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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
5 min read
The One Model Team
HR has unprecedented access to workforce insights. With the right tools, people teams can influence major decisions, anticipate risks, and help steer their organizations with precision. But how is it going? Despite having more metrics than ever, most leaders don’t fully understand where the data comes from, how it’s processed, or how to explain it convincingly when challenged. This uncertainty limits HR’s ability to lead with authority, leaves them vulnerable in high-stakes discussions, and keeps them from realizing the full strategic value of their analytics. The Black Box Dilemma in HR Data HR teams are often caught in a tough spot. They are expected to present complex workforce data to business leaders, but they rarely own the systems that generate that data. Instead, teams like HRIT, HRIS, compensation, and talent acquisition manage the raw data and its architecture. HR leaders typically interact with metrics through polished dashboards or summary reports—without seeing under the hood. This creates a "black box" effect: numbers appear, but the process behind them remains murky. When business leaders—many of whom are comfortable interrogating data—start asking questions, HR professionals can feel exposed. If you’ve ever presented a headcount trend only to be met with: "How exactly is this calculated?" or "Why is this figure different from last quarter’s report?" …then you know this uncertainty. Without direct visibility into the data pipeline, HR struggles to provide clear answers, leading to friction, credibility loss, and cautiousness in future reporting. Do you trust your HR analytics? Hear what Hayley Bresina, one of our People Analytics and AI experts, says is the real danger of low data confidence when making strategic decisions. The Confidence Effect Being able to stand behind your numbers is powerful — but that’s just the beginning. Data confidence is the backbone of HR’s credibility and the key to driving real strategy. Without it, HR’s influence shrinks. Decision-makers hesitate, strategic initiatives stall, and HR is reduced to a support function rather than a strategic driver. Jaime Dyer, a former leader at Gap and current VP of Customer Success at One Model, described how differing definitions and lack of a “single source of truth” created inconsistent reporting that eroded confidence across her organization. Business units hesitated to act on HR data, and leadership grew wary of pushing forward big initiatives without airtight proof points. What Happens When HR Owns the Numbers On the flip side, when HR has true confidence—rooted in knowledge of data sources, definitions, and processing—they can: Speak with authority on trends and insights Build trust with business leaders Challenge assumptions with evidence-based recommendations Ensure decisions are backed by reliable, accurate data Mitigate compliance risks and safeguard against costly errors Knowing why data confidence matters is only part of the puzzle. In Part 2 of this 3-part series, we’ll discuss how HR leaders actually gain trust—and sustain it—in the real world. Want to see what trustworthy data looks like? Connect with us.
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Featured
4 min read
The One Model Team
No HR leader ever said, ‘I wish my analytics were more rigid.’ HR and People Analytics teams are under pressure to keep up with change, from shifting work models to surprise data sources. That’s why we built One Model: not just to handle change, but to thrive in it. Flexibility and adaptability aren’t bonus features. They’re baked into every part of our platform. Start with What You Need Most traditional HR systems ask you to shape your data around their structure. We flipped that on its head. From the start, One Model was built with an open architecture that lets organizations work the way they want to. Bring your own data models. Use your own methodologies. Whether you’re tracking employee performance or new hire turnover or anything in between, our platform lets you start with what matters to you—then scale, flex, and shift without having to start over every time something changes. And when something does change? You’re ready. When Priorities Pivot, You Won’t Miss a Beat From compliance issues and return to office initiatives to sudden reorganizations, today’s HR priorities shift fast—and your analytics platform needs to keep up. One Model is built to adapt with you. Whether you’re adding new data sources, adjusting key metrics, or deploying new models, the platform all ows you to evolve your insights without disrupting existing workflows. Take flexible work policies, for example. If leadership suddenly needs to understand office attendance trends or assess how remote work is impacting productivity, One Model can easily incorporate new inputs, like badging systems or collaboration tools and enable your team to build updated models quickly. No waiting on IT, no starting from scratch. One Model, Built for All Your Whys We get it, no two organizations think about workforce data the same way. That’s why we don’t offer cookie-cutter dashboards or inflexible templates. One Model adapts to your strategy, not the other way around. Our customers use the platform to solve problems as unique as their teams: predicting turnover among critical roles, evaluating equity in promotion patterns, or measuring engagement across continents. Whatever your “why,” the platform is built to give you fast, relevant, and trusted insights. Hear our team describe what adaptability means for you. It's so foundational to our platform that it's even found in our name. Not one model for all, but one model for each client. Ready to see a data model that flexes your way? Let's connect.
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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
1 min read
Lauren Canada
People Analytics doesn’t have to be a security headache. This quick visual guide breaks down how One Model keeps your data safe—without limiting access or slowing you down. Click here to view the full infographic. Click here to view the full infographic. Click here to learn more about how One Model protects your data.
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Featured
3 min read
The One Model Team
The ability to adapt isn’t a perk—it’s survival. In a world where AI rollouts happen overnight and workforce expectations shift just as fast, agility in people strategy has become a defining trait of resilient organizations. Why Adaptability is the Hidden Engine of HR Resilience A decade ago, HR teams could build five-year plans with confidence. Now? Plans need to flex weekly. From post-pandemic recoveries to sudden market shifts, the pressure is on for HR to guide companies through uncertainty—not after the fact, but in real-time. And yet, many still operate with rigid systems that weren’t built for speed. These legacy tools might have supported steady growth—but in today’s climate, they can’t keep up. When a new regulation lands, attrition spikes, or DEI priorities evolve mid-year, leaders need answers now, not in next quarter’s report. The Hidden Cost of Rigidity When systems are too rigid to accommodate real-time shifts, HR teams spend more time patching than progressing. Trust, momentum, and morale suffer. For many organizations, when new compliance rules hit, their HR system can’t adapt. What should take days can stretch into weeks, exposing gaps and eroding internal confidence. Meanwhile, an agile competitor using flexible analytics could potentially retool their processes in under 48 hours—no disruption, no drama. Adaptability doesn’t just save time. It saves reputations. Flexibility in People Analytics: The Game Changer The good news? HR teams don’t need a crystal ball—they need flexible analytics. Platforms that allow new data sources to be added quickly. Dashboards that evolve with changing business questions. And insights that support action before trends harden into problems. Staying Ahead Isn’t Optional Adaptability isn’t just about responding to disruption. It’s about building a muscle that lets your organization learn, shift, and thrive. Want to learn more? Request a demo today.
