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Featured
6 min read
Jul 09, 2025
When most people hear “waterfall chart,” they think of finance teams explaining profit margins or budget deltas. But waterfall charts have a lot more to offer, especially to HR teams trying to tell the story behind their numbers. HR leaders are asked every day to explain workforce shifts: Why did headcount grow? Where did attrition hit hardest? How are internal moves like promotions and transfers shaping the workforce? That’s where waterfall charts come in. Let’s explore how these charts work, why they’re so valuable for HR analytics, and how you can use them to tell compelling workforce stories. What's a Waterfall Chart, Really? A waterfall chart breaks a total value into its component parts across time, showing what changed along the way. You see where you started, which factors drove increases or decreases, and how you ended up where you are. This structure is ideal for HR data, where metrics are constantly in motion. Headcount rises and falls, employees move between departments, people leave and new hires join. The beauty of a waterfall chart is that it doesn’t just show what changed – it shows how and why it changed. Why HR Needs Visuals That Explain “Why” Most traditional HR reports show a beginning and end value. But the real story often lies in what happened in between. A simple bar chart might say: “We had 1,000 employees last quarter and now we have 1,030.” But it doesn’t show how you got there. That’s a missed opportunity. Because without understanding the drivers—hiring, resignations, promotions, retirements—you can’t manage them. Waterfall charts fill that gap. They offer a sequential breakdown that HR leaders, executives, and business partners can all grasp at a glance. Let’s explore some real-world examples of how you can use waterfall charts in HR. Use Case: Explaining Headcount Changes Scenario: The CEO asks for a quick update on workforce growth. Instead of handing over a spreadsheet, show a waterfall chart that breaks down the change: Starting headcount: 1,000 +299 new hires 114 voluntary terminations 155 involuntary terminations Ending headcount: 1,030 Now leadership can see more than just the incremental headcount increase. They understand what’s behind the growth—whether hiring is meeting demand, and whether attrition is a concern. The chart shifts the conversation from “What happened?” to “What do we need to act on?” This type of breakdown is especially valuable during budget reviews, reorganizations, or merger planning, where workforce movement needs to be clearly explained and justified. But headcount changes aren’t just a company-wide story, they’re built on countless individual shifts happening across departments. Promotions, transfers, and team realignments often play a huge role. A waterfall chart makes those internal flows visible too, helping you show not just growth, but how that growth is being shaped from within. In the chart above, you can see this department isn’t just growing, it’s optimizing talent placement from the inside through promotions and transfers. Or take it one step further with a year-over-year waterfall. Seeing workforce changes across multiple years—periods of growth, attrition spikes, hiring drives—helps contextualize trends and spot inflection points. In the chart above, it's easy to observe how consistent hiring outpaced both voluntary and involuntary exits, resulting in sustained headcount growth over time. For One Model customers: You can also use the Waterfall Chart to visualize your Headcount Reconciliation Report. This helps surface discrepancies caused by unrecognized termination reasons, acquisitions, or other data quality issues. Addressing these early ensures your metrics and dimensions stay clean, accurate, and actionable. One Model’s Waterfall Chart: Designed for HR Many BI tools offer waterfall charts, but they’re usually tailored for financial analysts. One Model’s new waterfall visualization is designed specifically for HR workflows and people data, with the flexibility and depth HR teams actually need. This means you can: Track key movements – like growth, exits, and internal mobility over time Segment by dimension – such as department, location, or employee type Drill into the details – click to explore root causes or dig deeper Present with clarity – with labeled bars, color-coded trends, and clean visuals Because it’s native to the One Model platform, the waterfall chart connects to your people data model, which ensures consistent definitions and always-fresh data. It also aligns with the other analytics visuals your team already uses, making it easy to add to dashboards or presentations. Making Data Talk Workforce dynamics are complex. People leave, get promoted, transfer, take leave, return, or burn out. Metrics are constantly in motion. Waterfall charts bring order to that movement. They unpack complexity without overwhelming your audience and, when paired with strong data governance, they become one of HR’s most effective storytelling tools. Explore more on storytelling with data. Want to learn more? Request a demo today.
