2018 More events, TBD. “As One Model continues to expand our client base in the U.S. and abroad, we’re looking forward to participating in more international HR, data science, and AI events,” says One Model’s Senior Marketing Manager, Stacia Damron. “Both domestic and international trade shows have helped us showcase our workforce analytics solution to a broader, more diverse audience, and they offer us an opportunity to foster and maintain valuable relationships with clients and partners alike." About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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Featured
11 min read
Chris Butler
“We scoffed when you predicted he would leave, six weeks later he was gone. Never in a million years would I have said he would leave ” — One Model AWS ML Test Customer Prediction is becoming a Commodity I've been meaning to write this post for a couple of years now after first testing AWS machine learning tools for use with our customer's data sets. Prediction is becoming commoditized by highly available and inexpensive tools like AWS Machine Learning, Google's Cloud Machine Learning Engine, and Microsoft's Azure ML platform. It is now easy to take advantage of machine learning at a ridiculously low cost to the point that anyone can pick it up and start using the toolsets. For HR this means any analyst can cobble together a data set, build a predictive model, and generate predictions without a data science team and no advanced knowledge required. Further below I give a rundown on how to create your own attrition risk model and predictions using Amazon's machine learning service but first, I'll discuss some of the observations we've had in using the service. When everything works well Right out of the gates I had a good experience with using these toolsets, I loaded a fairly simple data set of about 20 employee attributes (known as data features) and ran through the available UI wizard creating a predictive model. Even before generating a set of predictions the data source and model created provide some interesting information to look at, correlations, and a test set of predictions to see how well the model was expected to perform. You can see in the images above an example of the data correlations to target (termination), and the performance of the model itself in a test evaluation. An encouraging first step, and so far I've spent about $0.05c in processing time. Loading a data file of employees that I wanted to run a prediction on and a couple minutes later we have a probability score and prediction for each person in the organization. The performance wasn't quite as good as the evaluation test but it was still quite significant, I ran this test on a historical dataset (data as at one year ago) and could check the real-life performance of the model using actual terminations since that time. It wasn't bad, around 65% of people the model predicted as a 1 (terminated) ended up leaving the organization. This was on a data set that had a historical termination rate of ~20%. With some minor tweaking adding additional data features, removing some others that looked problematic and running the models and predictions monthly to incorporate new hires we pushed the performance up to an average of 75% over the following 12 months. That means 75% of the people the machine said would leave, did so in the next 12 months. Not bad at all. For one of our customer tests, we found 65 high performing employees that were at risk of leaving. That's a turnover cost equivalent to at least $6,000,000 and this was on the first run only two weeks after they started our subscription with us. In fact, if they could save even one of those persons from leaving they would have well and truly paid for our subscription cost let alone the $15 it cost me to run the model. I mocked a dashboard on our demo site that was similar to that delivered to the customer below. Since testing with other real world data sets I have the below observations about where the AWS tools work well. Works really well on higher turnover organizations, you simply have more patterns, and more data to work with, and a statistically greater chance of someone leaving. Turnover greater than 15% you could expect good performance. Simple feature sets work well with high turnover organizations i.e. employment attributes, performance, etc. I would however always add in more calculated features though to see if they correlate e.g. Time since last promotion/transfer/position change, Supervisor changes, Peer terminations etc. The less turnover you have the more important these additional data features are. A model generated across the whole company's data worked just as well as a model generated across a subset i.e. sales, engineering. Great, for the most part we could generate a single model and use against the whole organization. Ignore the categorical prediction 1 vs 0 and instead use the probability score to create your own predictions or buckets, i've found it easier to look at and bucket populations into risk categories using this method and obtain populations with probability values that we can focus on. This is particularly useful when we want to bucket say the top 600 or the top 12% of our population to match our historic turnover. I found the best test of performance before applying to current data was to run one model every month for a historical period say the last two to three years (24-36 monthly models), load the results into a database and be able to see how the models perform over time. It allows you to take a wider view of the models performance. When everything falls apart Well not quite but when it doesn't perform as well as you might expect, conversely to the above i've run tests on organizations where i haven't seen the same stellar outcomes. Or where the model works really well for a period of time but then dives off a cliff with no explanation as you can see in the below image. This is an example where we had a model that was perfoming really well until the turnover behaviour changed completely and was no longer predictable with the data we had feeding the model. This could happen with any model but we had particular issue trying to overcome with limited set of levers we could pull in AWS. You can see that the new behaviours were being identified but it took time to re-learn and regain it's performance. A note on the metrics use below; I like to use Termination Rate - Annualized as a measure of performance because typically we run and make these predictions monthly so the populations in each bucket are changing as new hires are made, terminations leave, and peoples attributes change which may make them change risk categories. This is the reason why you will see rates exceeding 100% as the denominator population is being refreshed with new people in the risk bucket i.e. Termination Rate - Annualized: High Risk = Terminations: High Risk / Average Headcount: High Risk (annualized then of course) Generally I've seen lower performance working with organizations who have low turnover (<8%) or are just relatively small. There just were not enough reliable patterns in the data to be able to obtain the same level of gains that we see in higher turnover, larger organizations. You can increase performance by adding more features that may show additional patterns but in the testing we did we could only get so far with the data available. However while we had lower performance we still saw turnover rates (terminations/average headcount) of high risk populations around the 40-60% mark which is still significantly better than the average turnover and provides a population to go and work with so the effort is not wasted. To counter some of this you can use the probability scores to create risk buckets where you can then focus on precision of the prediction sacrificing recall (number of terminations captured). In this way you can be quite confident about a population even though it will be a smaller subset of the terminated population. Ultimately we didn't use these tools in a production capacity because we needed to overcome a different set of challenges that individual organizations don't have to deal with i.e. how to deliver at scale for any customer, with any size (even small companies) and shape data set, to do so regularly, and always be at the highest level of accuracy. The automated tools available just couldn't meet our requirements and i'll discuss some of those reasons below, so we had to build our own Machine Learning for HR which we will release some content around soon. In People Analytics the most common use case of prediction is still turnover as it represents a huge cost to the business and data is for the most part readily available. Next we will spin up a model in AWS and generate some predictions. Stay Tuned for Part 2 If you would like to talk about our framework sooner please contact us or
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7 min read
Chris Butler
The biggest change in people analytics that surprised me in 2017 wasn't any new leap in technology or shiny new object. For me, it was the growth in interest and uptake by smaller organizations. Traditionally this space has been reserved for companies that had statistically significant populations and budget's to match them. They could hire a team to build and grow HR analytics and had discretionary budget to spend on tool-sets to assist them. A few years ago you rarely would have seen a company with less than 5,000 employees spending resources on these initiatives. The last couple years and this year in particular however we've seen a substantial increase in appetite from companies with less than 1,000 employees. In fact, the smallest company I spoke to in 2017 was barely over 100 employees. These companies are not just kicking tires either, they are purchasing technology, hiring people analysts, and making out sized gains in capability when compared to their larger peers. Budget is being procured and goals are being set that would shame many large organizations. Did you know that 35% of our new customers in 2017 came from organizations with less than 1000 employees. What are they doing? Basically the same activities as larger organizations. They are gathering and making sense of all their people data, delivering reporting/analytics to their business users, and moving into advanced analytics across the hire to retire spectrum. Goals are lofty, and without a significant organizational burden they are able to move fast. Time from analysis to decision to action is on the order of days if not hours. System complexity is a major challenge. Smaller companies still struggle with the same challenges however, often the system complexity is the same or sometimes more complex than larger organizations. We have observed that often smaller companies have collected a number of systems to help make life easier but generally they're sourced from a rainbow of vendors and often are the bright new shiny applications that don't have the maturity to provide the level of data detail and access that a more established vendor may provide. At this size it is also much easier to transition to a different product or spin up a new technology which while collecting some great data can make life much more difficult to merge these systems together for analysis. Overall i think many organizations at this scale have much richer data than many of their larger peers but it is more fragmented. How can you possibly find statistical significance with a small population? From my conversations with these companies this is a known factor in how they conduct analysis and interpret their findings. It's not a showstopper but just another data point to be kept in mind. We personally had to adapt some of our machine learning prediction functionality to be able to cater for smaller companies. A predictive attrition model for example generally works better the more terminations you have, with a small population of terminations you typically won't do so well. "For smaller organizations we now employ a method of synthetically creating data that is not the same as, but is representative of the original data set - essentially making a 500 person company look like it's a 50,000 person company." One Model has had great success in employing this to very large enterprises as well to enhance the behaviors and patterns seen in the data. There are options to overcome the smaller data set challenges. Most people we know in this space don't care that they only have 500 people, because our software allows us to deliver value to their organization irregardless. Is it a passing fad for smaller organizations? I don't know yet, but I don't think so. We have to keep in mind the number of smaller organizations is orders of magnitude larger than larger organizations and it really is only the most forward thinking of these companies that are undertaking these activitites. Typically (but not always) it's the companies that are growing, doing well, and apt to hiring people who are interested in using data to support decisions (think technology, and bio-tech). This is not every smaller company, but my belief is that the entry point for HR analytics is becoming earlier, and earlier in an organization's growth curve. What does it mean for us in larger organizations and the space in general? Increased demand for HR analytics skillsets from smaller companies (more choice in who you work for). Conversely, if you need to hire for your people analytics practice don't discount people who have worked for smaller companies - you may find some great candidates in this pool. Any human capital competitive advantage you have in being a larger company is being assumed by your smaller competitors. An increase in the number of vendors supplying technology in the space, small companies are typically the entry point for new startups. With availability of technology targeted at the systems smaller companies use, expect the adoption rate and therefore the effects of the first two points to increase. We're going to see different examples for the use of people analytics at smaller scale companies, these will be interesting and learnings may even apply to smaller business units within large organizations. We're giving away free 30-min consultations to help companies take charge of their HR people analytics and data in 2018. Would like like to learn how we can help you take your people analytics and workforce data to the next level? Take advantage! Click here, or click on button below to schedule a complimentary consultation. One of our team members will get in touch with you and speak with you one-on-one to address any specific challenges your company might have. Cheers to a new year, Chris Butler One Model, CEO
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6 min read
Mike West
You may not think your company is yet doing People Analytics but here is one way your employer is already doing so, poorly. Employers routinely use Credit Scores to screen candidates. “47% of employers conduct credit checks to screen potential new potential hires” (Society of Human Resource Management) That is to say, they use a score designed to measure credit default risk and apply it to employment screening on some dubious premise that this may be predictive of success, good judgment or good moral character in the context of employment. What is a credit score? A credit score is a numerical expression of an analysis of a person's credit files, to represent the creditworthiness of the person. Lenders, such as banks and credit card companies, use credit scores to evaluate the potential risk posed by lending money to consumers and to mitigate losses due to bad debt. Lenders use credit scores to determine who qualifies for a loan, at what interest rate, and what credit limits. A credit score is based on a history of financial transactions sourced from credit bureaus. A credit score applies a mathematical "algorithm" to a profile and history of transactions to categorize a loan candidate so that financial institutions can make a better decision about whether or not to loan someone money, and/or to make a better decision about how to price a loan to loan money profitably, with regard to risk.A bank may offer to lend a low credit score candidate a loan but at a higher interest rate. This allows the bank to maintain a certain level of profitability from different risk segments or deny working with certain risk segments all together. Why do employers use credit scores to screen employees? In employment the credit score is presumably used for a similar high level purpose as banks (control risk). In the case of employment you are trying to reduce the risk of a "bad" hire or stated inversely, reduce the risk of not selecting a good hire. Employers have more applicants than available positions and they limited time to consider each candidate, so they want some quick means to make a decision. Or sometimes we make a decision but apply additional rigor through screening devices to prove to ourselves we made the right decision. Directly from a credit reporting firm’s website : Credit Scores can help employers “make decisions quickly and easily when deciding on potential candidates” (TransUnion) To shed additional light on this practice, I bring up a conversation at a recent U.S. Senate hearing… “What is the evidence that there is strong correlation between accessing an applicant's credit history and eventually problems of loss to the employer?” Senator Rosenbaum (Oregon) “We don’t have any research to show any statistical correlation between what is in somebody’s credit report and their job performance or their likelihood to commit fraud.” Erick Rosenberg (TransUnion) Here are some other facts to consider about using Credit Scores for employment screening: - 1 in 4 credit reports have been found to have an error - 1 in 20 have been found to have serious errors - Other issues : “52% of all debt on credit reports stems from medical expenses” CFPB 25% error rate? If you thought your core HR data has problems, maybe it doesn't sound so bad now? Scores are being used as a reflection of "character" but if the medical debt statistic is correct, in half or more cases credit scores may be low because of uncontrollable circumstances - how is that reflective of character? Imagine that as a result of chance circumstances you are in dire need of money and you are also as a result refused a means of obtaining money? Does this practice make any sense? It's predictive but you don't need any math at all to predict this outcome - it's a self fulfilling prophecy - self fulfilling prophecies are convenient if you sell predictions. How do the credit reporting agencies and employers defending this practice? The "all else equal" claim. That is that "all else equal" this is a better way of making a decision than nothing. Is it really? If we can find evidence of errors, systematic bias and NO evidence that there is any relationship with job performance - is this really a better way of making a decision than a coin flip? Or why not do the work to find something else not equal that has less errors, less systematic bias or more evidence of relationship to job performance? Why not find a better way of making things not equal? We have always done it this way or this is how others are doing it. Come on, is this really a good reason? Is this how to make good business decisions? This is not how any great business decisions are made, ever. It seems like a "plausible method" of making decisions by logic or rational argument. Here are some theories that are at least as plausible as the theory that credit reports may be good predictors of performance … Maybe because we really have no idea how to screen a candidate for characteristics that relate to performance without doing this work directly … Maybe because we want to find a way to systematically discriminate against populations that come from impoverished communities, thus discriminating against a high percentage of minorities, without doing so directly… Or we don't delve into the details to see how this math may or may not play out and we don't care. Maybe because we want to eliminate candidates who have high medical expenses and would cost our health plans money, without doing so directly… (This actually seems like the most mathematically plausible scenario to me) I ask again, why are you using credit scores to screen job applicants? How about this, why don't we actually do the work to see what factors drive performance in our organization and/or isolate with data how we can increase the probability of high performance, regardless of starting individual characteristics. To me this is a better way of making decisions and a better way to run a business. Thank you to John Oliver (yes, John Oliver the comedian) for highlighting these issues in a clear, emotionally charged and entertaining way on his show : Last Week Tonight (HBO) – Be advised - wear headphones - there is some language in this that may be offensive and not safe for a work environment. I would argue the practice of using credit reports as screening devices is equally offensive and unsafe. I wish that each employer would put as much time researching the practice of using credit reports as employment screening devices as an HBO comedian did. Seems like it would be even more useful to us to know these things than it is for him, is it not?