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Featured
7 min read
Chris Butler
Organizations often assume pushing workforce data directly into a general-purpose warehouse like Snowflake before connecting to an analytics platform is an optimal strategy. While this could be appealing for enterprise consistency, this warehouse-first approach overlooks critical workforce-specific requirements. Starting with One Model's comprehensive data extraction and modeling capabilities before moving to an enterprise warehouse ensures greater success, efficiency, and analytical accuracy. The Hidden Complexity of Workforce Data Workforce data is dynamic, constantly evolving, and deeply intertwined with organizational operations. Generic data warehouse systems are effective for storage but lack specialized capabilities for extracting and modeling HR-specific complexities such as transaction-based employee data, hierarchical structures, and frequent retroactive updates. Where a Warehouse-First Approach Falls Short Organizations considering a warehouse-first strategy should recognize several limitations: 1. Delayed Implementation and Compromised Data Integrity HR data extraction requires specialized and time-sensitive transaction log-based incremental updates, not repetitive snapshots, to avoid redundancy, inflated costs, and reduced query performance. Without dedicated subject matter expertise or experience extracting HR data into a warehouse, this complexity can delay HR time-to-value significantly and introduces unintended engineering decisions that compromise data quality. 2. Complex Workforce Data Modeling Generic data warehouse solutions lack pre-built schemas necessary to handle nuances of sensitive workforce data like performance, terminations, rehires, and salary adjustments accurately. Starting from scratch on these builds can lead to downstream analytical inaccuracies and inefficiencies when making use of data that isn't modeled properly. 3. Manual Maintenance and Increased Security Risk Warehouse-first strategies require continuous manual oversight and updates of both pipelines and stored data, increasing the risk of errors, compliance issues, and security vulnerabilities. Managing and maintaining these challenges manually in-house can lead to time delays which in turn diminishes organizational trust in analytics. 4. Unprepared for Advanced Analytics and AI Data warehouses typically optimize data storage and architecture for basic querying and lite reporting rather than deeper analytical processes like predictive modeling or preparing data for use in AI initiatives. Without specialized modeling, organizations face severe limitations in extracting strategic value from their workforce data. The One Model Advantage One Model is a comprehensive, full-stack solution designed explicitly for People Analytics, emphasizing openness, flexibility, and transparency. One Model provides expertise and support while also allowing your team full visibility and control over data transformations through accessible SQL code, facilitating seamless integration and customization. Specialized Data Extraction One Model's managed connectors efficiently handle complex data extraction at scale from industry leading HRIS systems like Workday, Oracle, and SAP, as well as hundreds of systems across the HR tech and Work Tech stack. Unlike brute-force methods taking monthly snapshots, One Model intelligently captures meaningful data changes nightly, significantly enhancing scalability, accuracy, and speed. Self-Healing Data Models One Model utilizes self-healing data models powered by advanced algorithms and AI-driven error correction. One Model tools continuously monitor and automatically correct data anomalies, ensuring ongoing accuracy, especially critical for managing common HR challenges like retroactive updates. Built-in Compliance and Security Sensitive employee information demands stringent security. One Model strictly adheres to industry-leading compliance standards such as SOC 2 and ISO 27001. The platform incorporates robust role-based access controls, ensuring secure, compliant data management, and protection. Flexibility and Control in Data Flow One Model supports flexible data flows, whether directly from source systems like Oracle or Workday into One Model, or subsequently pushing refined data to warehouses like Snowflake. One Model can also ingest directly from warehouses like Snowflake for ad hoc data loads or full system loads of historical data. Organizations retain complete control, choosing precisely how and when data moves, ensuring no compromise to data integrity, governance, or analytical capability. The Right Data in the Right Place Even with the challenges outlined above, there is clear strategic value in centralizing workforce data within enterprise warehouses like Snowflake, Redshift, or BigQuery. The issue isn't the warehouse itself but rather how the data arrives there. A warehouse-first approach mistakenly treats workforce data like just another standard dataset, missing the complexity and specialized structure needed for effective analytics. To bridge this gap, One Model created the Data Destinations toolkit. Rather than forcing a choice between HR-specific modeling and an enterprise-wide warehouse strategy, Data Destinations ensures that once the data is modeled in our platform, organizations can easily push carefully structured, analytics-ready, and secure workforce data directly from One Model into their warehouse of choice. This maintains the integrity, accuracy, and privacy of the workforce data, making your warehouse strategy not only scalable but strategically valuable. Conclusion Starting your People Analytics journey directly with One Model addresses the foundational concerns raised at the outset: managing complexity, ensuring data accuracy, and maintaining compliance and security. Unlike the generic warehouse-first approach, One Model prioritizes capturing and modeling workforce data in its raw, uncompromised form, specifically tailored for the intricate demands of HR analytics. By opting for direct integration, your organization gains not only superior analytical capabilities and efficiency but also robust security and compliance that are built directly into the data pipeline. Rather than forcing HR data into a one-size-fits-all solution, One Model’s specialized approach empowers your analytics team to produce precise, actionable insights. Ultimately, investing first in One Model ensures your HR analytics infrastructure is both strategically aligned with business objectives and resilient enough to adapt and evolve with organizational needs, transforming your workforce data into a reliable driver of informed, strategic decision-making. Want to learn more?
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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
4 min read
Steve Hall
When it comes to People Analytics, the most valuable tool is one that lets you to ask the right questions and explore solutions. Canned insights can't answer the real questions you need to answer. Recently, during a demo with a prospective client, a question came up that perfectly illustrates how One Model is a platform built for problem-solving rather than just offering irrelevant canned insights. The Situation: A Forecasting Challenge The scenario began with a focus on Female Representation metrics, specifically forecasting whether the organization was on track to meet its diversity targets for women. The forecast feature showed trends for different job levels, and while representation looked promising for some levels, there was a noticeable downward trend for the executive level. Naturally, the prospect wanted to know: Why is this happening? This was not a question with an easy, pre-packaged answer. Instead, it required a deeper dive into the data—an approach that highlights One Model's value as a tool for discovery and insight generation. Digging Deeper: How We Tackled the Problem To address the question, we demonstrated how to use filters and visualizations to isolate and explore the data. Here's how it unfolded: Applying Filters: We filtered the data by job level and gender to focus specifically on female executives. From there, we looked at key metrics like net hiring trends and termination rates. Identifying Patterns: The data revealed a significant drop in representation between 2023 and 2024, which appeared dramatic due to the auto-scaling of the graph. Exploring Causes: By clicking through different visualizations, we identified that termination rates, particularly "other" terminations, were higher than expected. Using One Model's hotspot maps, we further pinpointed the specific business unit and region where the issue was most acute. Forming Hypotheses: Using this information, we leveraged One Model's built-in predictive AI capabilities to identify potential turnover drivers and develop actionable insights. Flexibility Matters This scenario underscores something critical about One Model: We don’t solve all your problems; we give you the tools to solve them. Other platforms that rely on rigid, canned use cases might struggle in this situation; no solution can offer pre-built analyses for all possible scenarios. Without a pre-built guide addressing their specific issue in this specific organization, the user will hit a wall. One Model, by contrast, enables users to dynamically filter, explore, and analyze data to uncover answers. Why This is Critical for People Analytics This scenario demonstrates the real-world challenges of People Analytics. Insights are rarely handed to you on a silver platter. Instead, they require a combination of curiosity, exploration, and judgment —qualities not even AI will bring to the table. While some HRBP-level professionals might not engage in this level of analysis, advanced People Analytics practitioners understand that solving complex, niche problems—like representation trends at a specific level—requires more than surface-level data. The One Model Advantage Here’s why One Model is different: Speed: Because One Model creates a unified single source of truth for your organization, you can explore complex interactions without having to manually manipulate data, saving you time. Flexibility: You’re not limited to prebuilt Storyboards or canned content. You can adapt and dig into unique questions in real-time, even in situations where you need to create new metrics to explore an issue. Depth of Insights: By enabling dynamic exploration, One Model allows for nuanced and complete answers that out-of-the-box solutions can’t deliver. The takeaway from this use case is simple: Good insights require effort. Platforms that promise quick, prebuilt solutions often oversimplify problems or deliver incomplete answers. One Model’s strength lies in empowering users to dig deeper and uncover real insights—even when the questions are complex. With One Model, you’re not just using a People Analytics platform—you’re solving real problems.
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Featured
4 min read
The One Model Team
Having the right vendor partnership can make a huge difference. And the wrong one can lead to huge headaches. One Model understands this, and we strive to be more than just another software provider. We seek to be a trusted partner for both HR and IT teams, deeply entrenched in the success of both departments. By partnering with One Model, tech teams get: Expert resources to field HR’s requests A common challenge many businesses face is the reliance of HR teams on their internal IT for business intelligence (BI) support. This not only strains IT resources but also may not always result in optimal solutions tailored for HR needs. With One Model, HR gets access to expert People Analytics resources. This isn't just about having an extra set of hands; it's about having a skilled set of hands, well-versed in BI, ready to converse, collaborate, and create. More time to focus on IT initiatives With One Model, tech teams can channel their energies and expertise towards initiatives directly tied to their KPIs. Our proposition is simple: let us empower HR with solutions that meet their BI needs while IT reallocates their time towards other tech initiatives. This isn't about pitting departments against each other; it's about recognising and optimising strengths of both groups. Increased transparency and accessibility If there's ever a need for IT to get involved, no problem. One Model's platform is built on transparency. Developers can literally inspect the SQL, ensuring a seamless integration of our platform into your ecosystem. This creates a harmonious interplay between HR and IT, with both departments benefitting. A cost-effective approach to People Analytics The cost of hiring and maintaining a single data engineer is substantial, and it’s not easy to find IT candidates with People Analytics experience. Data engineers often earn an annual salary of over $110,000 each year. And this doesn’t even include additional expenses your organisation will need for data architects, project managers, and other resources — especially as you scale. Partnering with One Model's team is much more cost-efficient, allowing you to allocate your resources more strategically. “From the tech leader’s perspective, there’s a significant cost to having HR rely on your internal IT team for BI support. So as you consider building your own solution from scratch or buying a People Analytics tool, One Model’s flexible platform is ideal because we’ll partner with your HR team and deliver the best of both worlds. We specialise in supporting HR’s needs, so tech teams can focus on their own KPIs. And, if developers ever have questions, One Model is open enough for them to jump in and literally look at the SQL. It’s a win-win for HR and IT.” — Taylor Clark, Chief Data Scientist, One Model Navigating the complexity of people data While many development teams are adept builders, navigating the labyrinth of people data is a different beast altogether. A common misconception is that IT teams can effortlessly manage data extractions, transformations, and integrations from HR systems. The reality? People data is complex, intricate, and often disorganised. “Many IT teams are already handling data extractions, transformation, and integrations across HR systems. With that experience, the justifiable assumption is that People Analytics will be a straightforward project. But the challenges of People Analytics are unique. For example, creating historically accurate, effective dated data models across multiple systems. One Model is the only vendor that confronts these challenges head on.” — John Carter, Senior Sales Engineer, One Model With One Model, you're not just getting a People Analytics platform, you're gaining a partner skilled in deciphering, managing, and optimising people data. Where many falter, we excel. The challenges that often stymie others, like managing Workday's unique constraints, are where our expertise comes to the forefront. We do the heavy lifting, ensuring that HR's data needs are met so tech teams can avoid the typical complexities. Our approach isn't just about providing a platform. It's about building a valuable, long-term partnership and commitment to ensure the success of HR, IT, and the overall company. Ready to learn more Download our whitepaper Why Tech Leaders Prefer One Model’s People Analytics Platform to learn 4 key reasons IT teams choose our platform over others on the market.