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Featured
21 min read
Jun 30, 2025
Welcome to Richard Rosenow's keynote presentation at People Analytics World 2025. In this talk, Richard draws parallels between art and HR, emphasizing the need for a blend of creativity and analytical rigor. As AI technology advances, he advocates for elevating human roles within HR, moving away from rigid systems to embrace flexibility and strategic thinking. He underscores the necessity for HR leaders to be fluent in business operations and to engage in meaningful dialogues rather than relying solely on data. Listen in or read our lightly edited transcript: Kicking Off the Future of People Analytics What a great way to start the day. I am delighted to be back. I love the People Analytics World series because it brings together a group of people passionate about this field. We get to have phenomenal, nerdy conversations all day, today and tomorrow. I can't wait. Today I'm going to put forward something a little bit different. People Analytics World helps us stay on top of what’s happening in the space—trends, changes. One of the best talks I heard last year wasn't on data science or AI—it was on power. A phenomenal talk by Amit Mohindra, worth looking up if you missed it. It changed how we think about People Analytics and what’s shifting in this space. Postmodern Inspiration from an Artist Brother Let’s start with this piece: a laser-cut relief collage, which includes screenshots from a Reddit discussion about Israel and Palestine. It explores the gap between what happens in life and the emotional flattening of the news. The closer you get, the bear disappears, and you begin to see the words. It's an interplay of perspective. It’s by Tom Rosenow—my younger brother. He’s graduating from his MFA program next week, which I’m incredibly proud of. In conversations with Tom, I've learned this piece is a good example of postmodernism. The interplay with interpretation, understanding, and layered meaning. We talked about what postmodernism is—and I found there are lessons we can bring back to People Analytics and HR. The Fusion of Art, Science, and Analytics But first, let’s talk a little bit more about my brother and myself. We were raised by two engineers—civil and industrial—who ran a small machine shop. We actually had computer-controlled machines in our garage. That might explain a bit about who I am, if you know me. When I went to my undergrad, I studied sociology. (Where psychology is person-based, sociology studies the systems of a culture; understanding the system in order to understand the people). When Tom went to undergrad, he studied fine arts. He was an oil painter who did some tremendous work. So our engineering parents were very understanding when their sons pursued traditionally soft sciences. But throughout our careers, we both returned to our original passions, but in very different ways than we started. For example, Tom does some really interesting work here. This is actually a copper plate, about 3 ft by 3 ft. These things are massive. He has learned more about sanding and metals than I ever would have expected from an MFA program. Here's another one of his pieces. This is an aquatint in cyanide. Cyanide? No. Yes. Cyanotype. Uh-oh. Art history majors. Keep me in check here. It's a fantastic piece. It's another massive collage, but it is an incredibly mechanical process. It almost takes alchemy to create this. He etches it in, dips it in acid, then applies the ink. It's a massive process. This is not art as I knew it. I knew art as painting, and I'm not an artist by any means. He got all of the talent there. But what I love is the blend between this art and this science. I have a similar story. I was one of the few people deliberately trying to break into the People Analytics space. I’m passionate about it and excited about where it’s going. But I found art and science coming together there, too. And a lot of you in the room have found that too. Into this traditionally soft domain of HR, we brought this harder edge. We brought the math. We brought the science. We brought these things together in a very unique way, like with this people overview by Tony Ashton, our Chief Product Officer. It's available at our demo booth. So from talking to my brother about this and seeing where art has gone and where his art exists within the space of postmodernism, I'm gonna walk us through a couple of the eras of art and some things we can learn along the way when it comes back to HR and People Analytics. Art through the Eras: From Romanticism to Postmodernism When Tom went for his MFA in fine art, I thought he was going to create these grand, beautiful expanses of understanding nature and intense emotions. I was thinking of romanticism—emotional, heroic, grounded in human nature. Like "Wanderer Above the Sea of Fog" by Casper David Friedrich, that really shows off human nature and the heroism of the space. Then came modern art—logical, orderly, rule-based. Artists asked: What makes this art? What’s the wiring here? How do we play with that and have people experience it in different ways? This particular piece, "Composition with Red, Blue, and Yellow" by Piet Mondrin - I don’t think he made it in Excel, but he could’ve—there are some parallels here. No, it’s a tremendous piece. But it was a rejection of the incredible romantic era. I think some of you are starting to see where I'm going with this. In this art evolution, there wasn’t a debate about Which one is the real art? Which one is most correct? They are art. Next came postmodern art. This particular piece is “Crushed” by Andy Warhol, a great example of postmodern art. Postmodernism says I respect that modern art had some rules. But we're gonna play with them, and we're gonna try to understand this more human piece that's coming back around. And it started to layer on top of what modern art had