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12 min read
Mike West
“Half the money I spend on advertising is wasted; the trouble is, I don't know which half.” Henry Ford, Lord Lever, John Wanamaker... People often ask, “Does People Analytics work?” These are smart people, who genuinely would like a straight answer to an honest question. I will sometimes start a conversation about People Analytics with something like this: did you know that in the United States alone, companies currently spend about 7 trillion on employees in Payroll*? My experience has been that how, where, when and why money is spent matters and results will vary on how well or poorly money is invested. Unfortunately having worked in HR for over 15 years I can tell you that more often than not decisions affecting how people are : a.) coincidental b.) arbitrary, c.) biased or d.) political (by this I mean rooted in conflict – the expression of personal or group advantages and disadvantages). None of these imply a reason that is good for doing business, or people or the economy. (*It is probably over 10 trillion when you include the cost of Benefits and other Perks) Before you can say whether or not People Analytics’ works, you need to know what its job is. We can gather from the name, People Analytics is the application of analysis to people. O.k. but what’s the goal of People Analytics? How can you say whether it works if you don’t know what it’s supposed to achieve? In prior People Analytics Q/A posts I attempted a definition - I was looking for a unique combination of words to represent the essence of a complex concept simply without missing what is different about it. This is what I came up with:People Analytics is the systematic application of behavioral science and statistics to Human Resource Management to achieve probability derived business advantages. I chose these words deliberately but admittedly a little too deliberately to roll off the tongue in casual conversation. So let’s take a step back and talk about it. More simply put, People Analytics is the application of analysis to people in a business. For the sake of discussion we are going to put "people in a business" in the realm of Human Resources. To understand People Analytics lets first understand what is the job of HR? Most people think of HR as a series of low-level administrative activities: record-keeping, recruiting, benefits administration or as the legislation compliance arm of the government that sits in your organization. There is an element of truth to this. No organization could exist for very long without some attention to those things, however you think this is all HR is you simply have not been exposed to HR in a successful, large, modern organization. The need for a dedicated HR function, historically, and for any given organization, occurs when we start to go from something small – where you can know everybody and everything that is going on in an organization - to something big – when it is no longer possible for one person to know everybody and everything that is going on. If at some point no one person can see clearly what is actually going on the organization will grow into chaos. The reaction to chaos now and forever will be, “My God we need to get control over this.” e.g. Ultimately successful organizations eventually arrive at, "We need a Human Resource function". At its essence, HR is about controlling the chaos of organization growth. Control doesn’t sound very exciting. A more exciting way of framing it is, if your investors wishes comes true (growth), then Human Resource Management is your reward. HR is a necessity as a consequence of growth, and growth is the primary way businesses derive value for shareholders over the long run, then it might be accurate to describe HR as both a consequence of growth and a cause. Yes, just as eggs and chickens go, so goes HR. It is a great mystery, however in the case Kentucky Fried Chicken I will abdicate that I think the fried chicken came first, and then HR came along later. The idea that having an HR function may be a good thing for an organization begins with the need to create efficient processes to expand the business and eventually evolves into a more expansive appreciation for what intelligent employees and management expect out of an organization. Tending to The Employment Relationship The main thing everybody in an organization has in common is that we are all getting paid– that makes it a job - otherwise we probably wouldn’t be together 8 hours per day, 5 days per week, 50 (+/-2) weeks per year or whatever you do. You may like each other but you probably don’t like each other that much! The other thing we all have in common is that we are at this job voluntarily – beyond acknowledgement that the dissolution of direct slavery is a good thing – we can all thankfully acknowledge labor markets can sometimes be competitive and therefore opportunities for us abound. There are two sides to the equation and both sides make choices. The relationship between an employee and an employer is a rich interaction that can be understood through many different lenses (Psychology, Sociology, Social Psychology, Labor Economics, Anthropology, etc.) however these interactions boil down to decisions and consequences. Decisions are at the crux of our interaction. The leadership of an organization can truly decide to treat people however they want to but they don’t get to decide if people come or go as a consequence – the people decide this. The people also decide the level of creativity and effort they are willing to exert on behalf of an organization. In the short term, none of these interactions may appear to matter - in the long-term it is clear they always do. Achieving clarity in decisions to achieve desired outcomes is the entire point of management. HR wants a seat at the business table, but the truth is they were silent participants all along. Strategic Human Resource Management After we get the basic blocking and tackling of organization under control modern Human Resource Management extends into the macro-concerns of the organization regarding structure, quality of talent, culture, values, matching resources to future needs and other longer-term people issues related to the organization’s plans – we call this group of activities Strategic Human Resource Management. Strategic HRM gives direction on how to build the foundation for strategic advantage by creating an effective organizational structure and design, employee value proposition, systems thinking problem diagnosis, and preparing an organization for a changing landscape, which include new competition, downturns and mergers & acquisitions. Sustainability, diversity, corporate social responsibility, culture and communication also fit within the ambit of Strategic HRM by reflecting chosen organizational values and their expression in business decision-making. (loose credit to the Society of Human Resource Management for this) If that two paragraph description of Strategic HRM sounds like something straight out of a textbook, that is because it probably was. You and I both wish I would have a better mental firewall. Basically, I’m saying that if you are doing it right the purpose of HR is not really about specific activities or compliance; it is about enabling business growth and as you do that it about designing an organization for competitive advantage. Good HR should help extend the life of organizations by helping them extend the reach of what they do or grow better at what they do over time. Here is a brutal fact : over the long run most organizations fail If over the long run most organizations fail – are you getting a clear picture of how good of a job we are do with this strategic HRM stuff? Then again, maybe it is not HR’s fault, maybe HR had great things to say that were not heard. What is the goal of People Analytics? In 3 words : do HR better. In more words: help leaders and employees make better decisions together – reinforcing organization based competitive advantages - which result in sustained organization growth over time. What is the job of People Analytics? People Analytics provides a means to see and explain what is going on inside of an organization. People Analytics provides a framework to give HR’s disjointed practices a reason, coherence and direction. People Analytics also gives HR a more powerful language to communicate with others. Does People Analytics Work? Here is an interesting 18 minute macro way of answering this question in the form of a Ted Talk : The Surprising math of Cities and Corporations : by Geoffrey West Or you can formulate your own answer in less time than that thinking about the following questions… What is it worth to you, to find out what characteristics in a manager lead to a statistically better performing software engineering or sales team? What is the ROI of using math and science to identify a poor manager, that un-checked could influence the organization to do things that result in devastating class action litigation against your company? How much more likely is a person to be motivated to react to criticism of their management style when presented with data that “We asked the same questions of all managers and you are in the bottom 25th percentile on these measures – please explain”? If you believe that the best hiring criteria for success in your organization is intelligence, represented by an Ivy League education, believing they are more successful, and that is not actually true, what is that worth to you? How much additional do you pay for a graduate of an Ivy League Education x number of employees over time? Further, what additional costs to business performance about being wrong about what makes people successful in an organization? Not to mention, what possible business drags and penalties will accumulate when your organization grows less statistically representative of its community as a result of a faulty premise? What is it worth to you to discover that a many million-dollar Benefit program actually does not relate at all to retain employees when that was the primary reason cited for implementing that program? What is it worth to you, to discover that a 10 million dollar benefits program now, will result in a 100 million benefits program later if your organization continues to grow on the same track? What is it worth to you to avoid the consequence to commitment and morale of offering a benefit and not being able to deliver on it, or slowly taking it back over time? Does the current gain in morale from implementing this program, exceed the future consequences of retracting it? What is the ratio of value to the organization? What is the ROI for a hospital to have a 12.5% 1st Year Tenure Attrition Rate versus 25%? What is the difference in patient satisfaction or outcomes? What is the potential cost of Nurse mistakes? What is the reduction in probability of mistakes by reducing the 1st year turnover rate? … Based on my experience with the question above and real organization data, the question “Does People Analytics Work?” is an absurd one. Would you dream of asking a mathematician, “Does math work?” or asking a scientist, “Does science work?”, or an engineer, “Does engineering work?” or a doctor, “Does medicine work?” Someday I hope we will move past this question for People Analytics. ---------------------------------------------------------------------------------------- #PAQA = People Analytics Question and Answer Series What is People Analytics? What is not People Analytics? Why People Analytics? What is the history of People Analytics? What are the key questions of People Analytics? What is the actual work of People Analytics? What are the alternatives to People Analytics? What is the technology of People Analytics? Feel free to suggest ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, and PeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model. One Model takes data from numerous systems and organizes it so that you can measure, predict and influence workforce behavior to effect change. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin
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Mike West