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5 min read
The One Model Team
The Cost-Time-Quality triangle can be a helpful tool when comparing various technology options. This framework is commonly called the “Project Triangle,” but we have modified it here to break out Flexibility as a critical fourth element — creating a diamond shape. The rationale for extending the triangle to include Flexibility is that analytics in all domains is full of unknowns. A rigid design could never anticipate all the future use cases and content demands of an ever-changing world. Doing a meaningful comparison of solutions requires an understanding of the relative flexibility of the various options being considered. And so our suggested framework for comparing people analytics technology options is the Project Diamond. Comparing the three options Below you’ll find a generalised comparison of each option using the Project Diamond framework. These findings are based on our direct experiences and discussions with customers and other people analytics experts, so consider it illustrative. Cost To build a solution from scratch, you’ll still need to buy a bunch of technology (eg, BI tools, data warehouses, and hardware). And there are often many hidden costs associated with that approach. In a buy option, the cost is typically the technology license/service fee, which has a two or three year commitment, as well as any initial implementation services. In a build option, the cost represents the IT and PA resources that are needed to create the data model and metrics definitions, the warehouse and visualisation tool costs, and any required maintenance and change order costs. Opting for a platform that offers the best of both buy and build generally has the same initial entry cost as the buy option, but is less expensive overall since access to the skilled data engineers and an experienced customer success team augments your resources, supports additional requests, and reduces rework throughout your people analytics journey. Depending on the vendor you select, that type of support will cost you extra red tape, extra dollars, and potentially even extra resources towards manual work. Time Internal build projects nearly always run slower than planned, and they often fail altogether. There are countless stories and statistics on failed business intelligence projects. Buy options leverage pre-built assets to deliver a “turnkey” people analytics experience that can get you up and running relatively quickly. But for some vendors, implementation can be a long and drawn-out process. Instead, you could opt for the best of both buy and build. So you get a fast implementation experience with a proven and pre-built starting point, and you also get the ability to enhance and build upon that starting point over time — either on your own or alongside the vendor’s knowledgeable customer success team and skilled data engineers. Quality This element has more potential for overlap. There is wide variation in what may be built internally since internal IT teams have considerably less experience working with data from HR systems in an analytics context. While high quality builds can exist, they require superb internal IT resources and incredible amounts of time and money. A common downfall here is that the initial implementation team declares success and rolls off to another project, leaving a knowledge and capability gap. Buy could be better quality than a build since you get a pre-built starting point, but that depends on the vendor. Choosing a PA platform that can deliver the best of both buy and build will ensure the highest quality solution. This option allows teams to get the full value out of all their HR data — by centralising it into a single source of truth, transforming it into an integrated dataset purpose-built for people analytics, and configuring the platform and analyses to their organisation’s exact requirements. And if the vendor has a highly-skilled team of data engineers available to support, PA teams gain a partnership with talented individuals who can ensure the quality of the data assets they create. Flexibility The most significant gap is in flexibility. Internal build solutions usually involve multiple teams, and the data and analytics needs of HR must compete for resources with the business’ core product and customer data needs. Meaning the HR function often needs to wait in line for basic changes to their data warehouse and visualisations, and their simple request could be very challenging to execute. In a buy scenario, there is ongoing innovation from the provider as they need to remain relevant and competitive in the marketplace. But that vendor may not make the enhancements your team needs or allow for configuration within the tool they’re selling. If you’re looking for the best of both worlds, you’ll want to purchase a flexible PA platform that allows teams to either build their own data assets within the purchased solution, or partner with the vendor to support that build. The ideal vendor will focus on transparency, flexibility, and customisation — enabling people analytics teams to access the backend of the platform to configure their instance to fit their exact needs. Ready to learn more? As you use Project Diamond to assess your people analytics technology options, you may want to download our whitepaper The Evolution of the Buy vs. Build Conversation in People Analytics, which can help you use Project Diamond to determine if buying an out-of-the-box solution, building an in-house solution, or choosing a path that delivers the best of both worlds is right for you.
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6 min read
Tony Ashton
The recent announcement by SAP SuccessFactors to sunset its legacy People Analytics product leaves SAP SuccessFactors customers facing significant uncertainty. The sunset signals the deprecation of some* key reporting technologies used by SAP SuccessFactors —Canvas, Classic, Table, and Tiles and Dashboard Reports. With these SAP SuccessFactors reporting tools being shut down, businesses reliant on them face potential operational hiccups in their decision-making processes. However, this development also presents a unique opportunity to explore superior alternatives that can ensure continuity, innovation, and enhanced analytics capabilities. In other words, it could be the perfect time to consider an upgrade. Why Settle When You Can Ascend In contrast to SAP SuccessFactors’ winding down approach, maybe it’s time to look for a forward-thinking analytics and reporting platform that does double duty: Addresses the immediate gaps left by the SAP SuccessFactor updates and provides a robust foundation for future growth. Look for a solution that offers: Advanced Analytics and Reporting: Leveraging state-of-the-art technology to deliver deep insights and customizable reporting capabilities that grow with your business. Seamless Integration: Effortlessly merge data from various sources, including SAP SuccessFactors, ensuring a smooth transition and continuity of operations. Future-Proofing Your Analytics: Ensure that your chosen solution’s analytics capabilities evolve to meet future challenges head-on with continuous updates and a commitment to innovation. Learn more about getting People Analytics out of SuccessFactors and your other HR tools. Transitioning to a More Capable and Dynamic Solution The journey from SAP SuccessFactors' legacy reports to a more sophisticated and comprehensive analytics platform like One Model can be seamless and transformative. Get started by: Conducting an Analytics Audit: Understand your current analytics and reporting needs and how they might evolve. Evaluating One Model’s Offering: Explore how One Model’s features and capabilities align with your business objectives. Planning for Migration: Leverage One Model’s support and resources for a smooth transition, ensuring minimal disruption to your operations. While the deprecation of SAP SuccessFactors’ legacy reporting tools marks the end of an era, it also opens the door to embracing a more advanced, flexible, and comprehensive analytics solution like One Model's SuccessFactors People Analytics Solution. By choosing to upgrade, organizations can not only overcome the challenges posed by SAP SuccessFactors’ transition but also position themselves for stronger, more data-driven success in the future. Is it time for an upgrade? Embrace the future of analytics with One Model—where innovation, integration, and insight come together to drive your business forward. Note: After recording this video we noticed that SAP SuccessFactors had deferred a couple of their deprecation announcements. Table Reports and Canvas Reports will stay around for a while longer, while Classic Reporting and the Tiles & Dashboards are still being deprecated. This illustrates the complex data structures and variety of different technologies at play in the SAP SuccessFactors reporting landscape remains a challenge. For a complete answer to this, come and have a chat with us. We'd love to show you a better solution. Let us know you're interested, and we'll reach out to schedule time.