developed, bringing something back about human creativity and meaning. And we started to look and say, Hey, this is a can of soup. Is this art? What does this mean? What does it mean for me? And even that question, that reflection, that playful interaction is part of the art itself. Breaking the False Dichotomy But something was built on top of where "modern" was and where it's going. And I see that a lot. We hear a lot about art and science as being a pendulum, where things swing between them. We hear about emotion and logic. We hear about people and analytics and how we show up. Are we left brained? Are we right brained? And these things are in contrast in many of our conversations. And I believe this is a false dichotomy. What's happening in the world of work today is we need to show up with both. We can't be 80% people and 20% analytics. I can't be just a people person. I can't be an analytics person only. We have to show up as empathetic nerds. We have to be 100% people/100% analytics, and we've got to take that forward to something new. So when people ask what’s happening in People Analytics, what are the changes we're seeing?, what I see is how can we can break through to that next thing?. It's not going to be a return to that romantic era of HR. And we're not going to be able to stay in our kind of modern space forever. There's going to be something next, and we're starting to see hints of that start to emerge on the scene. So I'll lay it out very clearly. HR through the Eras: From Romanticism to Postmodernism Artisan HR (Romantic Era) We began in the artisan HR era—the romantic period. I use the term artisan intentionally, because people think of this time period as qualitative or even anecdotal. Not quite: Qualitative is actually its own scientific discipline and anecdotal HR is kind of mean. The truth is when I think about the best HR people I've worked with, as they've grown their careers, it really parallels what a lot of artists have done. They lived in the flow of work, and built their careers through the experiences, the apprenticeships, the journey. They really understand the business, and they contribute something powerful through that innate human understanding of the business itself. As with artists too, there’s a pretty wide range. The incredible ones, and then there's “everybody thinks they're an artist,” as well. This is what makes it tough to manage an HR team. It’s a very human domain. This piece, “The Fighting Temiar” by Joseph Mallard William Turner is a beautiful example from that romantic era. People Analytics (Modern Era) Let's talk about People Analytics, though. I think this is the modern era of HR. This is where we're living. This era is about what comes next…like this beautiful piece, Girl with a Mandolin by Pablo Picasso, which interacts with and starts to set up some of the rules and the structures around this shift. I think about this a lot because at some point, we're going to reach the edge of what People Analytics does. Ten years ago when we started down this space, a lot of teams were blasting the marketing that People Analytics will fix everything. Just use data. But we all know, the longer someone has been in this space, the most experienced People Analytics leaders end up talking about power. They talk about different change management. They talk about different dynamics at play, and they bring that extra piece back into the conversation that was missing. Because we can't data science our way out of HR, we have to bring something back together. Postmodern HR (Next Era) and Trends that Signal the Shift That brings us to postmodern HR. Think of "The Brooklyn Rail" by David Salle. It’s about an experience and finding meaning. And the question is how do we bring meaning back to HR and People Analytics? Originally, HR was about personnel—how to manage and pay people. Then we moved to data—measuring things like sentiment, productivity, engagement. We flattened multidimensional humans into spreadsheets. That was progress. This new phase is about outcomes. Some of you here will say "We've been knowing outcomes for years.” We’re not going to throw out data - we’re going to build on these things and keep growing. Keep what we need and maybe drop some things that aren’t serving us. The New HR Skillset Let's talk about the skills required to thrive in next gen HR. The skillsets required are evolving: First, “soft skills”—critical thinking, change management, communication. These skills were and still are in high demand during change management projects, which are often better served by HR than, say, CHROs. Then came data literacy—understanding and communicating data. These skills helped HR teams translate complex datasets into organizational insight, but now AI is learning to speak human, reshaping the role of data literacy in the process. What’s next? Business fluency. This goes beyond speaking the language of data. What really differentiates modern HR teams is how fluent they are in the way the business operates. Do you know what it is that drives your business forward? Are you able to speak to business? Are you part of the business? We need all three. From Complexity to Nuance: How HR Creates Value How we create value has changed. Artisan HR understood complexity and did its best with what it had at the time, no technology, no data. People Analytics brought order to the chaos. What’s coming next is nuance—understanding what’s uniquely true for your people and business.This is where postmodernism really shines. We’re going to this space where it's uniquely about your business, your people. Where those spiky edges of your data model are actually going to give you the difference you need to make change happen. This next era of HR will be focused on outcomes, grounded in business fluency, powered by nuance, and informed by predictive analytics: Predictive analytics won't disappear, but will shift from being