# 1 Reason people leave google: to connect with their personal calling to change the world. Ironically, it is also the number one reason people join Google. To be fair, I'm a data guy and I don't have access to their data anymore - so consider these an educated guess. It's great that I haven't seen the data in a long time (fuzzy) because I can speculate without assertions I am sharing secrets. Most importantly you can read the things that Laszlo Bock has to say about what attracts, motivates and retains employees directly from him. I do not believe I am off message at all. Across a number of People data projects we get a big theme : you best attract, engage and retain people by connecting with their passion. The role of the company is to leverage assets to provide support to help people materialize dreams because some of those crazy bets are going to someday hit. Get out of the way Google's head of Engineering, Alan Eustace, once said, "One thing is certain about the next big idea, I probably won't have it." (I paraphrase from memory). They work hard to attract, engage and retain talented employees for this reason. They pay more than lip service to these ideas - they invest big in them. As a result of this effort they have much lower average overall turnover than most companies, and really really low key employee turnover, however even Google can't keep them all. Keeping them all might not even be the right goal (for them, the company or for the world). Here is a human story I worked with the now CEO of Omada Health, Sean Duffy, years ago in the Google People Analytics group. We were analysts. I recall the moment when was accepted to Harvard Medical. He had excitement on his face, I asked him what was going on and he showed me the letter. Looking in his eyes I could see it and said, "I'd bet a million dollars you take it". I was right and I was wrong. He went, but soon thereafter he met up with some other guys and they decided to start a company together. They have been working on it for over 5 years, they have iterated, they raised a lot of money from VCs, and I think they have something. Most importantly I believe they have a unique business model. If I understand their business correctly they don't take money for services up front. They get paid by changing population health outcomes, or if not that precisely, at least there is some kind of reconciliation based on outcomes. They can make money from interested insurance companies, large employers and now, it sounds, from Medicaid/Medicare. This might just be the future of medicine. Historically Google has hired people who were overqualified for their roles : a.) because Google is attractive enough that they can, and b.) because they were betting on the future of this person with the company, not a temporary functional skill-set. The result of that is that you end up with too many individuals in small roles who are chasing too few big roles and you get a jammed up talent funnel. If you are stuck in small thinking, there is no solution. You choose winners and losers. You acknowledge that some of your good people are going to go, and that's o.k. You wish them well and try to maintain good relations through a strong alumni network. However, Google doesn't think small. The most interesting thing for me is to watch the big business changes at Google that I believe may directly stem from people data. One word, many letters : Alphabet. What other company would do this? Crazy? No way. The beauty of Alphabet is many fold. By splintering into many parts Google reaps the following benefits: 1.) Increasing the number of available internal paths to big roles, 2.) clarification of decision making, and 3.) clarification of impact - on an entity level but on an individual level. Opportunity can expand infinitely if you think big. Opportunity + Talent + Resources = an economic engine for the future. Don't take my word, here is what Google Chief Finance Officer said himself : “This belief was the impetus for our organizational structure, which enhances focus on opportunities within Google and across Alphabet, while also pushing our leadership to extend the frontier that we are addressing,” said Alphabet’s chief finance officer, Ruth Porat, in the company’s last earnings call." This quote is taken from the The Atlantic's, "Alphabet, Jigsaw, and the Puzzle of Google's New Brand". Stop a minute and think about this. Google's Chief Finance Officer is talking about organization structure and leadership opportunities as a core component of business strategy. Yes, that just happened. No doubt about it Google's Management team is different. They are also smart, they mean business AND they get people. This is The_New_HR. Say what you will about Google, I believe they have this people growth driver thing pretty much wrapped up if other companies don't get on it fast. Alphabet will allow Google to take apart any industry they want to. Consider this fair warning.
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15 min read
Mike West
I spent four days in San Francisco visiting People Analytics professionals and attending Al Adamsen's Talent Strategy Institute : People Analytics and Future of Work Conference. I am deeply grateful for Al's work, that of the conference speakers, and those people working in the space that were willing to allow me to come by, drink their coffee and chat. Here is what I learned. Sometime last year a Deloitte report indicated that growth in People Analytics had plateaued or was slowing, however, at the conference this week Josh Bersin @ Deloitte suggested that over night we have moved into the "Tornado" on Geoffrey Moore's Crossing the Chasm diagram. Basically, I will paraphrase Josh by saying the long winter is over, the birds are chirping but the sleeping giants have awoken. Another way of putting this is that is that People Analytics has begun the "Tipping Point" of exponential growth and/or that we have started to pass through the infamous "Hockey Stick" point of inflection on a growth curve. Believe me when I say the VC's ears are perking up. Two years ago, when we presented our ideas we got blank stares. It was like presenting at the Elk's Lodge, with less alcohol. Still, they had a point; "who is going to buy it?" We are in a better market for entrepreneurs, but seeing this now everyone else is rushing in too so we all experience something like a tornado until winners and losers are sorted out. If you talk to startup entrepreneurs you get a similar picture, although if you get behind the scenes most will admit some degree of frustration with the market - they are having difficulty connecting with buyers and budgets despite that they have made substantial advances in technology and its possible uses within HR. People Analytics Technologists look at the strange HR customer market in utter disbelief, mirroring how the HR customer looks back. These people need us now more than ever, why aren't they beating down a path to our door? To be realistic, in our winter analogy, I remind, we are still only in February, the Texas Blue Bonnets don't peak until April. Maybe we can go on a walk together then. Regardless of where we are on the curve, the original view of "slowing" or "plateau" was clearly incorrect. The argument digresses into methodology. The Deloitte report suggested growth in People Analytics related titles (on job boards) + their consulting revenue was not what would be expected on an exponential growth path, however this vastly underestimated internal job growth, restructuring and refocusing of staff. It is not a small thing - that may have masked at least a 10x year over year magnitude global increase in investment in People Analytics, in one year. Simultaneously, we see growth at PWC, Hay, KPMG, McKinsey and the rest. This is the nature of this space, we have no sense of the pie or each share. People Analytics related job titles in my network indicate anything less than 2x annual growth estimates are far far far off. 2x growth per year is a more than safe bet. For example, my network of People Analytics friends have expanded by 1000 people in just over the last quarter. We are in a once in a lifetime shift in HR akin to the emergence of Finance from Accounting or Marketing from Sales. There was a time they did not exist - you can't remember it. It is big trend, it will have broad impact on the world, potentially every human being, and we are apart of it. Part of the problem is that to get an accurate estimate of growth we must count roles that can be classified under many different titles (People Analytics, HR Analytics, Workforce Analytics, Workforce Insights, Workforce Intelligence, Talent Analytics). These were all represented at the conference. Additionally, we should consider I/O Psychologists, which may or may not go into a formal People Analytics job title or function but relate to the same underlying trend. I have heard I/O Psychologists are the fastest growing job in the country, with an expected annual growth rate of 53%. I/O Psychologists are 1 out of 4 types of people you will find in these pop up People Analytics groups. When I talk about People Analytics I/O Psychologists put their hands on their hips and roll their eyes at me, "What about us, we have been doing this work in organizations for a long time?". Meanwhile they meet by themselves at their conference, SIOP. If you are not an I/O Psychologist, you probably never heard of it. :-) Love you I/O guys and gals - I just have to take little jabs because I didn't get my phD - it is my own insecurity. We also may or may not be counting folks who sit in an HRIS/HR Technology role that have received a new calling. For many, the focus is shifting from an underlying purpose of organizational HR efficiency to organizational HR effectiveness. The emphasis is changing from "some data, any data", to "how do we get data from here to there?", to the question "What can we do with this data?" We learned that Chevron is well into a multi-year effort to train a distributed network of over 200 HR people on how to support business decisions with data - some of these were previously HRIS analyst positions. None of these people were technically "People Analysts", in a People Analytics usage, and many will never carry this actual title. With no standard professional framework for the function materializing yet (like say Finance or Marketing) it is all very confusing, particularly to those on the outside. Another part of what is going on is technology fragmentation. We see a host of new services and products from names we have never heard before AND we see extensions of old services and products from all the names we have heard before. I think IBM has 12.5 different products relevant to our space. I don't even think people who work for IBM know all their People Analytics related products. :-) In effort to focus on the solution and not the technology, technologists are pretty much all using words now like, "insights", "actionable", "business outcomes", "tell stories with data", etc. such that without looking carefully at the details it is difficult to know what anyone is really doing, why they are different, and why you would go with one over another. Technologists are selling to people that have day jobs and People Analytics typically has never before been one of them - so those who have something to sell are on precarious path. CHROs are inclined to hire and wait. Let's let the new guy (or gal) sort this problem out. The demographic change in HR is exactly what demographic change is to presidential politics. Change takes a while to get going, plays out over decade, not always in the ways we expect, but demographics are the most powerful force for change on earth. Similarly change in the People Analytics space has primarily been in repurposed headcount and where it comes from - The_New_HR pitted against the huddled old HR masses whose only remaining options are to join, resist or spout utter non-sense. In our political skit : Jeb Bush appears overwhelmed with disbelief, Hillary Clinton reminds "I was like you once too", and Donald Trump fights politically correctness with factual incorrectness. Sarah Palin is not in the race but hangs around to entertain us with poetry. With Millennials joining together with GenX in disgust of all that is established, boomers gather in fear, and there either is or should be tear gas everywhere. Sarah Palin, I can write poetry too. Much like the US Republican Presidential debates, it is a crowded field with differing views and plenty of interesting arguments. It is also a complete mess. Indeed, isn't this is the way the whole world changes? Fits and starts, a little discomfort, and then one day you wake up and think, "20 years ago what exactly did we do without the internet?" Millennials, by definition, have no idea. Many of us have already tired of email and Facebook.The Millennials response, "come on everyone, we agree, but let's keep moving." Wasn't this true of every generation before them, just with different technology backdrops? Note the background images of the atomic bombs in our parents generation. I believe I caught the tail end of hiding under the desks bit at