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8 min read
Richard Rosenow
When considering implementing a people analytics solution into your organization, an important first step is to consider if you should buy an out-of-the-box solution, build one yourself from scratch, or buy a flexible solution you can build upon. If you choose to build on your own, have you considered the ongoing maintenance requirements and costs you’ll encounter over time if you choose to build on your own? If you choose this DIY approach, you’ll have to constantly allocate valuable internal resources towards updating the system and keeping it running — pulling your teams away from more strategic and impactful work. Instead, you could partner with a trustworthy people analytics vendor to take that maintenance off your team’s hands. Let’s dive into what maintaining a people analytics solution entails and why it’s so important. Then, we’ll explore how choosing the right vendor can help you ditch the DIY drama and keep your people analytics solution running smoothly. The Continuous Journey of Maintenance The allure of developing an in-house people analytics solution is often marred by underestimating the ongoing commitment required for maintenance (as my colleague, Shiann, learned from her own in-house development lessons). Unlike the initial setup, maintenance is a continuous journey, marked by the need to adapt to new technologies, regulatory changes, and evolving organizational needs. The pitch from internal teams who want to build their own systems that a central pool of resources will keep the analytics platform updated often falls short when confronted with the reality of constant evolution in HR systems and practices. The Complexity of Maintenance Maintenance encompasses much more than fixing bugs or updating software; it involves adapting to new data sources, integrating evolving HR technologies, and ensuring all systems remain aligned with organizational objectives. The challenge compounds when internal teams are tasked with maintaining a system built from scratch, as they must juggle maintenance on top of fire drill tasks, innovation, and the strategic redirection of HR practices. Vendor Advantages: Specialization and Scalability Vendors specializing in people analytics bring a wealth of experience and resources dedicated to the development, deployment, and maintenance of people analytics solutions. Their focus on HR technologies and data models allows them to offer solutions that are not only up-to-date with the latest trends and technologies but also scalable to accommodate organizational growth and changes in HR practices. Expertise and Efficiency People analytics vendors are equipped with specialized teams that understand the nuances of HR data, ensuring that maintenance is not just about keeping the system running but optimizing it to deliver actionable insights. Economies of Scale By serving multiple clients, vendors can spread the cost of maintenance, research, and development across their customer base, allowing for more significant investments in innovation and security. Proactive Evolution Vendors continuously update their platforms to incorporate new features, integrations, and best practices, ensuring that the analytics solution remains at the forefront of HR technology. Navigating Vendor Selection and Partnership While the benefits of partnering with a vendor are clear, not all vendors are created equal. It's crucial to conduct due diligence to ensure that the selected vendor has a proven track record, a robust maintenance and support system, and the flexibility to adapt to your organization's unique needs. Experience and Compatibility Look for vendors with experience in the systems you need (HRIS, ATS, survey, etc.) and those who have successfully navigated the complexities of integrating diverse HR data sources into a unified model. Support and Maintenance Model Understand the vendor's approach to maintenance — whether it's a named resource tracking your account or access to a central pool of experts. Ensure that their support system aligns with your organizational needs and expectations. Subject Matter Expertise Review the vendor’s leadership team and customer teams for a background in HR or the people analytics space. There are many data vendors out there, but there are only a few that focus on and care deeply about what it means to work in HR. That nuanced understanding shows up in how they care about your needs, what new HR support tools are on the roadmap, and how they spend their time developing solutions. Scalability and Adaptability The chosen vendor should demonstrate the ability to scale their solution in line with your organizational growth and the agility to adapt to emerging HR technologies and practices. You don’t want to have to switch vendors later in your people analytics journey once you realize they can’t handle more complex tasks. Why One Model Is Your Maintenance Partner for People Analytics When it comes to the crucial role of maintenance in people analytics, partnering with a vendor like One Model offers a comprehensive and streamlined approach that can significantly enhance your team's efficiency and focus. Here's how One Model stands out as a true partner to HR and people analytics teams with maintenance tasks: Seamless Data Pipeline Maintenance One Model proactively manages data pipeline maintenance, especially in scenarios where a vendor changes their API — which happens often. This adaptability ensures that your analytics operations remain uninterrupted and consistently reliable, removing the burden from your internal teams to monitor and adjust to these external changes. Data Engineering Support Included With One Model, break-fix solutions and ongoing data engineering support are integral parts of the subscription service. This means your team has continuous access to expert assistance for any technical issues that arise, ensuring minimal downtime and optimal performance of your analytics platform. Integrated Platform Workflow One Model's platform is designed to work in harmony, ensuring that changes in the data orchestration tools People Data Cloud™ are immediately reflected in the data storytelling front end and OneAI advanced analytics toolkit. This integration eliminates the common headache of fixing broken dashboards due to data table changes, enabling a smoother workflow and more reliable data visualization. Monitored Site Reliability Ensuring the reliability of your people analytics platform is paramount, and One Model takes this responsibility seriously. By putting One Model in charge of site reliability, we provide peace of mind that your analytics tools will be available when you need them, supporting on-demand access to workforce insights. Focus on Analytics, Not Software Maintenance By taking on all software-related aspects of the build and maintenance, One Model allows your team to focus on what they do best: deriving meaningful insights from people analytics. This division of labor maximizes the value your team brings to strategic decision-making, consulting, and insight-creation, without being bogged down by the technical complexities of software maintenance. Learn why more enterprises are turning away from proprietary solutions Read the Evolution of the Buy vs. Build Conversation today The Case for Vendor Partnerships The decision to partner with a vendor for people analytics should not be taken lightly. It involves weighing the benefits of access to specialized expertise, efficiency gains, and the ability to stay ahead of HR technology trends against the perceived control and ownership benefits of an in-house solution. However, when considering the long-term implications, particularly in the realm of maintenance, the argument in favor of vendor partnerships becomes compelling. Maintenance is not merely a technical challenge; it's a strategic imperative that ensures the people analytics platform remains relevant, effective, and aligned with organizational goals. In this context, vendors offer a partnership that transcends the mere provision of technology; they become collaborators in the journey towards achieving HR excellence. In conclusion, as organizations navigate the complexities of modern HR practices, the choice of partnering with a vendor for people analytics offers a strategic advantage. It ensures access to cutting-edge technology, specialized expertise, and a scalable solution that evolves in tandem with the organization. The maintenance of a people analytics platform is a journey best undertaken with a partner like One Model who brings not only technology but also a commitment to innovation and excellence in the field of HR analytics.
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5 min read
Joe Grohovsky
In a recent editorial (here), Emerging Intelligence Columnist John Sumser explains how pending EU Artificial Intelligence (AI) regulations will impact its global use. A summary of those regulations can be found here. You and your organization should take an interest in these developments and yes there are HR legal concerns over AI. The moral and ethical concerns associated with the application of AI are something we must all understand in the coming years. Ignorance of AI capabilities and ramifications can no longer be an excuse. Sumser explains how this new legislation will add obligations and restrictions beyond existing GDPR requirements and that there is legislation applicable to human resource machine learning. The expectation is that legal oversight will arise that may expose liability to People Analytic users and their vendors. These regulations may bode poorly for People Analytics providers. It is worth your while to review what is being drafted related to machine learning and the law as well as how your current vendor addresses the three primary topics from these regulations: Fairness – This can address both training data used in your predictive model as well as the model itself. Potential bias toward things like gender or race may be obvious, but hidden bias often exists. Your vendor should identify biased data and allow you to either remove it or debias it. Transparency – All activity related to your predictive runs should be identifiable and auditable. This includes selection and disclosure of data, the strength of the models developed, and configurations used for data augmentation. Individual control over their own data – This relationship ultimately exists between the worker and their employer. Sumser’s article expertly summarizes a set of minimum expectations your employees deserve. When it comes to HR law, our opinion is that vendors should have already self-adopted these types of standards, and we are delighted this issue is being raised. What are the differences between regulations and standards? Become a more informed HR Leader by watching our Masterclass Series: Why One Model is Preferred when it comes to Machine Learning and the Law? At One Model we are consistently examining the ethical issues that are associated with AI. One Model already meets and exceeds the Fairness and Transparency recommendations; not begrudgingly but happily because it is the right thing to do. Where most competitors put your data into a proverbial AI black box, One Model opens its platform and allows full transparency and even modification of the AI algorithm your company uses. One Model has long understood the HR law and how the industry has an obligation to develop rigor and understanding around Data Science and Machine Learning. The obvious need for regulation and a legal standard for ethics has risen with the amount of snake oil and obscurity being heavily marketed by some HR People Analytics vendors. One Model’s ongoing plan to empower your HR AI initiatives includes: Radical transparency. Full traceability and automated version control (data + model). Transparent local and model level justifications for the predictions that our Machine Learning component called OneAI makes. By providing justifications and explanations for our decision-making process One Model builds paths for user-education and auditability for both simple and complex statistics. Our objective has been to advance the HR landscape by up-skilling analysts within their day-to-day job while still providing the latest cutting edge in statistics and machine learning. Providing clear and educational paths to statistics is in the forefront of our product design and roadmaps, and One Model is just getting started. You should promptly schedule a review of the AI practices being conducted with your employee data. Ignoring what AI can offer risks putting your organization at a competitive disadvantage. Incorrectly deploying AI practices may expose you to legal risk, employee distrust, compromised ethics, and incorrect observations. One Model is glad to share our expertise around People Analytics AI with you and your team. High level information on our OneAI capability can be found in the following brief video and documents: https://bit.ly/OneModelPredictiveModeling https://bit.ly/OneModel-AI https://bit.ly/HR_MachineLearning For a more detailed discussion please schedule a convenient time for a personal discussion. http://bit.ly/OneModelMeeting