the final deliverable to informing more nuanced, forward-looking conversations. Dashboards become dialogues Insights become recommendations Human judgment is elevated, not replaced At a recent CHRO panel discussion, they made it clear that they don’t want data or even “insights.” They need to know what action to take. People Analytics teams can create insights and pass them on and hope they make change happen. But this next phase is going to be around answers. And that means human judgment will be elevated, not replaced. We’ve seen marketing materials that claim their tool can replace your whole HR team. But at the end of the day, we need those HR people in those human positions, pushing towards finding meaning and using that to drive change forward. And this is how People Analytics becomes a strategic function. This is the carrot, but there’s a stick, too: AI is coming. Fast. The AI Moment: A Turning Point AI is getting pretty good at what we’ve been doing for the past ten years. This piece was generated by me and ChatGPT. This is a people analyst. I love this piece. I shared this with my brother. He said it was horrible. He hated it. So I'm gonna print out a bunch, and I'll sign them at the booth. But, truly, anyone that's played with ChatGPT and other AI tools for a minute knows what it's capable of doing. But what it's really good at is when we've structured things, when we have order, when we have a checklist - a lot of the stuff we’ve built up our credibility on and even our own careers within People Analytics. ChatGPT will come for us before the HRVP. And so we’ve got to start looking ahead. And this is why: This is an internal company demo from One Model. Our Chief Data Scientist showed me 6 months ago what we were working on. I got permission to talk about it. This isn’t a flashy, product demo. It was a raw internal tool. But when I first saw it, it gave me a “before and after” moment in my own thinking. I had worked in data foundations, partnered with engineering teams, built and scaled product. And yet, watching this demo made me realize: If I went back to practice, my job is not going to be there. So I knew this is really important for me. I’ve got to stay on top of this, keep moving forward. The demo we’re going to see is of AI ingesting Workday and Greenhouse data, standardizing it, creating an entity relationship diagram, and writing 2,000 lines of code—all in under three minutes. (I trimmed it to two minutes by removing file navigation and background setup, but the actual processing time is unchanged.) It starts with a simple prompt: “Please load these new files and combine them to a new event fact table called job event open.” The user selects files from Greenhouse and hiring manager data. The tool recognizes the content, standardizes the structure, and proposes a plan. It automatically looks at it and understands in a way that a senior data engineer could still manipulate. Then the AI begins building out a full project plan. It determines what internal tools to use and how to organize the work. It’s that idea that if you had a hundred hours to cut down a tree, you’d spend ninety sharpening the axe. The AI isn’t rushing. It’s setting up something defensible and deliberate. And, again, this is something that a senior data engineer will be monitoring, watching, confirming. Once the plan is confirmed, the tool gets to work. It creates an entity relationship diagram—yes, fully AI-generated. If you've ever tried to get a data engineer to build one manually, you know how significant this is. The diagram clearly shows what’s new, what’s modified, and how everything connects. The stuff in yellow is what's gonna change. The stuff in green are the new files. It asks, Does this look right? Can we start? After that, it writes the code. Two thousand lines of it. In seconds. This code would typically take a skilled team days or weeks to deliver. But here, it’s done and ready to audit almost immediately. What once took weeks or months can now happen in minutes. The impact of this shift is impossible to ignore. When I saw this demo, I remembered every fight I've had with IT. And I remembered how many months it would take to get a new file. How One Model Fits the Postmodern HR Vision We’ve built One Model for this moment. Our company was founded on the idea that data engineering, architecture, and ingestion are critical to making People Analytics work. We've spent 10 years solving those tough foundational problems, building robust tools and assembling a team of some of the world’s best data engineers. These tools were originally created to support our own team. But here’s what’s changed: when AI uses those tools, it becomes fast, powerful, and consistent. The results are auditable and repeatable—exactly what you need in a business-critical function. So what does that mean for HR and People Analytics teams? First, it means we can elevate our humans. If your team is still stuck managing pipelines manually, doing repetitive file loads, or serving as the human API between systems—it’s time to move them out of that invisible work. Not only is that work automatable, it’s also where AI is making the fastest inroads. Second, we believe it’s time to reject rigid systems. Too often, teams are forced to fit their unique organizational realities into someone else's template. One Model offers one flexible data model per customer. We don’t expect you to conform to our structure—we adapt to yours. We represent your business the way it actually operates. And third, we fully embrace generative AI. That means building AI-first tools, promoting an AI-first mindset, and encouraging teams to ask: could the AI do this first? Only then should we assign it to a person. This is how we support postmodern HR teams—by freeing them from invisible work, respecting the uniqueness of their business, and empowering them to thrive in an AI-driven future. Thank you.