school - never mind. Maybe Snapchat and Twitter is not really all that bad after all, not negating the atomic power new ways to communicate have. Absent shared mindspace (among executives, HR and People Analysts) for a dedicated budget the go to pragmatic solutions for HR is: 1.) Hire more people (go figure), 2.) training (go figure) and 3.) try to leverage existing technology until we can figure out our roadmap and make the business case for what else we need to do this thing (go figure). Lately I have had no fight in me for "Gloves Off Friday Posts" - apparently needing to recover from my brutal loss to Excel a few weeks ago. What is the current #1 business tool for analysis in the world? Excel. This is not a scientific assessment, but at the conference the number of negative references to "we had to do this in Excel" or we are "trying to get out of Excel", were something akin to counting "ums" when I speak. "Um, there goes another one." :-) All of the successful examples of People Analytics provided at the conference that I heard were stories of the journey out of Excel. The conference should have been named: "People Analytics And Your Future At Work After Excel". In the field of People Analytics, at the moment, success is found via, "we spent x years and x dollars to get on a scalable technology architecture for People Analytics workflow". In one example provided : 2 years and 3.5 Million dollars: I appreciate the transparency. The stories of this journey provided just at this conference include: Intuit (Michelle Deneau), RackSpace/Tesoro Corp (Robert Lanning), McGraw Hill (Antony Ebelle-Ebanda), Gap (Anthony Walter), GE (Heather Whiteman),... but this is only a small sample. Nobody disagreed. Nobody was shocked. The story is the same everywhere - there are many more interesting things we can do, but there is little way around the journey - wrapping arms around the data is a part of the journey. Confront the brutal facts. It should be mentioned the folks that presented are just the heros, the survivors. I appreciate Tauseef Rahman for pulling me aside and descretely mentioning the problem of "Survivor Bias" - e.g. we do not hear from the failures so we don't know if they might have done the exact same things and got a different result. Survivor bias may be a problem, but unless you go looking around for skeletons you will probably never know for sure. At the moment we have no real measure of success for our field. Success is not measured in terms of speed or cost of implementing a data warehouse and reporting suite, relative to peers. That said there are real differences. Absent any other clear measure of success this might do as a proxy in the beginning. I'm anxious to see what happens with Robert Lanning in his new role at Tesoro - he has a particularly good track record. It is not that Excel and other ad-hoc tools are not useful, it is just that they don't scale with demand after you actually show people what insights it is possible for them to get in our space. Beware of this - they never knew before how powerful this HR Analytics stuff really was - all bets are off when executives finally see it. Also at the conference, someone reminded us of one of Amit Mohindra's (Apple) clever laws, which I paraphrase as "upon exposure, demand for People Analytics related insights increases exponentially" My personal experience is the same. If you believe us, pay careful attention to what this implies. If you do not create a scalable analytical architecture at the outset to meet demand in an equation like T+1 = demand^2, you will fail if you cannot increase resource^2. The math suggests this will probably occur about 1.5-2 years into the role. No worries, opportunities in the space will abound for some time so you will get a second or third chance to get it right at a clean employer. My learning parallels what Ian O'Keefe (next gen Google People Analytics) says, "we must work three problems simultaneously : efficiency, effectiveness, and user experience". Some people are better at managing expectations than I was while working in HR, but in my experience the examples provided indicate this scale problem is a competency for technology, not people. People are for asking good questions, constructing ways of approaching those questions, finding insights, telling stories, consulting, making decisions, etc. --- technology is simply for scale. These are two different sides of the coin that must be managed simultaneously (and well). You can manage expectations with "no", but we are not equally exposed to executives that understand this word - let the conflicts ensue. In my biased, not humbly stated, opinion you must use technology to take basic reporting off the table, but you must simultaneously create means to expose that data to a variety of downstream systems to support a complete analytical workflow : statistical applications for data scientists (R, SPSS, Python), niche people analytics applications to augment (varied), and data innovative visualization applications used by other functions of your businesses and executives (Tableau, Domo...). If your data doesn't join sources AND port to these environments efficiently, I don't care how good it is now, you are going to get stuck somewhere. If you are addressing the problem with people your people needs/costs are going to need to expand exponentially with demand. If you do not have an exponential budget (e.g. think Google) and don't have a scalable architecture for repeatability and change, then you have real trouble. Other troubles. If you can't get data into advanced statistical models you will bore and be eventually replaced. If you can't communicate in tomorrow's visual frameworks you will be dismissed. There is no application that is simultaneously best at all desired functions, or after achieving that mountain will be able to maintain a lead for long, so consider carefully how each technology extends and connects with the other technologies, what technology providers are most facilitative of partnerships, and what the support model looks like. Who is going to do the work to support changes, when, do they fall under your authority to control? Pay attention to all the tiny little details. ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. If you have interest in participating in the webinar series, let me know here: (http://www.misc-peopleanalytics.com/lean-series) And if you know anyone else who you think would, please let them know too!
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Mike West
January 18th, 2016 In the 53 years that has passed since Dr. Martin Luther King Jr. delivered his I Have a Dream speech America has clearly come a long way, however the work of freedom is not finished. If you listen carefully the words of Dr. Martin Luther King's speech are still as relevant today as ever. While he was speaking primarily to obvious injustices against people of color at in his time - and some of these measures have progressed - specifically, black people suffer less lynchings and can actually directly influence the political process - yet, upon close inspection of facts, it is obvious there is a startling and frustrating lack of progress today. On nearly all fundamental metrics of prosperity we see massive pernicious disparity by race. How could we have worked so hard and arrived in a place not really that much different from where we started? I will not insult you with an easy or absolute answer : the 3 step process for racial equality or the 5 reasons things are or aren't as bad as some would claim. Difficult problems cannot be satisfied by the sticky residue of low hanging fruit. I will share my few observations. I don't know if the world has really become more complex, but it certainly seems like it has. Over my lifetime I have worked for over 10 different organizations in professional and nonprofessional roles and have never met a blatant racist at work (and very few even in my personal life). How much easier it would be to confront the obvious ignorance of racism directly. This type of problem seems it could be tidied up promptly. However that is not our work today. The problem we are dealing with is much much more insidious than this. I can find no racists, but of "unconscious bias" I could dredge up volumes for you. Here is a tidy summary published by the New York Times : Racial Bias, Even When We Have Good Intentions. Lacking an actual human descriptor - we are fighting an elusive thief that we are forced to call some inhuman name "unconscious bias". Who believes in something you cannot see and a story you cannot tell. Imagine the frustration of toiling hard each day, stockpiling the fruit of your labor for tomorrow's meal and finding each night some unknown stranger steals it you in the quiet of the night. You have no way of catching, identifying or accusing this stranger and nobody believes you. Your lack of food must be explained by something else - perhaps you are lazy. You have spoken about it for so long, and this injustice so unbelievable that sometimes even you wonder if you have gone crazy. This is a description of the actual horror of unconscious bias. One of the things we are doing now, different than in the past is that we are beginning to face this thief directly. It turns out that clever people have devised clever ways of actually catching the thief in the act. Here is a video of great work conducted by People Analytics at Google, speaker is Brian Welle, a former colleague of mine : https://youtu.be/nLjFTHTgEVU . Brian will blow your mind. Fundamentally, at its essence People Analytics is about using clever research methods and data to reduce mistakes of human bias, because bias causes us to make worse decisions for our business than we would have made with a more perfect understanding of truth. It was only a matter of time that People Analytics would turn its attention directly upon matters of diversity too. While justifiable in its own right as an effort for "fairness" or for "the law", but we actually don't stand against bias at work just for these reasons - we actually benefit from truth too. This is not benevolence or charity - that demeans it . The most astounding thing about working on issues of bias is that when we make decisions with less bias we benefit directly too! If you did not get this point from your reading of the book "Moneyball", or watching of the movie, go back and watch it again - you missed an important detail. They did not do this "diversity thing" because we feel sorry for people who look different or throw the ball weird - it turns out that if you like winning people who throw the ball weird might make great teammates! This is the beauty of all things good and eternal. In truth there is actually no threat to anyone. Open up the door, let every truth come in. The house only expands. We may never reach a place of perfect truth or perfect answers on this earth, but as I am reminded by Dr. Martin Luther King Jr. I too refuse to believe that there are insufficient funds in the great vaults of opportunity of this nation (as he addressed the United States of America at Washington DC in 1963) I leave you with his words, which even 50 years later, never fail to bring tears to my eyes. We are not where we wanted to be, but Dr. Martin Luther King Jr. is no less prophet if we open our minds, hearts and ears. "In a sense we've come to our nation's capital to cash a check. When the architects of our republic wrote the magnificent words of the Constitution and the Declaration of Independence, they were signing a promissory note to which every American was to fall heir. This note was a promise that all men, yes, black men as well as white men, would be guaranteed the "unalienable Rights" of "Life, Liberty and the pursuit of Happiness." It is obvious today that America has defaulted on this promissory note, insofar as her citizens of color are concerned. Instead of honoring this sacred obligation, America has given the Negro people a bad check, a check which has come back marked "insufficient funds." But we refuse to believe that the bank of justice is bankrupt. We refuse to believe that there are insufficient funds in the great vaults of opportunity of this nation. And so, we've come to cash this check, a check that will give us upon demand the riches of freedom and the security of justice." - Dr. Martin Luther King Jr. Full I Have a Dream Speech : https://youtu.be/I47Y6VHc3Ms ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. If you have interest in participating in the webinar series, let me know here: (http://www.misc-peopleanalytics.com/lean-series) And if you know anyone else who you think would, please let them know too!