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11 min read
Shiann Weiss
Over the course of my career, I’ve had the privilege of working with several awe-inspiring developers and building some pretty cool proprietary tools before I arrived at One Model. Every project was unique, and I experienced first-hand the rub between business, development, and internal end-users. From working toward MVP (minimally viable product) and beyond to switching away from proprietary solutions, I’ve seen both success and failure along the way. Here are just three failed IT projects from my trove of stories that I feel everyone could learn from. I hope that my experiences can help us all consider the possibilities and rethink building a proprietary people analytics solution. The Build from Scratch At a previous company, we had a special, custom report generated for all of our clients that we built from two data sources: publicly scraped information and connected account-based search engine data. We had two critical needs that made a proprietary solution very appealing: If we solved for the collection and transformation of the data, we could drop our unit price from 6-8 cents per unit to 2-3 cents per unit. With the number of units we needed to purchase on a weekly basis, this would make a considerable impact on our bottom line. Compared to other store-bought solutions, we felt the visualizations of our reports were just too unique. If we couldn't replicate those reports exactly and reduce the resources needed to generate them, a build wasn't going to be worth it. What happened? We evaluated what was needed, created the MVP, and worked through a series of sprints to create enhancements and fix some minor bugs. MVP took us about half a year, and we worked on enhancements for another few quarters. It worked great - For a while. About two years later, our development team was busy implementing some exciting new clients, building advanced features on other systems for our bread-and-butter clients, as well as making new in-house software development enhancements for other parts of the org. At this time, our reporting tool was working as expected… until it wasn’t. The data source broke, and we had to find and code a new solution. All of a sudden, an item non-existent on the roadmap became a big problem that we needed to fix asap. It created a strain for both the business and the development team. What did I learn about DIY? When it's proprietary, you own it. It may seem easy to build something, but companies often fail to plan for ongoing maintenance and prepare to fix major issues. It's too easy to neglect a tool that seems to be working for the team. Discover how you can get the best of both worlds when you buy and build. The Rebuild from Scratch I worked for a company with an efficient CMS proprietary system that we used internally to manage our clients, including some of the most recognized brands in apparel, sports equipment, and toys. When I joined the company, this tool had been used for nearly a decade. The development team often compared the code to a giant Band-Aid ball — so many patches had been put in the code that it was now almost impossible to update something without causing a lot of problems in other places. After I had spent several years working directly with this tool, the founder of the company, who was one of the original developers, had a new concept that would revolutionize how we managed our clients’ content. We assembled a small but mighty team, with seasoned members specializing in ideation, development, project management, and end-user experience. We laid out a three-week Scrum process that would keep the project on track. What happened? The board cut funding after seven months. In this case, the board paused funding, the lead developer left, and the project died. It was really unfortunate because the permissions, data structure, and communication mechanisms between different parts of the tool were in the final stages of development. When other top developers were brought in from other parts of the company to review it, they were impressed with the quality of work and how much was there. However, the support, both to build and from the board, was gone. What did I learn about DIY? The process and concept were really cool, but ultimately, I learned that the boring stuff is often what takes the longest to build and funding can dry up or be shifted towards a new project. Like building a house, the bones are the most important part and can eat up a huge chunk of your funds — even though that’s not the part you actually see. People can also optimistically underestimate the effort it takes to get the backbone of any project stood up. The Failed Build and Switch to Buy I was brought into a company specifically to work with their proprietary marketing automation platform. It allowed me to put all clients into the same strategy but use their own unique messaging from their unique email addresses and phone numbers. It had safeguards to reduce the possibility of client branding cross-contamination. It created scalability with a measurable/adjustable strategy while still allowing for highly customized messaging. However, there was a problem, the proprietary tool was built without reporting. Also, working inside the tool was cumbersome and increased the risk of human error. To counter this, there were multiple checks involving marketing and development before anything could be updated within the tool. What happened? Change requests were worked into a queue for the development team, and they worked on some enhancements as they had time against other business needs. The problem was development was already spending a lot of time just helping with day-to-day operations in the tool, and it became harder to justify additional time commitment for the tool. Often if the tool broke, it required our top talent to figure out and fix the issue. The company ultimately decided to buy a flexible platform that provided a proven starting point and empowered us to build customizations within it. We brought in consultants, evaluated companies, and noted requirements. We needed a custom implementation, but we wanted to see if there was an option that would allow us to do the more complex projects we always dreamed of creating. We purchased and stood up enough to start the existing automations in the new environment. Then over the next year, end-users and developers worked to make customizations in the newly purchased CRM and Marketing Automation tool to meet our needs. In the end — success! We were finally able to build the strategies that we wanted, and the tool was regularly updating and becoming better. We also had a support team beyond our development team, and our capabilities exponentially grew. What did I learn about DIY? Sometimes it seems like a good idea to do it on your own, especially when you have such amazing talent internally. However, your team is ultimately interpreting end-user needs who may not have the full vision for all their needs. Also, while your development team is good, they may not have the exact experience to build that specific type of solution, and therefore the code may not be as flexible as it needs to be to accommodate future requests. So while it will work (because your dev team is amazing), you’ll quickly discover that MVP is not really MVP, and you are stuck with something that needs a lot of Band-Aids. Buying and then building upon that tool — now known as build+ — set us up with a flexible solution and high-quality support team. Why Do We Gravitate Toward In-house Development for Internal Tools? Building your own HR analytics software is really a funny concept when you actually stop and think about it. You wouldn’t have your field workers build their own cars to go to each event. You don’t have IT build computers for your company. You buy the cars and the computers. You wouldn’t ask your team to reinvent Microsoft Office either. It is unrealistic to expect your developers to create something great when comparatively non-DIY, 3rd party solutions took thousands of build hours from people who have spent decades working in their fields. Data transformation and machine learning are the same when it comes to people analytics solutions. Compared to a DIY solution, One Model accelerates time to value in an organization and becomes usable in just a few weeks. Plus, One Model acts as a strategic partner with a skilled team of data engineers and experienced customer success practitioners who share the people analytics journey with you. Learn more here. So why is building so hard? What challenges will your developers face? 1. Your HCM may handle data differently than you expect and, therefore, you’ll have to do work to put that data into an analytics-ready table format. For example, Workday combines your data with business logic. Therefore, most of its data is in the form of snapshots over time. To answer any question related to time, or filtered by a period, you need to pull every possible snapshot and stitch them together into a proverbial “flipbook”. 2. In order to connect old data sets or pair them with complementary systems, like surveys or learning management tools, work will be required to merge the data with appropriate keys to ensure dates align for proper analysis. 3. You did your best creating the requirements, but your HR team is not a tool designer. It is more likely that factors will not be considered and key requirements missed. This is the number one reason your IT team will never be finished building this solution. After something has been built, a seemingly simple request, like a breakout or grouping, can require significant rebuilds. You’ll be saying, "Technically it works as designed, but every new question requires a rebuild and takes so much time. Our HR analysts can't even do a voluntary turnover graph." Your IT team wants a solution that offers the best of both worlds, so you can buy the right starting point and then easily customize within it. Your team wants you to look at One Model. Connect with us today. One Model offers a best of both worlds approach and lets you bypass all the headaches and start making people decisions based on your data. With pre-built storyboards and step-by-step predictive analysis tools built-in, you own the transformed data and your development team can use One Model as an HR data consolidator for all your HR tools. They can own the transformation logic while your team works on answering the questions currently burning a hole in your soul. Plus, One Model is flexible — so your teams can build and customize within the platform to fit your organization’s unique needs. Read our whitepaper to learn more about this best of both worlds approach.