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Featured
12 min read
Jun 26, 2025
Few metrics are as directly tied to HR’s impact on business outcomes as time to fill. That’s why it’s helpful to start by distinguishing this data point from other hiring measures: -Time to fill measures the entire duration from when a job requisition is opened to when an offer is accepted. -Average days to fill a position refers to the typical length of time required to fill roles across a given period, often broken down by role type or department. -Time to hire tracks how quickly candidates move through the recruitment pipeline after they enter it. Each of these metrics offers valuable insights, but time to fill provides the broadest view of recruiting efficiency and helps inform strategic workforce planning. Table of Contents Why Time to Fill Matters How to Calculate Time to Fill Key Factors That Affect Time to Fill Time to Fill Benchmarks and Trends How to Improve Time to Fill Without Sacrificing Quality Tools and Technology That Can Help Why Time to Fill Matters Time to fill is more than a recruiting KPI. It affects core business performance. Every day a role remains open can lead to lost productivity, missed revenue opportunities, and increased workloads for existing team members. It also drives up hiring costs, from extended job postings to the time spent by hiring managers and recruiters. By tracking this metric closely, organizations can spot inefficiencies, forecast hiring needs more accurately, and align recruitment with broader business goals. On the other side, a well-managed time to fill improves candidate experience and helps secure top talent faster. When organizations move quickly, they reduce the risk of losing high-quality candidates to competitors. It signals a well-run, decisive culture, which is something top candidates notice. Streamlining the process also minimizes friction in the hiring journey, leading to higher offer acceptance rates.. How to Calculate Time to Fill At its simplest, calculating time to fill is about measuring the number of calendar days between two points in the hiring process: when a job requisition is approved (or posted) and when a candidate accepts the offer. The formula looks like this: Time to Fill = Date Candidate Accepts Offer − Date Job Requisition was Opened Some organizations use the job posting date as the start point, while others use the date of requisition approval. The key is to be consistent in how you apply the calculation across your organization. To track this metric over time, many companies calculate average days to fill a position by summing the time to fill for all closed requisitions over a given period (such as a quarter or a year), then dividing by the number of roles filled: Average Days to Fill = Total Time to Fill for All Roles ÷ Number of Roles Filled This gives a useful baseline for benchmarking and identifying trends. For example, you might find that average days to fill vary by department or role type, which can help focus improvement efforts where they are needed most. Key Factors That Affect Time to Fill Many variables can influence how long it takes to fill an open role. Understanding these factors helps HR teams and business leaders interpret time to fill metrics and identify where improvements can have the most impact. Some of the most common factors include: Role complexity and market demand Senior-level roles, highly specialized positions, or jobs in competitive talent markets typically take longer to fill. The availability of qualified candidates directly affects average days to fill a position. Internal process efficiency Slow approval cycles, unclear job requirements, or delays in interview scheduling can all add days to the hiring timeline. Streamlining these internal processes is often one of the fastest ways to reduce time to fill. Employer brand and candidate experience Strong employer branding helps attract more qualified candidates, while a positive candidate experience keeps them engaged throughout the process. Both can contribute to shorter hiring timelines. Technology and tools The right technology can significantly improve time to fill. Modern applicant tracking systems (ATS), automated scheduling tools, and AI-powered candidate sourcing all help move candidates through the pipeline more efficiently. Time to Fill Benchmarks and Trends Knowing how your organization’s time to fill compares to industry standards can help set realistic goals and highlight opportunities for improvement. While benchmarks vary widely by industry, region, and role type, a few patterns tend to hold: Overall averages: Across industries, the average days to fill a position typically ranges from 30 to 45 days. However, some roles can move much faster or slower depending on demand and internal processes. Industry differences: Tech companies, for example, often face longer time to fill cycles for specialized engineering