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Mike West
Did you think I'd crumble? Did you think I'd lay down and die? Oh no, not I. I will survive. - Gloria Gaynor, "I will Survive". 2002, White House Station, New Jersey, Merck, Organizational Learning I am helping to organize a launch meeting for our new Performance Management process and we need to stand up a website. I studied Sociology and Psychology and finally Human Resource Management with a natural penchant for Abstract Mathematics. My skills don't extend into HTML - in 2002 we didn't have SquareSpace - At Merck we were lucky we had Internet Explorer installed. I was invited to go talk to this guy in IT about creating a website for the event. For reasons I will describe, I'll never forget this meeting. To protect his identity I will call him "Stan". I get to Stan's desk area/cube. First, I see this row of stuffed animals. Let me just stop here and provide context. At Merck, at this time, we wore suits and ties to work every day – presumably in case a member of Congress, a foreign dignitary or a nobel laureate just stopped into the office. We were East Coast Pharma. We were a 100 year old organization and one of the most productive research institutions in the world. In our minds, we saved lives and we put billions of dollars in the economy, and we proud of both - we were buttoned up. Guys like me wore single-breasted jackets. If you were at the top your suit was double, maybe triple breasted. I don't recall what Stan was wearing precisely, but by the row of stuffed animals on his desk, I knew this guy was a strange animal. I was curious how Stan got away with this. Stan told me to come in, pull up a chair and sit down. I sat next to him in his cubicle, side by side staring at a couple of computer monitors, stuffed animals behind me. He had at least three computer boxes. He asked, “what do you want ?” As I recall, at the same time I was speaking he was typing in HTML, occasionally with diversions to chat while he kept typing. With swift keyboard strokes he would move seamlessly between computers, moving things from a design environment, to test, to production. Somewhere in this exchange I thought, "I don't care about the stuffed animals, this guy is pretty cool." I thought in my mind - if I ever go start a company this is the first guy I would take. For fear of losing access to him I told no one else about this. I don't know if I actually ever said this to Stan directly. Time went on, and Stan and I went on our separate ways. A few times after I left Merck I tried to engage Stan in projects I had going, but it has never worked out. I don't know if he kept up with changing technology or not. My learning from Stan is that “most of us use computers; some of us use them differently.” Excel - “most of us have used Excel; some of us use it differently.” I share the story about “Stan” because it makes what I am going to say about Excel just a little more vivid: “most of us have used Excel; some of us use it differently.” I want you to understand the degree to which I understand, appreciate (and even love) Excel before I describe why it may be the most dangerous business application of all time. This story about Stan with HTML foreshadowed how I would some day use Excel. It didn't come easy. Nobody taught me how to work with HR data in Excel. The way I use Excel is different than most people I have ever observed working in Excel. Granted I got there somewhere after spending over 10,000 hours on it over 15 years. I sit sheets like data stores, with pivot tables that feed into lists that other tables sit on, and lookup functions that move data around, transform it into whatever I need it to be, that feed into downstream analysis, and finally, charts. I have figured out how to do multidimensional reporting in Excel. I work data through recursive algorithms in Excel. I use add-ons to run a variety of statistics. I take the charts provided in Excel and strip them down to the bones, rebuilding them into something beautiful, apparently never imagined by Microsoft. When I am working with Excel, I often don't even see the detail of the data – at times I might as well be operating in another dimension. Having said all this, I'm sure there are people who have even more advanced Excel skills than myself. I'm not saying I'm the greatest - simply that there are differences between how people use Excel, so when we talk about Excel it is important to keep this in mind. Excel can be a very powerful application. 2006, Mountain View California, Google, People Analytics There was a time at Google where I was working with employee data in Excel and developing ways to run out reports by all segments we had in the data. I had spent probably 150 hours working iterating report trials off this dataset over several weeks. I started on the Google bus on the way into the office as I travelled from Berkeley to Mountain View, worked all day at Mountain View, stopping only for food and coffee, then continued working on the Google bus on the way home at night, collapsing into bed when I arrived, and starting over again the next day. At one point, while working with the structure of the data, reaching that point just before it was ready to share and it was like the sun rising on a very dark night. In some way it was like seeing God. That or I had reached the ceiling for caffeine consumption - one or the other. If I did not see God at the very least experienced what it must be like to see pure truth. The best example I can provide of what this is like, it is like standing on the edge of the Grand Canyon or an ocean or looking at the stars in complete darkness on a crystal clear night. I have not since recreated a feeling this vivid at work - apparently I lost track of the specific Excel function for this :-) - but what remains with me today is an appreciation that there is a truth encapsulated in data and a beauty in its mathematical structure, which also happens to be powerful. If you spend enough time in it, you might see it. Don’t take my word for this, here representative thought from a series of quotes about the beauty of mathematics: “It seems to me now that mathematics is capable of an artistic excellence as great as that of any music, perhaps greater; not because the pleasure it gives (although very pure) is comparable, either in intensity or in the number of people who feel it, to that of music, but because it gives in absolute perfection that combination, characteristic of great art, of godlike freedom, with the sense of inevitable destiny; because, in fact, it constructs an ideal world where everything is perfect but true.” Bertrand Russell (1872-1970), Autobiography Thoughts about the beauty of mathematics The way that Excel is different from most other applications for working with data is that in Excel, you can actually directly see the data you are working with. There are a number of other reasons why Excel is the most used business application of all time, but I won’t bore you with the nuances. This quote sums it up: “Microsoft Excel is one of the greatest, most powerful, most important software applications of all time. Many in the industry will no doubt object, but it provides enormous capacity to do quantitative analysis, letting you do anything from statistical analyses of databases with hundreds of thousands of records to complex estimation tools with user-friendly front ends. And unlike traditional statistical programs, it provides an intuitive interface that lets you see what happens to the data as you manipulate them” (The Importance of Excel) I love my friend excel, but I'm about to shake his hand and then pummel him. The main argument against Excel has been that the things that make Excel great are also its biggest downside. First, Data Quality: Excel makes it too easy for people to make mistakes Excel makes it too easy for people to lie For starters, while it is incredibly easy to get started making spreadsheets, it’s also incredibly easy to make mistakes that cost companies millions (or billions) of dollars. In 2008, University of Hawai’i professor Raymond Panko published a summary of 13 field audits that checked spreadsheets used in ‘real-world’ environments. His analysis found that a whopping 88% of the spreadsheets had errors! In evaluating possible solutions to the spreadsheet errors he described in his 2008 paper, Professor Panko wrote: “… few spreadsheet developers have spreadsheeting in their job descriptions at all, and very few do spreadsheet development as their main task.” One problem is that since everybody has at least some knowledge of how to use Excel, many people misjudge their own expertise, as well as that of others. This is different from when how we hire and judge software developers. Business managers don't know that there is a problem (actually, lots of problems) with spreadsheets, while IT regards spreadsheets as falling outside its jurisdiction. So spreadsheet management falls into a black hole. While Excel the program is reasonably robust, the spreadsheets that people create with Excel are fragile. There is no way to trace where the data came from, when, and what was done to. The biggest problem is that anyone can create Excel spreadsheets—badly. Because it’s so easy to use, the creation of even important spreadsheets is not restricted to people who understand programming and do it in a methodical, documented way. There are a number of public examples of Excel mistakes, some with substantial impact 2012, London, "The London Whale" The problems of Excel apply to anything and anyone who’s working with data in Excel, not just HR. Here is a description of the particularly high impact example at JP Morgan: Microsoft Excel Might Be The Most Dangerous Software on the Planet. “After the London Whale trade blew up, the Model Review Group discovered that the model had not been automated and found several other errors. Most spectacularly, “After subtracting the old rate from the new rate, the spreadsheet divided by their sum instead of their average, as the modeler had intended. This error likely had the effect of muting volatility by a factor of two and of lowering the VaR . . .” The explanation in English: someone at JP Morgan was running bets (to the tune of tens of billions of dollars) in Excel and there was an error. There may have been other negligence or nefariousness going on as well, but I found the most outrageous part of the story that this sophisticated derivate work was completed in Excel in the first place. Stupefying actually. At the time I was getting paid 1000 times less money to do similar work in Human Resources- you have to ask yourself, "Maybe I should have considered a different profession?" "I could totally screw up derivatives, maybe even 1/3 as bad as this "whale guy". The other ways that Excel falls down are: Difficulty seeing workflow (e.g. how the data goes through stages) Difficulty documenting workflow, process audit trail. Difficulty with dependence - difficulty transitioning spreadsheets from one person to the next. Stale data and/or constant rework as a result of stale data. Difficult to see the real cost of manual work in Excel being performed across the organization. Inability to secure data. 2013, Austin, Texas, ACMETech, Workforce Analytics Very few people know that between the time that I worked at Children’s Medical in Dallas and started my own consulting company, two and a half years prior to joining One Model, I worked for a technology company in Austin. Let’s call them AcmeTech. AcmeTech lured me from a children’s hospital in Dallas with higher pay, better benefits, a well stocked micro kitchen, free lunch on Fridays and a ping pong table. I felt bad leaving the children. Little did I know at the time, as a result of this decision I was going to hell. When I interviewed with AcmeTech I described the important analytical work I had done in HR at Merck, PetSmart, Google and even at a very modestly funded non profit children’s hospital. The emphasis of this work was in automating analytical workflow AND then spending my time in more sophisticated and high value analysis like exit prediction models. I thought we were on the same page. Well, I started with ACMETech and soon learned that this, in fact, we were not on the same page. I was expected to create weekly HR reports for the division I supported in Excel. We were dumping data out of WorkDay into Excel, aggregating into metrics and reporting by segment. I have a history creating prototype reports just like this in Excel but these always were temporary, not long term, solutions. At ACMETech this is something they had been doing for years and there were some unique nuances to the way they were doing it that prevented full automation. My predecessor diligently showed me how to copy data from one sheet to another, change a series of things (to be recalled by notes or by memory), check for these other things that may or may not go wrong, then publish the reports out by email. A single report would take me a full day to complete and there were several different versions of these reports for different stakeholders. There were a dozens points of possible failure. There were five of us on this team doing the exact same reports for different divisions. When I raised this issue to my manager and my ‘managers manager’ I was told, “We want you to keep doing the reports the same way they were done by your predecessor.” This is my problem: somehow I had gone from at one point of the time one of the most brilliant People Analytics teams in the world to now something slightly above “human cog” in a car headed to nowhere, driving off a cliff. Here were my views on ACMETech's Excel Based Workforce Analytics Process: There was not much value in these reports as produced, relative to other work we could be completing on behalf of the organization. The incredible waste of time and money in this approach - not to mention life effort. When things eventually went wrong or people were through with this it would be our neck on the line (in the case of my division my neck specifically) The way we were running these reports were affecting the quality of experience for the recipients of the reports. I knew there were better ways that would save ACMETech HR time, money and credibility that could be put in place very quickly. My mind just didn’t work the way we were running these reports - I could operate more effectively in other capacities. At the end of the day I went back and said, “You hired me for my expertise, there is a better way.” The reply was, “Do it our way or leave”. My reply, “o.k. then”. That’s when I decided to go start my own company. I wanted to work with people who really wanted to work with me, or not at all. I acknowledge that ACMETech HR can choose to do whatever ACMETech HR wants to do and that is fine, but if you look at what they were spending each year on headcount, turnover and hire reports for their organization it was at least $500,000 and if you calculate that over 5 years they have spent at least 2.5 million for a very basic reporting framework - without any real semblance of advanced analytics we know as People Analytics and full of holes. This, my friends, is why Excel in HR is dangerous, and a great case study for why you should consider an alternative solution for analysis and reporting! There are a variety of options today, One of which is One Model - which in full disclosure I recently joined - I'm a little biased. Other options are out there: seeWhat Your HR Analytics Technology Sales Rep Doesn't Want You to Know. ---------------------------------------------------------------------------------------- Disclosures: This is a "Gloves Off Friday" post Mike West is a bad man Mike West writes way too much about People Analytics Mike West is currently VP of Product Strategy for One Model ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems. Connect with Mike West on Linkedin ---------------------------------------------------------------------------------------- I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose.