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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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9 min read
Dennis Behrman
Human resources (HR) management has become more critical in today's rapidly evolving business landscape. HR departments face the challenge of attracting, retaining, and nurturing talent while ensuring the organization's success. To address these demands, HR management platforms have emerged as invaluable tools. However, implementing AI-powered people analytics solutions has transformed HR platforms, empowering organizations to make data-driven decisions and optimize their practices for improved efficiency and effectiveness. With AI-powered people analytics platforms, organizations can leverage insights and trends to enhance their HR strategies, leading to better talent decisions and organizational outcomes. AI-Powered HR Management Platforms AI is changing the landscape of HR management by augmenting and automating various tasks. Based on Society for Human Resource Management research, AI adoption for HR tasks was particularly widespread among larger companies, with 42% of firms employing at least 5,000 workers utilizing AI in 2022. While having specialized data analysts is still crucial for effectively utilizing AI, user-friendly tools increasingly empower employees across all roles to perform data analysis. HR analytics tools examples leverage advanced algorithms and machine learning to analyze vast data and make intelligent recommendations. Some key use cases of AI in HR processes include: Recruitment and Candidate Screening HR professionals prioritize streamlining the recruitment process, and AI technology is crucial in achieving this goal. By automating job advertising, AI helps save time and optimize campaigns for better results. AI algorithms measure outcomes, predict future trends, and reduce costs. Furthermore, AI addresses unconscious bias by reaching a diverse candidate pool and engaging passive candidates. It automates routine tasks, provides feedback, and ensures transparent communication, enhancing the candidate experience. AI in job advertising improves efficiency, diversity, and the recruitment experience for potential employees. Employee Onboarding and Training AI platforms revolutionize employee onboarding and training by automating administrative tasks, offering personalized onboarding plans, and providing interactive learning experiences. They streamline paperwork, documentation, and scheduling, ensuring a smooth organizational transition. AI platforms offer diverse online training resources and use machine learning to analyze performance and suggest personalized skill development. Moreover, they facilitate knowledge sharing and collaboration through natural language processing and chatbots. These platforms enhance efficiency, effectiveness, and employee experience during onboarding and training processes by leveraging AI technologies. Performance Management and Feedback One of the key benefits of AI platforms in performance management is their ability to capture and analyze vast amounts of data. By leveraging machine learning, they identify patterns and correlations, providing managers a comprehensive understanding of individual and team performance. These platforms automate performance evaluations, offer real-time feedback, and track key performance indicators, facilitating ongoing feedback and coaching conversations. They also provide personalized recommendations for improvement, suggesting relevant training programs and resources based on individual strengths and career goals. With AI platforms, organizations can optimize performance management processes, empower employees to drive their development and foster a culture of continuous improvement. Predictive Analytics for Workforce Planning Through the analysis of historical data, HR analytics software can identify patterns and trends in workforce behavior, such as employee turnover rates, skill gaps, and recruitment success. This enables organizations to make accurate predictions about future workforce demands and make proactive decisions to address potential challenges. AI platforms also consider external factors such as market trends, economic indicators, and industry forecasts to provide a holistic view of the workforce landscape. By incorporating this external data into predictive models, organizations can anticipate talent supply and demand changes and align their workforce planning strategies accordingly. AI-Powered People Analytics Solution in HR Management In the swiftly evolving business landscape, staying ahead requires more than mere intuition; it demands insights derived from data. AI-Powered People Analytics Platform is a transformative tool poised to redefine how organizations understand and nurture their most valuable asset: their people. Seamlessly adopting advanced AI capabilities with comprehensive workforce data, this platform unlocks a deeper understanding of employee dynamics. According to Straits Research, as of 2022, the worldwide people analytics market was estimated at $2.58 billion, and it is projected to reach $7.67 billion by 2031, exhibiting a CAGR of 12.88% during the forecast period of 2023-2031. From predictive analytics that shapes strategic decisions to personalized development paths that amplify individual growth, embark on a journey where data-driven precision meets human-centric leadership. Discover how this platform redefines success by empowering companies to cultivate thriving, resilient, and engaged teams. Key aspects of people analytics in HR management include: Employee Engagement and Retention Through analyzing various data sources such as employee surveys, performance data, and employee feedback, people analytics can identify patterns and trends related to engagement. It aids organizations in recognizing gaps and issues related to engagement and retention, measuring progress, and establishing objectives to enhance employee engagement and retention strategies. Through data analysis concerning turnover rates and mobility efforts, organizations can pinpoint trends that affect engagement and retention, uncover any underlying biases, and develop precise approaches for improvement. Diversity and Inclusion Initiatives People analytics empowers organizations to improve corporate culture and drive diversity and inclusion initiatives by leveraging data and insights. Organizations can identify gaps and set goals by analyzing employee demographics, representation, and inclusion metrics. People analytics helps uncover biases in talent processes and enables organizations to develop strategies for fair and equitable practices. Additionally, it measures the impact of diversity and inclusion initiatives on employee experiences and outcomes, allowing organizations to make data-driven adjustments. Ultimately, people analytics provides valuable insights to foster inclusive workplaces where all employees feel valued and empowered to contribute their unique perspectives. Succession Planning and Talent Management People analytics is vital in talent management and strategic workforce planning within organizations. By analyzing employee performance, skills, and potential, people analytics provides valuable insights for identifying and nurturing high-potential employees for future leadership roles. It helps organizations create talent pipelines by identifying skill gaps and developing targeted development programs. People analytics also aids in succession planning by enabling data-driven assessments of potential successors, allowing organizations to make informed decisions for key positions. With the help of people analytics, organizations can effectively manage and develop their talent, ensuring a smooth transition of leadership and fostering a culture of continuous growth and development. AI-Driven Insights for Informed Decision-Making Utilizing AI algorithms, which can dissect intricate data sets, yields valuable insights that can be acted upon. HR professionals stand to benefit significantly, as these insights empower them to execute well-informed judgments regarding recruitment, performance assessment, and the cultivation of talent. Implementing AI-driven analytics enables a strategic approach to HR, fostering enhanced decision-making across hiring, performance management, and talent development. Predictive Analytics for Identifying HR Trends and Patterns According to McKinsey, 70% of corporate leaders regard people analytics as a top priority. Organizations are placing a strong emphasis on understanding the skills and capabilities of their workforce. This proactive approach empowers them to preemptively tackle hurdles, fine-tune workflows, and execute impactful HR strategies. By harnessing AI-driven insights, HR leaders gain the ability to discern underlying dynamics, ensuring that their initiatives are both finely tuned and aligned with evolving organizational needs. Enhanced Employee Experience Through Personalized Recommendations AI-powered people analytics platforms can provide personalized recommendations to employees, such as learning and development opportunities, career pathways, and wellness programs. This improves employee engagement and satisfaction. Additionally, AI-powered HR platforms integrated with enterprise learning management systems can go beyond traditional training and development initiatives. Enterprise learning management systems can recommend wellness programs and resources that promote employee well-being, including mental health support, fitness activities, and stress management techniques. By addressing the holistic needs of employees, an enterprise learning management system contributes to a healthier and more productive workforce, fostering a positive work environment. Harnessing the Power of AI-Powered Analytics Platforms for Organizational Success The AI-powered people analytics software is revolutionizing HR management platforms. By harnessing the power of artificial intelligence and data-driven insights, HR professionals can make more informed decisions, improve employee engagement, and enhance overall organizational performance. These advanced platforms enable the automation of repetitive tasks, enabling HR teams to focus on strategic initiatives and personalized employee experiences. Moreover, AI-driven predictive analytics tools for HR can provide valuable insights into workforce trends, enabling proactive talent management and effective succession planning. As organizations embark on this transformative journey, the collaboration between technology and human expertise will shape the future of HR, driving innovation, productivity, and success in the workplace. Learn more about One Model's people analytics software.
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4 min read
Dennis Behrman
Phil Schrader and Stephen Haigh had an opportunity to attend the People Analytics World Conference in London April 26-27, 2023. During their visit, Phil was asked to give a public demonstration of how HR analytics software works. While we can't speak for other people analytics tools, we can speak to One Model. The crowd was mesmerized and had lots of questions at the end that you definitely have to watch. Join Phil as he walks through data import, export, and all the magic in between — even showing in real time how an AI model is built exclusively on your data. Phil, always cheeky and fun to watch, is a great teacher in all the things you should look for when assessing which people analytics tool is right for you. Compared to other HR analytics tools on the market, you'll quickly see that One Model is more transparent, easier to use, and more open than any other option on the market. Want your own personal tour of One Model? Request time to meet today. During the video, Phil walks us through each of these layers: The Consumer Layer: At the top of the platform, users, such as HR Business Partners, can access data, insights, and storyboards through a user-friendly interface. The storyboard feature allows users to interpret data visually and navigate through various tools like Explore, Storyboards, and Data. These tools enable users to slice and dice analytics, explore heat mapping, and gain insights into different data sources. From Consumer to Analyst Layer: One Model's flexibility empowers users to transition from the consumer layer to the analyst layer effortlessly. Here, analysts can customize the views, rearrange elements, and dive deeper into the data. With simple clicks, they can transform data into charts, change metrics, and connect multiple systems to gain a holistic view. Configuring Metrics and Data Engineering: As analysts continue their exploration, they can configure metrics according to their organization's specific requirements. They can modify calculations, adjust inclusion/exclusion criteria, and create unique views tailored to their audience. Furthermore, One Model offers transparency into data engineering, allowing analysts to delve into the underlying data models, processing scripts, and data sources. Unleashing the Power of Data Science: Finally, One Model empowers advanced analysts and data scientists to build predictive models. With the augmentation feature, analysts can create and maintain multiple models, evaluate their performance, and put them on schedules. The platform provides a guided walkthrough for model building, enabling users to define their objectives, select relevant metrics, and generate predictions. The prediction capabilities extend to specific employee segments or the entire population.