or data roles, while industries such as retail or hospitality may have much shorter hiring timelines for high-volume positions. Role-specific trends: Leadership positions and niche skill roles usually take the longest to fill. For these, it’s important to track benchmarks by role type rather than relying on a single company-wide average. It’s also important to track internal trends over time. If your average days to fill a position are increasing, that may signal new bottlenecks or changing market conditions. Conversely, a steady decline can point to process improvements or stronger employer branding. How to Improve Time to Fill Without Sacrificing Quality While reducing time to fill is a valuable goal, speed alone isn’t enough. The aim is to improve efficiency without compromising the quality of hires. Here are several proven ways to achieve that balance: Streamline hiring workflows Look for unnecessary steps, handoffs, or approval delays in your process. Clear ownership, well-defined roles, and faster decision-making can all help shorten time to fill. Use data to drive decisions Analyzing time to fill metrics at each stage of the hiring funnel can highlight where delays occur. For example, if interview scheduling adds significant time, investing in automated scheduling tools may provide a quick win. Strengthen collaboration with hiring managers Engaged hiring managers who are clear on role requirements and committed to the process help move candidates through the pipeline faster. Regular communication and shared expectations are key. Attract better candidates faster A strong employer brand and a positive candidate experience naturally reduce time to fill. When candidates are eager to work for your organization, they are more likely to move quickly through the process and accept offers. The goal is not simply to hire faster, but to create a well-managed process that consistently brings the right candidates to the right roles — and does so in a timely and efficient way. Tools and Technology That Can Help The right technology can make a measurable difference in improving time to fill. Many modern HR tools are designed to streamline key parts of the hiring process, reduce manual effort, and speed up decision-making. Here are a few categories to consider: Applicant Tracking Systems (ATS) An ATS provides a central platform to manage candidates, automate communications, and track time to fill metrics across roles and departments. It helps ensure consistency and visibility throughout the process. Recruitment marketing platforms Attracting the right candidates faster starts with effective sourcing and employer branding. Recruitment marketing tools help promote open roles, target ideal candidates, and manage talent pipelines. AI and automation AI-powered sourcing tools can identify qualified candidates more quickly, while automation can speed up tasks like screening, scheduling interviews, and sending updates to candidates. These tools can help reduce bottlenecks and shorten average days to fill a position. Analytics and reporting Beyond tracking time to fill, robust analytics can reveal deeper trends in your hiring data. Understanding which roles, departments, or stages consistently cause delays gives you the insights needed to drive focused improvements. This is where One Model stands out. As a leading People Analytics platform, One Model helps HR teams to go beyond surface-level metrics. It doesn't just show you how long it takes to fill a role; it helps you understand why. With advanced analytics, predictive modeling, and customizable dashboards, One Model enables organizations to pinpoint process bottlenecks, forecast hiring demand, and even identify which candidates are most likely to succeed long-term. By integrating data from your ATS and other HR systems, One Model delivers a holistic view of your talent pipeline and helps transform hiring from a reactive process into a strategic advantage. Key Takeaways on Time to Fill Time to fill offers valuable insight into the efficiency of your hiring process, but it’s only one part of the broader talent picture. Tracking this data point consistently, benchmarking it thoughtfully, and using it to inform improvements can help organizations hire more effectively and maintain business agility. At the same time, the goal isn’t simply to move faster. The most effective hiring strategies balance speed with quality, ensuring the right candidates are brought into the organization through a thoughtful, well-managed process. With the right data, tools, and collaboration between HR and business leaders, companies can make meaningful progress in improving both hiring outcomes and the experience for everyone involved. Looking to improve your hiring speed and optimize your recruiting process? Explore more expert insights in our Resource hub. Or see firsthand how One Model helps leading organizations reduce time to fill. Schedule a demo now.