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"Life is a struggle, and then you die" So go make something of it. Work on something important and watch over these things. The 5 Reasons Most HR Analytics Efforts Stall: #1. Not having enough precision on what is the right problem to focus and what questions you need to answer to solve that problem. The typical fail is that you will spend a huge amount of time, money and effort to get a HR reporting environment set-up but downstream users do not use them. People say the information is nice to have - they just don’t have time to go look at your reports. Sometimes the problem is that the information has little relationship to important decisions, or little bearing on the work that anyone is doing. Often the people supported will request an infinite assortment trivial changes in the desperate hope that each change will produce a better result. Or with no specific reason provided you and your solution just go from hero to old hat over night - and you are left to wonder what actually went wrong. The problem is that you spent your resources and time working on the wrong problems and questions. More could have been accomplished with your time and effort had clarity been achieved at the outset. #2. Not having all the right data you need to answer the questions you want to answer. The worst possible outcome of analysis is to produce a statistically significant finding that increases certainty in a wrong answer. This is a common outcome of the “missing variable problem” (the unknown unknowns that wreck most analysis) This is some portion of the 80% of the variance your model did not explain. You ran the analysis but did not include the right control data, so you get an answer, but you get a wrong answer, and you have no way of knowing you got a wrong answer. Sound like a nightmare? This is not a nightmare, this is a real problem. The second worst possible outcome is when you do all the work and don’t achieve a statistically significant finding but could have produced a finding if you had included the right variables. In either case, not having a basic theory that would explain what variables to include in the analysis results in you never achieving a satisfactory outcome from your effort or you may double or triple the hours to reach a conclusive answer because many passes are required. These problems are why we pay university scientists the big bucks. Big Bucks!? O.K., not really! University scientists try harder because they know their work will be 'Peer Reviewed' by other really smart people who also know something about this topic. We don’t have this check inside organizations - we have non experts reviewing the work of experts. Major danger. #3. Expecting technology to solve the whole problem (absent analysts). You have made an important investment in supporting technology, but you may not get anything of lasting value out of this investment because you did not factor in the cost of acquiring (or creating) skilled operators of this technology. It is as if you have this wonderful piece of machinery sitting in idle. The success you have with analytics is dependent on the experience and preparation of the people working the analysis. You can achieve two different analysis outcomes with two different analysts, using the same technology! The worst part - if you get it wrong, how would you even know? Between different analysts you will find different choices about what data to include, chosen research method, as well as differences in skill in executing chosen method. Clearly, you need to think about analysts, but you must also think about the rest of the organization. It does not help you to have this group of "really smart data people” and nobody else who knows how to use their work. You need everyone on the same page on where we are trying to go, what everyone's role is, and how it all fits together. #4. Expecting your analyst to solve the whole problem (absent the right technology & support). Analysts are evaluated based on results. Some other HR roles can produce activities (implementing programs, policies, processes and systems) and we declare victory at the conclusion of the activity without respect to impact (which conveniently is never measured). Success is defined as completion of the project on budget and on time, then on to the next. Analysts do not have this privilege! For Analysts, the proof is in the pudding. If you tell the truth, "based on our data and the tools I have I found nothing of lasting significance to you" - your reward is you don't get invited back to the meeting. Analysts either produce very little value and stick around (satisfied with the activity for pay, as opposed to outcomes) or they leave for another opportunity to do better analysis. They either have a fire in their eye or they don't. You want the one who cares, or don't bother even starting. You have made an important investment in a person, but you can get nothing of lasting value out of this person without providing the tools and support they need to complete their work. Managing an environment like this is difficult, but not impossible - it requires skill and care. #5. Expecting results without someone putting in hard work. Your typical project management wisdom applies - choose one out of three : time, quality or cost. Every new question you want to answer will involve investment in new data collection, cleanup, transport, joining, reshaping, statistics and figuring out how to best communicate the results. We inevitably want automation of routine analytical workflow, but there is a first and second priority constraint : what should be made routine? How can it be made routine? We (technologists) will try our best to design out of this, but the first pass is best handled by a human - this will be hard to get around. It will fail if no one has put in the work. This doesn’t necessarily mean you have to do all the work - or even the hard work - just that somebody does and there is no way to escape this cost. You can bring in consultants to do the work, you can hire enough people in your organizations to do the work, or you can buy packaged solutions that help with part of the work. In this you will be making big trade offs on time, quality of delivery and cost. Beware - no silver bullet will kill this beast. You must make a commitment to ongoing refinement of the analytical process or you get an analytical process that really does nothing for you. If you get into the real day-to-day work of HR Analytics, the People Analysts are dealing with data that generated for some other purpose that does not conform to basic needs and expectations of our existing purpose. The best way to understand what must be done to automate an analytics workflow is to have someone work through the analysis one time to understand what data is there, what is not, what is wrong, and figure how what is there must be improved for successful analysis to occur. Often we implement expensive reporting solutions on the hope that these will produce useful insights. Why invest in automation (repeatability) if you can not achieve a useful finding through a manual effort? Hope? Hope is a great attitude to apply to all situations, but not a great strategy. Why not run through it manually one time and figure out if it is worth automating? The most important question is - when you got to the end of it all manually, do you end up with a report that is useful to the organization? If you did, great, now is the right time to make decision about automation. ---------------------------------------------------------------------------------------- People Analytics is difficult, no doubt about that, but... I’m putting together a series of live group webinars where I will be revealing a process for dramatically increasing probability of success of People Analytics - building on a career of success and failures (Merck, PetSmart, Google, Otsuka, Children's Medical Dallas and Jawbone) - and applying new ideas I have developed over the last few years applying ideas from Lean to People Analytics. The goal of this webinar series is to engage a select group of qualified early adopters, who have access to an organization, are willing to apply the process, report back how things are going, and work out the bugs out together. This group will have opportunity to share their findings with the broader People Analytics and HR community, if you choose. ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 15 years of experience building People Analytics from the ground up as an employee at the founding of Merck HR Decision Support, PetSmart Talent Analytics, Google People Analytics, Children's Medical (Dallas) HR Analytics, andPeopleAnalyst - the first People Analytics design firm - working with Jawbone, Otsuka and several People Analytics technology start-ups. Mike is currently the VP of Product Strategy for One Model - the first cloud data warehouse platform designed for People Analytics. Mike's passion is figuring out how to create an analysis strategy for difficult HR problems.
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To whom it may concern, For the last 2 years I am proud to have run my own People Analytics consulting company - PeopleAnalyst, which I like to call the first Independent People Analytics Design Company - but On January 1st - I will be joining One Model. These are the reasons: 1. I believe that People Analytics is important to the future of HR, the future of business and humanity – perhaps one of the most important business trends in our lifetime. I recently shared the principles I hold, supporting this thought here: Future of Human Resources - in 10 Principles. Beyond these principles I frequently try to point out that we had Accounting before we had Finance, we had Sales before we had Marketing, and we had HR (People Operations) before People Analytics. In every historical case, the application of an analytical framework to the pre-existing operational function revolutionized the way business was done, and those who were early to it were able to exploit lucrative informational advantages for a brief period of time before they became ubiquitous. In face of history these changes did not occur that long ago but today we think of them as always being the way they are. People Analytics’ time is now – in the future it will be “required for entry” in the big girls/boys club, but not be as much of an advantage as it is now. Pay very careful attention to the investment companies like Google and Walmart have made in People Analytics – 30+ people each and growing, going back several years. These companies are not stupid – this says something - they saw something. Google is “cleaning up” on the application of data to the People Operations/Talent area and in many markets they are a force to be reckoned with - nobody is anywhere near them on what they offer and how they do HR today. They are a steamroller. 2. Long ago I decided that my work - the application of data, math and science to HR - is my reason for being and part of the intentional legacy I want to leave on this earth. My commitment to what we now call People Analytics is unchanging - the key for me as I go through life is just figuring out where my efforts will have the most impact. I make moves when I sense the math on this has changed. Merck -> PetSmart -> Google -> (brief cross functional divergence)-> Children’s Medical ->PeopleAnalyst (my consulting company - the first People Analytics design company) -> now One Model… As we move forward, I think my area of greatest contribution will be to embed my unique way of approaching People Analytics into a technology environment, making it more realistic, accessible and affordable to more organizations. It is quite an amazing thing for a guy like me to have access to an engineering team with seed funding – I’m not going to pass on that opportunity. 3. Team - I believe in the magic of teamwork. I saw this video, which reminded me of what can be accomplished with teamwork : http://wapo.st/1U19g0M Gives me chills - the good kind. InfoHRM --> Success Factors --> SAP If you know anything about the history of enterprise reporting solutions for HR, the foundational predecessor to modern HR Analytics, you will find that the engineering team at One Model has a very interesting pedigree. Going back 10 years, the only system in this space was a little company called InfoHRM – they were out on the leading edge of HR reporting, essentially running a “cloud-like” solution before we even knew what the cloud was. InfoHRM was acquired by Success Factors (purportedly to help them crack the HR data reporting challenges they couldn’t seem to solve on their own), and then Success Factors was later gobbled up by SAP. I don’t know the whole behind the scenes story, but my general impression of what happens to people in these big company acquisitions is that how the product is perceived, the dynamics of working for an organization, as well as where you fit into all that really change. These guys fell out of that. When you see people who helped built a product category before anybody else was doing anything like it, who say now we are building something better knowing what we know now, you stop and listen - at least I do. TechStars – The Top 1% of Startups Another thing I really like about One Model is that they came out of the TechStars Accelerator program. Accelerators like TechStars are super-selective--less than 1% of applicants get in. You could say they are pickier than schools like Harvard, Stanford and MIT. In addition to direct assistance in getting the business model on the right track, and the well know “Pitch Day” TechStars alumni have access to a network of investors and advisors for life. In the Startup world, access to capital matters and access is primarily determined by your network. An element of this may seem like a superficial game of “who you know and who knows you”, but an element of this is ability to get to people who have been through good and hard times and can help you solve really difficult problems. Austin - SXSW and Food Trucks about say it all. These guys came to Austin to launch their company a little after the time that I did. I’m an HR guy – I’m all about culture and Austin has the right culture for me. Austin is hip (some call it weird), is second only Silicon Valley for startup community, in a US state that is friendly to business, has a lower cost of living than either US coast, and is well positioned geographically for US enterprise sales – 4 hours by plane to either coast and within driving distance or very short plane flight of 3 other major cities (Houston, Dallas and San Antonio). The startup community is tight-nit, collaborative and with a lot less of the showmanship and games you see in the Silicon Valley – I think a higher percentage of people here take creating a real business more seriously. Beyond these intangibles, when it comes to HR data, keep an eye on Austin, this is where it will be, there is some important stuff going on this space here right now. Mostly I just love Austin – it is an island of authenticity and creative energy unlike anywhere else. 4. Product Focus – oh where we can go together. One of the big mistakes I see in the field right now is that most people that are thinking at all about the space are thinking too narrowly. They think People Analytics is just one type of question, one type of data, or the application of a certain method of working with data. Let’s say prediction, for example. Examples include, how do you predict hires who will perform well in your environment or how do you predict what people are most likely to leave in a given time frame. However, some of these strike me as gimmicks - not standing up on real solid data - People Analytics is much more than this. For example - I have personally worked with data on HR on decisions involving how organizations select (staffing), onboard, pay (compensation), perk (benefits), the origins of happiness and motivation at work, quality of managers, employee commitment/turnover, performance, diversity, learning/training, time off policy, the relationship of HR outcomes to sales outcomes, etc… Others have worked on topics I haven't - the list continues. On top of the varied subject matter focus, you can focus on how you collect data, the tools to make data flow more efficiently, the methods you can use to analyze it, statistics, how you visualize the data, how you distribute to other people, etc. Any and all of these are potentially viable areas of business focus that you will see niche products in. As methods, machine learning algorithms and prediction are hot right now – all these are in our future, but we still have a lot of work to do on them. Here are the main perceptions I will offer on product focus at this time: People Analytics is eclectic, expansive and inclusive. In its essence, People Analytics is the systematic application of behavioral science and statistics to Human Resource Management to achieve probability derived business advantages. We need solutions that enable analysts to be better analysts, in the world of possibilities, not try to replace the analysts entirely. We need solutions that create more heros, not less. People tire quickly of gadgets and nobody wants to purchase and manage an ever-expanding assortment of gadgets (or only if they are all made by Apple). I’ll put it another way - One Model looks more like an aircraft carrier to me than a paper airplane. Organizations operate as holistic systems, therefore the answers to problems span across areas of specific functional responsibility, expertise and operational data stores. We have a lot of silos of data in HR – HR has undergone progressive advances through technology specialization and will continue to. The great irony is that the future of HR Analytics may be just reversed: synthesis, not specialization. The differentiating premise of One Model is synthesis. Many advantages will stem from this vantage point. If you care about synthesis of data in HR One Model should be on your short list. To do any analytics, simple or advanced (prediction, forecasting, optimization), accurately, regularly, in a timely and efficient way requires address of sprawling un-integrated operational HR data sources and process. One Model decided to start with the ‘data munging’ fundamentals and build from there. That doesn’t sound sexy and is a little more difficult initially to get the same kind of PR as a result, but it is important, and they will steadily deliver increasing value on that foundation, offers a lot of possibilities, and takes customers into the future in steps, not all at once. Imagine showing up to the board room with predictions about employees but you can't accurately answer or get to quickly any of the basic employee ins and outs questions. Begging the question - do you really know your workforce? What exactly do you do here anyway? I'm all for prediction and One Model is going there but don't over promise, really get to know HR data specifically, get the 'data munging' fundamentals right, organize more sources data more efficiently than anyone else, and delight and surprise the customer progressively as you go. I agree with this – I think it works better, fits the needs of today's HR function, and matches my practical MidWest (US) values. Cloud / Software as Service is here, is the future. The premise of One Model is that they can invest big in infrastructure and innovation on that infrastructure and distribute those gains to everyone. It only gets progressively better and more efficient over time. Why should every company invest in homegrown infrastructure for HR Reporting and Analytics independently? To reinvent all HR Analytics workflow internally at every organization is unrealistic for most organizations as it is a ludicrous business proposition. We no longer design our own homegrown HRIS systems today - why create and maintain our own technology infrastructure for HR Reporting and Analytics? I think 5-10 years from now we will look back and wonder why we used to do this at all, evoking the puckered sneers that “legacy HRIS solutions” get today. Don’t get me wrong - you should invest in ultra advanced or niche innovations in analytics unique to your business, in your environment, however in order to have the time and resources available to apply that kind of focus, you can rent everything else. How about getting on a platform that can speak to those applications and everything else. Like I do, these guys believe in "play nice with others" and that good guys do win too sometimes. You want to come along for the ride? ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 10+ years of experience building People Analytics from the ground up at companies such as Google, Merck, PetSmart, Children's Medical, Jawbone and other places. Mike's passion is to develop thought leadership and to cross pollinate the frameworks and processes he helped develop and pioneer as an employee at these places. Mike spends most of his time teaching, coaching and writing on all things People Analytics.