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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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Featured
10 min read
Richard Rosenow
People analytics is essential in today's complex business world as it helps organizations make data-driven decisions to maximize the value of the workforce. There are, however, still barriers to adoption, from legal to ethical and from finance to IT. To ensure that people analytics is more accessible to all audiences, HR leaders need to have a nuanced understanding of the audience they’re speaking with, and the needs and interests of those teams. They also need to know this analytics space inside and out in order to stand their ground on the value that it can provide both employees and companies. I had the pleasure of speaking with HRD about this topic and more on a recent podcast. We’ve summarized some of those conversations below, but please take a listen too and let me know what you think. Prefer to Listen to the Conversation? Check out HRD's podcast Q: Why is it important that we reduce the barriers to implementing people analytics systems? A: It is crucial to reduce barriers to people analytics because, at the end of the day, people analytics is about decision support. Organizations need to make data-driven decisions about their workforce quickly and efficiently in today's complex business world. Workforce costs are most of the costs of doing work in many industries, making it imperative to understand how to maximize the value of the workforce. People analytics provides a way to do that. Beyond the cost of doing business, the workforce also is made up of people with families, friends, and rich full lives. It's essential to recognize that they are not just resources to be allocated but valuable contributors to the organization. To care for the workforce, we should be using every tool at our disposal to make better decisions related to the workforce. People analytics helps us do that. This is why you need to develop a plan on how to implement HR analytics before the process begins. Whether it's Workforce Planning (WFP), Diversity, Equity, and Inclusion (DEI), engagement, or breaking down silos, looking at the workforce through the lens of data shines a bright light on the organization. People analytics provides the data and insights to help managers make better decisions, improve employee experiences, and drive business results. Therefore, reducing the barriers to people analytics is critical to unlocking its potential and gaining a competitive edge in today's business world. Q: Why are HR leaders still afraid of people analytics? A: For those holdouts, I would first say give people analytics a second look now that the field has matured. I would also add that I recognize that there is a good reason for some HR leaders to be hesitant. HR professionals have seen many fads come and go over the years, such as competency models, 9 boxes, and stack ranking. HR is complex, so it's difficult to determine what new and shiny interest is real and what's fluff, and what will stick around. Humans might be the most complex thing we manage at work, and for our history at work to date, other humans have been the best way to interpret and manage humans. But this is shifting. Computers are just starting to break through and provide more nuanced and targeted support in that endeavour. People analytics is starting to become expected as a way to augment HR teams' decision-making, and more and more teams are delivering from this function daily. There is also a framing that I’d push back on that people analytics is the "future of HR,". I know this rubs some HR leaders the wrong way too. I would shift this to say that people analytics is a large part of the future of HR, but that people analytics will become HR or will become an automated tool that we use before it replaces the function. By that, I mean that the best parts of HR, the parts that we are most proud of, are still and will still be those human moments, and we do HR for very human reasons. HR analytics systems when run properly inform us around how to be more human and enable us to spend more time doing those things that are important to us as humanity over time. In the long run, those human things will remain and grow even after we implement people analytics systems. I don’t believe that HR leaders are necessarily afraid of people analytics. It's more of an adoption curve that all organizations are going through. People analytics is a valuable tool that can help organizations make data-driven decisions and unlock the full potential of their workforce. As the adoption of people analytics continues to increase and as people analytics teams learn how to integrate it into the function, more HR leaders will recognize its benefits and embrace it as a crucial part of their work. Q: How can we make people analytics more accessible? A: One way I’ve found is that we can make people analytics more accessible by encouraging HR leaders to think of data insights as another form of employee listening. As HR teams have been cut and their ratios have changed, with sometimes a ratio of 1 to 800, it's challenging to speak regularly with 800 people per year. Listening to the workforce through data allows you to listen at scale. I say this too because HR professionals are already good at listening, and framing people analytics as listening is a way to tap into something that HR is already good at. Listening through data can provide insights into employee behavior, preferences, and needs, which can inform HR practices and improve employee experiences. I’ll add too that data cannot do it alone. People are still needed to able to interpret and tell the stories behind the data in order to gain insights and make informed decisions. Therefore, it's essential to provide training and resources to HR leaders to help them understand how to use people analytics effectively. They need to understand how to collect and analyze data, interpret the insights, and use them to inform HR practices. Making people analytics more accessible and encouraging HR leaders to embrace data insights as another form of employee listening can help organizations unlock the full potential of their workforce and improve employee experiences. Q: Where are areas of concern around privacy and ethics and how can HR leaders reassure their employees? A: Areas of concern around privacy and ethics can absolutely arise when implementing people analytics. HR leaders must address these concerns well before starting down the path of using data to inform decisions and to reassure their employees that their privacy and ethical standards will be upheld. Privacy considerations should be in place from the beginning of the people analytics process. It should not be an afterthought, but rather a core component of the team's development, tool rollout, and system setup. Hiring a trained and tenured people analytics leader, someone with experience in the HR subject matter area, or pairing them with someone who has deep HR expertise is an important investment to help an HR team navigate privacy and ethical concerns and to provide guidance to the team. Although IT can be a great partner, they may not have the necessary expertise in privacy and ethical considerations specific to working with workforce data. Knowledgeable folk speaking to the nuance of ethics and privacy around workforce data should lead people analytics, and it's crucial to not have that data cross boundaries. For example, there could be "electric fence" items such as the content of employee emails or sharing DEI data at the row level that would violate privacy, ethical, and legal standards if folk outside of HR had access. When communicating about people analytics, HR leaders should also focus on sharing the positive impact it can have on employees. By using data to understand employee behavior, preferences, and needs, HR leaders can make informed decisions that improve employee experiences. Reassuring employees that their data is used for good can help them buy into data sharing. HR leaders must help employees feel valued and safe when sharing data. In summary, HR leaders must be transparent and upfront about privacy and ethical considerations when implementing people analytics. By emphasizing the positive impact of using data for good and ensuring that privacy and ethical standards are upheld, employees can feel more comfortable sharing their data and trust that their employer has their best interests in mind. Q: How can HR work with finance or IT to help navigate concerns around the cost or need for people analytics? A: I don't think anything gets done in business today without the buy-in of Finance and IT. We live in a unique economic environment and everything we touch is technology. Anyone that thinks they can go alone without IT or Finance leaders is going to be in for a world of issues. That said, many times IT or Finance leaders may not know this space or understand the lens of HR. They have their own concerns and lenses that they bring to business, so it is up to us as HR professionals to communicate and share the value of what we do both on our terms and on their terms to ensure that teams are aligned. It is also important to recognize that Finance and IT leaders are also employees and people leaders who have their own questions and concerns that need to be answered about how workforce data is used. By selling the overall vision of People Analytics and demonstrating how it affects everyone within the company, including their data, their employees, and their decisions as leaders, HR can gain buy-in and support for the initiative from Finance and IT as leaders as well as functional partners. Anyone heading down the path of starting a people analytics function requires collaboration and alignment between HR, Finance, and IT (and many other teams!) to ensure that the initiative is strategic and aligned with the overall goals of the company. Ultimately, gaining support for people analytics early from these partners leads to better decision-making and outcomes for the company. Want to learn more about One Model? Reach out!