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Mike West
“Men* wanted for hazardous journey. Low wages, bitter cold, long hours of complete darkness. Safe return doubtful. Honour and recognition in event of success.” Serious question. If you stumbled on the employment ad above, would you respond? The ad is purported to have been posted by Ernest Shackleton to recruit people (“Men*”) for his Endurance expedition to the South Pole. There is some debate whether the ad was actually written by Shackleton, never-the-less whoever wrote it could get credit for compelling ad copy, as well as possibly the first example of a realistic job preview. *This was around 1914, and apparently whoever actually wrote this was unaware of woman’s interest in suffering in the workplace for 25% less than men! This reminds me of a pernicious glass ceiling and wage disparity problem – and now suddenly taking a little boat down to the South Pole doesn’t actually sound so challenging to me. Lots more I can say about the journey into problems of diversity and gender, but we will circle back to HR data and diversity another time. For now, let’s just continue with our juxtaposition of the cold, icy cold, Antarctic and HR Analytics, in general. A Brief Summary of The Expedition (Wikipedia): Endurance became beset in the ice of the Weddell Sea before reaching Vahsel Bay, and despite efforts to free it, drifted northward, held in the pack ice, throughout the Antarctic winter of 1915. Eventually the ship was crushed and sank, stranding its 28-man complement on the ice. After months spent in makeshift camps as the ice continued its northwards drift, the party took to the lifeboats to reach the inhospitable, uninhabited Elephant Island. Shackleton and five others then made an 800-mile (1,287 km) open-boat journey in the James Caird to reach South Georgia. From there, Shackleton was eventually able to mount a rescue of the men waiting on Elephant Island and bring them home. On the other side of the continent, the Ross Sea party overcame great hardships to fulfill its sub-mission. Aurora was blown from her moorings during a gale and was unable to return, leaving the shore party marooned without proper supplies or equipment. Nevertheless, the depots were laid, but three lives were lost in the process. Clearly the expedition failed to accomplish all of its objectives – yet it is recognized as an epic feat of leadership, endurance and one of the last of “great expeditions”. Some time ago I came across the ad, which tickles my sick sense of humor and my imagination. Who would take this job. I wonder - who wouldn’t take this job? In my mind I notice subtle similarities to the choices I have made in my career – which circles on the question how to change Human Resources, if not the direction of work as a whole, with data. Despite difficulty and a seeming complete lack of possibility for fame in this work, I am excited by what I do and I love every minute of it. I probably would have responded to the ad. I am also intrigued by others who take an extended interest in my strange field - partly because historically extended interest has been rare and partly because it is far from what most would consider a "rewarding career". I have been tracking new roles in HR Analytics, Talent Analytics, Workforce Analytics (what I call People Analytics) through Job Postings off and on for a long time. More recently, I have been searching for these people on LinkedIn. It is a question that begs to be answered – who are these people, what is their story, what do they care about, what do they want to achieve, how are they received within their HR organizations, what are they working on, what are their struggles? Those questions are a work in progress. For now, the fresh faces and backgrounds I see in these roles in pictures and Linkedin headlines is already truly inspiring to me. They surpass my imagination in magnitude and breadth and to me represent "pure energy". I think the people who are drawn to these roles are special and amazing. I have no doubt that as a collective they/we will ultimately work through the difficulties to reach our destination (Destination? This too is a great question). “Honour and recognition” hopefully forthcoming. In real life who would respond to an ad like Shackleton’s? I think a high proportion of those people today would be these People Analysts. The Vast Expanse of Human Resources The real HR is vastly underestimated and mostly lost on most people without direct exposure to a leadership role in HR in a large modern organization. By large I don't mean 500, I mean 50,000. HR doesn't really come into the spotlight until you reach 5,000 employees. At this point, you start to realize the inherent value in getting HR right - that is, if it is not too late for you! If you reach 50,000 employees you probably have figured HR out and now you preach it, never-the-less you are in a world of much bigger HR issues. If you are in a leadership position in HR for one of these companies few people know your precise struggles. It is a lonely world. If you could peer inside the little windows you would see that HR in large modern organizations is a complex network of technology, policy, process and people within discrete areas of specialization. HR can be grouped broadly in categories of Staffing (Sourcing, Recruiting, Onboarding), Total Rewards (Compensation & Benefits), Diversity, Talent Management and Organization Effectiveness (Performance Management, Succession Planning, Organization Design), Training and Development, Employee and Labor Relations, and HR Law & Govt. Compliance.It is a veritable alphabet soup of things to learn, with an entire language of its own. Sounds crazy, but yes, it is a diverse world of very real and very different things, all in this thing we call "HR". If you want to dive deeper you can find more granular silos of responsibility, but I will refrain from the arcane details and just leave it at the high level. I always forget - how many ways can eskimos describe snow?, and why? I don't know the answer, however I can surmise that they must spend a lot of time with the stuff. This is a good place to pause – are you still with me? Here is a more down to earth human story for you. Several times I have spoken to recruiters (an actual real life role in HR), and as I attempted to describe the complexity of tieing out data from many HR sources to some meaningful business conclusion and what that actually requires I have been interrupted with a reply something like - “It is nice you have had exposure to all those other things, but to be clear in this role you will deal exclusively with HR data. Are you o.k. with that?” With HR data? After only just having described a variety of HR data sources, not really even making the point about how these sources should be tied to business data, I am perplexed at who and what I am dealing with here. I am left to assume that by HR, they mean Staffing, a subset of HR and the rest of it is lost on them. Clearly, per their guidance it will also be lost from me and the organization if I take this job under these conditions. Do I correct them, or proceed? I am sorry to be blazingly disrespectful, but I have been tempted to stop right there and say, “Hello, Hello, are you still with me?" Should I proceed ahead or turn back? O.k., for now I will spare everyone the graduate level course in Human Resources but if you would like one they offer specialized programs in this stuff at the University of Minnesota, Cornell, Rutgers, University of Illinois, USC, somewhere in Michigan, and a few other schools! Some of the world's most respected CHROs of the largest organizations of the world came from those programs - I’d be happy to make introductions. By now you are rolling your eyes at me. No problem, I’m used to this. I am not sure what other people were doing when Shackleton was hacking away at the ice that was destroying the hull of his ship - I am sure his choices in life made a few eyes roll as well. Leadership and decision-making for HR sub-functions is distributed among members of the HR Leadership Team (HRLT). In large organizations the HR Leadership Team generally consists of 7-10 Sr. Directors reporting to a “Chief Human Resources Officer”(CHRO, SVP of HR, etc.). The Chief Human Resource Officer may have leadership, decision-making authority and budgetary control over the HR function, but in some cases the big budgetary decisions may go all the way up to the CEO and/or the board. Staff/Employees/Humans typically represent a spend of 50-85% of most modern organizations revenue. Usually over 75%. Stop - check it out in the annual reports, these days available on the internet. These are real numbers. Where is the profit? It is the little bit that is left over after all those people are done with their work and paid. The shareholders are buying a share in that little bitty slice. "Our most important asset". Have we looked at how we do HR with data - not much, not really - we know it is somewhere down there - it's a line item. In most advanced organizations the head of HR will report to the CEO, but often they report to the head of Operations or Finance – this varies by company. It is usually a sign post – a little piece of ice floating – be sure to keep an eye on it, something big may actually be under this ice. Regardless, wherever they report, it is contingent upon the CHRO to drive alignment of HR goals with “the business” and alignment between HR sub-functions on a central strategy and operational architecture for Human Resources. Seen as a “support function” annual HR goals and budgets typically FOLLOW development of business strategy and goals. HR goals are assembled in a last minute hurried manner sometime AFTER all the other business functions have had their shot at the podium. If those others strategies and goals are not yet fully clear, or the HR/People consequences cannot yet be ascertained with the information on hand, or the HR leadership team fails to communicate the people implications of the business goals, then HR will follow the business in a misaligned and disheveled path. In my experience, these are accurate actual descriptions of HR goals. Whether or not the HR team is able to formulate a coherent plan HR has last shot at organizational resources - consequently HR efforts are often unclear, underfunded and unrealistic (relative to the magnitude and difficulty of the goals). Examples of strategic HR objectives include: “Improve Employee Morale”, “Improve Employee Engagement”, “Reduce Employee Turnover”, “Change our Company Culture”... Hey, a big challenge excites me, but FYI these population level averages are resistant to dramatic changes. I’m not saying they don’t matter or can’t ever change – the polar icecaps are melting as we speak - I am saying moving these things in a different direction than they want to go requires serious attention to detail, resources, teamwork and commitment. It starts with data – let’s try to have a look at it together. Before Data, HR Systems Historically HR has vacillated between a single system ERP (Enterprise Resource Planning System) or HRIS (Human Resource Information System) that is average at everything or a series of “best of breed” applications that are better at a single purpose functions (Applicant Tracking Systems, Performance Management Systems, Compensation Systems, Learning Management Systems, Time and Labor Systems, etc…) Since HR CHROs and/or critical stakeholders in HR change over every 2-4 years you can guarantee exchange of favored technology to solve whatever shortfalls exist. Meanwhile, within a few years of implementing a solution we have new leadership and who prefer a newer, more optimistic path. Never wanting to get trapped in the ice, there is constant pull towards fragmented systems stemming from HR sub-function implementation of “best of breed” applications that align with HR sub-function operational objectives. If I am the head of Staffing and my team is tasked with improving Staffing, why would I settle for anything less than the best Applicant Tracking System? If I am the head of Compensation and my team is tasked with getting a grip on Total Rewards, why would I settle for anything less than the best Compensation Management System? So on and so forth. There is nothing wrong with this, however it results in a lack of technology optimization across sub-function silos and some important data consequences that must be addressed before or during reporting and analytics. Larger, established HRIS systems (PeopleSoft, Oracle, SAP, Lawson, and recently Workday), have more dependencies to worry about and thus are inherently slower to innovate than the collective of single purpose systems. The core systems will always lag in one or more sub-function operational areas. For example, even 10-15 years ago you could facilitate the Performance Management on SAP or Oracle HRIS, however Success Factors came along and offered a solution that was designed for this and consequently much better at it. So for 5-10 years Success Factors took the HR market for Performance Management, creating a hundred million dollar market and adding yet a new system category for HR to manage and integrate. Success Factors was later acquired by SAP (for 3.4 Billion) , and to this day can still be purchased by your organization as a stand-alone Performance Management application, with or without SAPs HRIS product. To this day most of the organizations I have worked with or for have NOT fully integrated Performance reporting between Success Factors and their HRIS. They prepare for the process to begin by manually setting and loading a file. There is someone deep within the bowels of the organization doing several spins on this. Probably also with a smart phone going off widely in the middle of dinner with the family - if this person even gets dinner with the family for a month or two of their life. Getting it out and rejoined to a changing organization and broader analytical purposes is another thing entirely. You will then discover, you can't export all the data you want directly from the same report, and there may not even be a common key! You might ask, “You mean to tell me we have no efficient method to join and report this data that is central to HR and to the business? Are you kidding me?" No. We have seen the same in Staffing, Learning Management, and Compensation. Compensation is especially bad - I like to call Compensation Planning (an annual event at every large company) a "planned emergency". 99.9% of the problems is embed in systems, 100% of it is caused by choices made by humans. By virtue of the many simultaneously moving fronts of sub-function innovation HR will undergo constant fragmentation and change in systems. Innovation in HR systems is good for us, but it is also very disruptive. Let’s Talk About HR Data Contrary to widespread belief HR actually has a lot of data, and a lot of good data. It is just locked away in systems not designed at all for reporting or analysis. Universally, HR systems are designed to facilitate operational processes and while they can perform their intended operational functions well (by design), they often do so at the expense of reporting and analysis. I will take a bucket of ice water over the head if someone shows me a single HR system that can perform a chi-square or binary logistic regression out of the box. How about any statistics beyond addition, subtraction, multiplication and division . We are waiting. Still waiting. Frankly, I like to keep my statistics and data visualization software options open – I don’t want my HR system to do everything for me, but I do need to be able to get the data out of my HR systems. Often even this seemingly basic task (get the data out) is difficult. Apparently, nobody thought of about getting the data out. If you are laughing, stop laughing. I’m not kidding. We can’t get deep insights or complete picture of the story until we get the data out of multiple systems, join it, and have a look at it through applications designed to work with data for reporting and analysis. Most HR “Analysts” are cobbling data together inefficiently with manual, undeveloped or broken data process. Some 80% of the effort of HR Analyst efforts are attributed to manual augmentation of non existent or inefficient data workflow. They spend very little time on actual science, statistical analysis and presentation of analysis. I have been an HR Analyst in one form or another for over 15 years, I speak to HR Analysts weekly, and I have seen the surveys. HR data are/is complex because people and organizations are complex and the sub-functions to support HR (as described at high level in above) are varied. There is hardly any similarity in reporting between Compensation and Benefits, let alone Staffing, Training or Employee Relations. Who owns Turnover reporting? Who owns the Employee Survey? These come from completely different systems, with different data structures and different metrics. The desired metrics can escalate into the hundreds and with variations in the thousands. Most HR metrics are compound and with dozens of potentially relevant dimensions to monitor. Let's take a look at a very commonly produced and seemingly simple HR metric: “Employee Attrition/Turnover”. This metric is formulated as a compound calculation (Time Period Exits / Time Period Average Headcount). Now before you shout "Eureka! - I have it - you just divide this number by this other number" keep in mind that you will need to calculate this by segments along multiple dimensions: location, division, business unit, tenure, grade/pay group, performance group, age, ethnicity, gender, etc.… You may have 50 locations. Some locations have 5 people and some have 15,000 people. What if you want division by location by gender. Also keep in mind that in annual form the denominator (average headcount) requires 12-13 or more data points for each of subset of each dimension, and these subset counts must all align in definition and time period consistently with the numerator. Also keep in mind that organizations can be expressed in different ways (people reporting relationships or cost center reporting relationship, which do not typically match), and that the only constant is that organizations are constantly changing. Imagine a data set that changes simultaneously along multiple dimensions over time and you are trying to report on this consistently over time to demonstrate a "trend" or "story" - how exactly does this work? If you are flabbergasted, don't worry, most of us People Analysts can work this problem in our sleep. This isn't even the hard stuff. I just described the calculation of one metric, now multiple this effort by 20 more metrics with data sitting across different systems and try to derive some meaning from that. Shouldn't we be analyzing this stuff together to tell a story, not independently? Most of the people doing good work on employee turnover have long ago moved on to more advanced ways of looking at exits (logistic regression, survival analysis, hazard curves, predictive models, etc.) and incorporate data from many many more data sources. I can ask employees three questions and based on their answer tell you if their chance of leaving in the next year is "about average, 2x average or 3x average" without knowing anything else about them. Give me their job and salary and tenure and a few other details and we can clean up on this problem. Let me join it to performance and compensation and now you can decide carefully how you want to distribute the limited budget you have to work with. Why not? If you are not working on this, what exactly are you working on? Speaking of clean up. For most organizations, you will get started on a seemingly simple problem, and bang into an inconsistency or perceived data quality problem, which requires people sitting in another sub-function you have no authority over to make a decision, fix a process, or change how they do something. This is not simple any longer – this is really difficult. You can circle on the same issues for years. Welcome to People Analytics, Are You Sure You Really Want This Job? Then There Were People HR professionals are not selected for a background in technology, math or science and so depend on people outside of HR for augmenting support in these capacities. Since HR is seen by “the business” as lower in priority than other business functions (software engineering, finance, marketing, sales, etc.), and therefore not a very prestigious appointment for anyone - IT and data science support suffers. The head of HR is on the phone about something they want and the head of Sales, which call should I take? If HR is already underfunded for its goals, HR IT is even worse, not receiving adequate talent pool or funding. Budgets are divided among the heads of functional HR silos and so no unifying technology solution can be reached. There are simply too many different jobs to be done in too many different systems. Data architecture for this is an after-thought. Each Sr. Director will compete for the time, attention and resources of whatever HRIS or HR Analytics professionals exist to try to achieve their objectives first (at any and all cost to others –their performance rating is on the line!). If they cannot get that attention they want from IT or from their HR Analysts they will try to go outside the organization for the support. Talk about being crushed in the ice. You will be loved and despised, sometimes consecutively, sometimes simultaneously. How this can happen is one of the great mysteries of the universe - outside of light being possible to be described both as a particle and wave. It is sort of like the possibility of dying of thirst while standing on water (ice). Input fire and maybe drown. Now I am being dramatic. This is just to say that I think Shackleton and company did a pretty good job, given the odds. I have had opportunity to be in these data oriented HR roles for variety of very well respected organizations (Merck, PetSmart, Google, one of the best Children’s Hospitals in the country to name a few) - believe me when I say these HRIS and HR Analytics folks have many more “bosses”, a combined list of objectives much longer, technically more complicated, and with less resources than anyone else. There high turnover among HRIS and HR Analytics professionals for a reason. You got one. Great. I hope you have a backup plan! Other People Analytics posts by Mike West ---------------------------------------------------------------------------------------- Who is Mike West? Mike has 10+ years of experience building People Analytics from the ground up at companies such as Google, Merck, PetSmart, Children's Medical, Jawbone and other places. Mike's passion is to develop thought leadership in HR and to cross pollinate the frameworks and processes he helped develop and pioneer as an employee at these places. Mike spends most of his time teaching, coaching and writing on all things People Analytics.
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Stacia Damron
It’s sounds ridiculous, but it’s true. According to the New York Times, 4.2% of women held CEO roles in America’s 500 largest companies. Out of those same 500 companies, 4.5% of the CEO’s were named David.* While shocking, unfortunately, it’s not incredibly surprising. Especially when a whopping 41% of companies say they’re “too busy” to deploy diversity initiatives. But for every company out there that’s “too busy”, there are plenty of others fighting to get it right. Take Google, for example. In 2016, Google’s tech staff (specifically tech roles - not company-wide roles) was 1% Black, 2% Hispanic, and 17% women. They announced a plan to invest 150 million in workforce initiatives. The tech staff is now 2.5% Black and 3.5% Hispanic/Latinx, and 24.5% female, according to their 2018 diversity report. So what does that mean? It means that even the brightest and most innovative companies have their work cut out for them in regards to improving diversity. Change doesn’t happen overnight. Diversity breeds innovation; a diverse talent pool leads to diverse ideas. Get this; a Forbes article touts that transitioning a single-gender office to a team equally comprised of men and women would translate to 41% in additional revenue. “Metrics” (which is just a fancy word for data btw) don’t lie. It’s important to set, track, and monitor workforce diversity goals - especially when we have more tools than ever at our disposal to do so. Over the past few years, here at One Model, we've seen a huge push for placing a priority on monitoring diversity metrics. In 2016, a Fortune 100 financial services organization, Company X (name anonymized) selected One Model’s platform to measure and monitor company-wide trends in diversity data and metrics. As their people analytics and workforce planning solution, One Model allowed them to not only better report on their data - but also more easily track and monitor changes, determine key KPIs, and see how improvements they’re making internally are affecting the data. More Accurate Data = Better Reporting. During Company X's transition from SAP to Workday, they used One Model to retrieve and migrate survey data. This platform allowed them to combine and normalize the data from several sources, enabling the team to report off of it as one source. The successful migration provided the HR team with the recovered data and prevented the team from having to redeploy the survey, allowing them to more accurately reflect their current diversity metrics and progression towards goals. This was a win. Here’s the challenge: When pulled together, the data referenced above indicated that out of several thousand employee responses, a number of employees failed to select or identify with one of the given race selections. This represented a sizeable portion of the employees. One Model’s software helped them identify this number. Once they realized this, they realized they had an opportunity to setup other processes internally. They did just that - which helped identify 95% of the employees who fell within that group, obtaining vital missing data that raised the percentage of diversity within the organization. Determining Key KPIs and Measuring Improvements Furthermore, Company X used the One Model platform to identify and reward the departments that successfully hit their recruitment-based diversity goals. This allowed the team to survey these departments and identify the hiring trends and best practices that led to these improved diversity metrics. By identifying specific process and KPI’s surrounding these diversity metrics, departments that successfully met their goals could share recruiting tactics and best practices to ensure appropriate actions were taken to maximize diversity throughout the whole of the recruiting pipeline. Company X is currently implementing these processes and working towards replicating a similar outcome amongst other departments in need of workforce diversity improvement. Tracking and Monitoring Changes Last but not least, Company X wanted more visibility into why females had a lesser presence in managerial roles within the organization. While, male to female promotions were equal. (This past year, 32 people were promoted. 55% of promotions (16 people) were women), there were significantly more males than females in managerial roles. Upon reviewing the data, they learned that out of the company’s requisitions, females applicants only made it to certain stages within the interview process (namely, an in-person interview) 50% of the time. Half the time, the only applicants that made it to a particular stage were male. They determined a hypothesis surrounding a particular KPI - that if more females made it to this particular stage, the odds were higher that more females would fill these roles. Company X set a goal that they wanted a female candidate make it to a manager interview stage 80% of the time. They are testing different methods on how best to achieve this, and with One Model's help, they are able to measure the effectiveness of those methods. By providing this visibility, One Model’s platform is currently helping them monitor their progress towards this goal, and allows them to see the affect - the direct impact on numbers of M/F managers in real-time. Company X is one of the many companies that has realized and embraced the importance of diversity in workforce planning. We’re confident they’ll eventually hit their goals, and we’re proud to be a part of the solution helping them do so. Is your company ramping up it’s People Analytics Program or diving into workforce diversity initiatives? One Model can help you better view and report on the data associated with your diversity goals. Here are just a few of the top metrics companies are currently focusing on: Recruitment Metrics Representation Metrics, such as: Minorities / URMs Veterans Women IWDs Staffing/Placement Metrics Transaction Metrics Training Metrics, such as: Penetration of diversity-related training, general training participation rates, and demographics of talent pipeline Advancement Metrics External Diversity Metrics Culture / Workplace Climate Metrics *based on 2016 NYT data. Want to see what One Model can do for you? Scheduled some time to chat with a One Model team member. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own as you need to. We provide a full platform for delivering more information, measurement, and accountability from your team.
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