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5 min read
Phil Schrader
The Power of Combining Data Sources Am I weird for having a favorite metric that I always pull once I connect a customer's HRIS and Recruiting data for the first time? Oh well. Let's talk about my favorite merged source metric: First-year attrition by recruiter! I think it's one that can be useful for managing a recruiting function, but it's also a helpful classroom example to explain why we all need to merge recruiting and HR data. Connecting data across HR systems can be a tricky problem, but with the right tools, it is possible to gain valuable insights into employee behavior and business outcomes. In the video above, we explored how One Model can be used to blend data from different HR systems and gain insights into key metrics such as new hire turnover rate. Join the Conversation on Linkedin Segmentation is Key to Understanding Why Additional Data Sources Matter One of the key features of One Model is the ability to quickly break down data into more meaningful groups. Peter Howes will back me up in saying HR data without segmentation is worse than useless. To expand on my video example, by grouping turnover rate by year, we can get a better understanding of the overall trend in general employee retention. Additionally, by narrowing our employee outcomes analysis to specific subsets of employees, such as those who joined the company within the last year (or gender, department, etc), we can gain insights into specific areas of concern, such as early termination rates. But we can get these insights with data from 1 system. What happens when we combine data from say, our recruiting platform? You have the Power When You Join HR Data Sources Another powerful feature of One Model is the ability to connect data from multiple systems, such as recruiting data from your ATS and core workforce data from your HRIS (to use my video example). You can now make discoveries that actually improve processes within your organization. By connecting who has turned over with who actually recruited that person, we can make leadership decisions and work with L&D on potential coaching opportunities. Finding the “Why” After You Merge Recruiting and HCM Data Many people analytics teams (whether through intensive spreadsheet work or quickly using a tool like One Model) can create these insights, but interpreting these insights still requires the nuance and care of an HR analytics leader. Many struggle with providing the “why” behind the data. If you ask a seasoned recruiter they most likely will say that the number one reason is probably related to the applicant feeling mislead in the hiring process and that could increase new hire turnover. But are there other factors at play? Start with an exploratory data analysis and then get sophisticated with an AI engine that non-data scientist can actually use. Overall, the Explore tool in One Model makes it easy to connect data across HR systems and gain valuable insights into employee behavior and success rates. Whether you are an HR professional or a business leader, this tool can help you make data-driven decisions and improve your organization's performance. Want to See Phil Merge More Data? Schedule a Demo Today
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Featured
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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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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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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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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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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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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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
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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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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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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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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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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13 min read
Chris Butler
The vast majority of organizations and People Analytics vendors use snapshots for extracting data from Workday because this is really the only choice they've been given to access the data. But snapshots are problematic for several reasons: They are inaccurate - You will typically miss out on the changes occurring between snapshots. This makes it impossible to track data/attribute changes in between, to pro-rate, and create analysis any deeper than the snapshot's time context. They are inflexible - An object or time context has already been applied to the data, which you can't change without replacing the entire data set with a new context. They don't allow for changes - If data is corrected or changed in history, you need to replace the entire data set, urggh. External data is difficult to connect - Without effective dating, joining in any external data means you have to assume a connection point and apply that time context's values to the external data set. This compounds the inaccuracy problem if you end up having to snapshot the external data as well. A pain in the #$% - To pull snapshots from Workday now, you need to create the report for each snapshot period that you need to provide. Three years of data with a month-end snapshot means 36 reports to build and maintain. With our background in working with raw data directly from HR systems, this approach wasn't going to cut it—it couldn't deliver the accuracy that should be the basis of an HR data strategy. The answer isn’t just buying another big data tool, because this same challenges will still exist. Instead, you need to enhance your existing structure and fundamentally rebuild your data architecture to truly solve these issues. We do just that, we extract all employee and object data, analyze the data as it flows and generate additional requests to the Workday API that work through the history of each object. Data is materialized into a schema close to the original but has additional effective-dated transactional records that you just wouldn't see in a snapshot-based schema. This becomes our raw data input into One Model, delivered to your own warehouses to be used any way you wish. The resulting dataset is perfect for delivering accurate, flexible reporting and analytics. The final structure is actually closer to what you would see with a traditional relational schema used by the HRIS sold by SAP, Oracle, PeopleSoft etc. Say what you will about the interfaces of these systems, but for the most part, the way they manage data is better suited for reporting and analytics. Now don't get me wrong, this should be a low priority decision point for Workday when selecting an HRIS. Don't compromise the value of a good transactional fit of an HRIS for your business in an attempt to solve for the reporting and analytics capability because ultimately you will be disappointed. Choose the HRIS system that fits how your business operates, solving for the reporting and analytics needs in another solution, as needed. Time to get a little more technical. What I'm going to discuss below is the original availability format of data in comparison to the approach we take at One Model. Object-Oriented - The Why of the Snapshot Okay, so we all know that Workday employs an Object-Oriented approach to storing data, which is impressively effective for its transactional use case. It's also quite good at being able to store the historical states of the object. You can see what I mean by taking a look at the API references below: The above means the history itself is there, but the native format for access is a snapshot at a specific point in time. We need to find a way of accessing this history and making the data useful for more advanced reporting and analytics. Time Context In providing a point in time, we are applying a time context to the data at the point of extraction. This context is then static and will never change unless you replace the data set with a different time context. Snapshot extractions are simply a collection of records with a time context applied. Often when extracting for analytics, companies will take a snapshot at the end of each month for each person or object. We get a result set similar to the below: The above is a simple approach but will miss out on the changes that occur between snapshot, because they're effectively hidden and ignored. When connecting external data sets that are properly effective- dated, you will need to make a decision on which snapshot is accurate to report against, but you simply don't have enough information available to make this connection correct. This snapshot is an inaccurate representation of what is really occurring in the data set, and it's terrible for pro-rating calculations to departments or cost centers and even something as basic as an average headcount is severely limited. Close enough is not good enough. If you are not starting out with a basis of accuracy, then everything you do downstream has the potential to be compromised. Remove the Context of Time There's a better way to represent data for reporting and analytics. Connect transactional events into a timeline Extract the details associated with the events Collapse the record set to provide an effective-dated set of records. The above distills down the number of records to only that which is needed and matches transactional and other object changes which means you can join to the data set at the correct point in time rather than approximating. Time Becomes a Flexible Concept This change requires that you apply a time context at query time, providing infinite flexibility for aligning data with different time constructs like the below Calendar Fiscal Pay periods Weeks Any time construct you can think of It's a simple enough join to create the linkage left outer join timeperiods tp on tp.date between employee.effective_date and employee.end_date We are joining at the day level here, which gives us the most flexibility and accuracy but will absolutely explode the number of records used in calculations into the millions and potentially billions of intersections. For us at One Model, accuracy is a worthwhile trade-off and the volume of data can be dealt with using clever query construction and, of course, some heavy compute power. We recently moved to a Graphics Processing Unit (GPU)-powered database because, really, why would you have dozens of compute cores when you can have thousands? (And, as a side note, it also allows us to run R and Python directly in the warehouse #realtimedatascience). More on this in a future post but for a quick comparison, take a look at the Mythbusters demonstration What About Other Objects? We also apply the same approach to the related objects within Workday so that we're building a historical effective-dated representation over time. Not all objects support this, so there are some alternative methods for building history. Retroactive Changes? Data changes and corrections occur all the time, as we regularly see volumes of changes being most active in the last six months and can occur several years in the past. Snapshots often ignore these changes unless you replace the complete data set for each load. The smarter way is to identify changes and replace only the data that is affected (i.e., replace all historical data for a person who has had a retroactive change). This approach facilitates a changes-only feed and can get you close to a near-real-time data set. I say "close to near-real time" because the Workday API is quite slow, so speed will differ depending on the number of changes occurring. Okay, So How Do You Accomplish This Magic? We have built our own integration software specifically for Workday that accomplishes all of the above. It follows this sequence: Extracts all object data and for each of them it... Evaluates the data flow and identifies where additional requests are needed to extract historical data at a different time context, then... Merges these records, collapses them, and effective-dates each record. We now have an effective-dated historical extract of each object sourced from the Workday API. This is considered the raw input source into One Model, and it is highly normalized and enormous in its scope, as most customers have 300+ tables extracted. The pattern in the below image is a representation of each object coming through; you can individually select the object slice itself The One Model modeling and calculation engines take over to make sense of the highly normalized schema, connect in any other data sources available, and deliver a cohesive data warehouse built specifically for HR data. 6. Data is available in our toolsets or you have the option to plug in your own software like Tableau, PowerBI, Qlik, SAS, etc. 7. One Model is up and running in a few days. To accomplish all of the above, all we need is a set of authorized API credentials with access provided to the objects you'd like us to access. 8. With the data model constructed, the storyboards, dashboards, and querying capabilities are immediately available. Examples: Flexibility - The Biggest Advantage You Now Have We now have virtually all data extracted from Workday in a historically accurate transaction-based format that is perfect for integrating additional data sources or generating an output with any desired time context (even convert back to snapshots, if required). Successful reporting and analytics with Workday starts with having a data strategy for overcoming the inherent limitations of the native architecture that is just not built for this purpose. We're HR data and People Analytics experts and we do this all day long. If you would like to take a look, please feel free to contact us or book some time to talk directly below. Learn more about One Model's Workday Integration Book a Demo
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6 min read
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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