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Featured
11 min read
Jun 17, 2025
People Analytics is one of the fastest-growing areas in HR—but if you're wondering what People Analytics actually involves, or how to build a People Analytics career, you're not alone. Many new leaders quickly discover that success depends on far more than analytics skills—it requires navigating a complex data supply chain. What Does People Analytics Involve? In a new People Analytics role, even experienced professionals often find themselves grappling with challenges that go well beyond a new work environment. Though hired to deliver workforce insights and instill a data-driven mindset, they soon face upstream challenges in what we call the People Data Supply Chain—a web of systems, stakeholders, and limitations that restrict access to the data and tools they need across HR functions. Within weeks, new People Analytics leaders often find themselves working more closely than they expected with data engineering, technology, HR operations, and senior leaders—just to achieve one thing: clean, strategic, and actionable workforce data. For those beginning a People Analytics career, working through these challenges may feel overwhelming, but it is, in fact, the foundation of high-impact analytics work. The Journey to Actionable Data It can help to know that this is the gig; you're not alone. It comes with the territory. To that end, the image below depicts the 30,000-foot view of this uncharted path. Dive deeper into the experience below or listen to Richard Rosenow’s keynote at People Analytics World in London on why it’s so hard to grow in People Analytics—and what data-savvy professionals can do about it. The Hidden Culprit: Data Supply Chain Chaos We’re going to follow a fictional People Analytics leader who just joined a large tech company. The company has been curious about People Analytics for some time and is finally ready to dip their toes into the water. This new leader is tasked with building their People Analytics function from the ground up. It’s very instructive to know upfront that the root problem isn’t people or effort—it’s misalignment of data ownership, system setup, reporting expectations, org structures, and tech stacks. These factors quietly but powerfully constrain even the most capable leaders. People Analytics Leader as Data Engineer The new People Analytics leader begins by taking stock of available data, identifying extraction points, assessing availability, and persuading stakeholders to grant access. Along the way, they must also learn how the organization defines its metrics. These early steps are critical because People Analytics isn’t just about pulling reports. It’s about building analytical models that can turn workforce data into real insights. But like many companies still catching up to the “analytics revolution,” this one hasn’t prioritized data infrastructure. Key information is scattered, outdated, or simply unavailable for People Analytics work. The leader quickly learns to be resourceful— borrowing, patching and improvising wherever possible. That’s long been a trait of People Analytics professionals: generating value despite limited access. But as the questions grow more complex, issues of data quality will start to surface and scrappiness will only go so far. People Analytics Leader as Technologist Our People Analytics leader soon hits a wall, not because data is missing, but because system configurations and reporting structures are misaligned. Despite the team's investment in advanced HR systems like Workday, SuccessFactors, and Greenhouse, generating reliable, actionable insights remains a struggle. To troubleshoot, the leader focuses on tasks typically assigned to HR analysts or IT: resolving system issues, reworking configurations, and collaborating with tech teams, diverting even more time from their core analytics work. This would be challenging enough on its own., but HR technologists and IT teams often aren’t equipped for these data issues either. Their focus is typically on launching scalable systems and enhancing workforce experience, not on ensuring data is ready for advanced analytics. Once a system goes live, their role is usually complete, leaving gaps the People Analytics team must fill. To be fair, People Analytics is still new territory for many technologists. But recent waves of costly rework have encouraged stronger partnerships between functions. It’s now more common for People Analytics teams to be involved early in HRIS and tech implementations. Eventually our leader learns that the technologist hat stays on. HR enterprise systems are highly configurable and rarely implemented the same way twice. While this flexibility supports business complexity, it also creates massive variation in how data is structured and surfaced. And since upstream teams define system requirements, People Analytics rarely controls configuration decisions—yet they’re the ones who feel the consequences. People Analytics Leader as HR Operations Leader Now our People Analytics leader encounters a fundamental truth about technology: without standardized processes, documented operational methods, clear documentation, and operational guardrails, even the best systems will fail. When processes are inconsistent or ad hoc, the data they generate is unreliable—and no amount of analysis can reconnect it to meaningful business goals. Take, for example, an Applicant Tracking System (ATS). It depends on consistent process flows to produce clean data. But if a recruiter, eager to close a candidate, bypasses key steps or stages, the ATS won’t accurately reflect activities—and the people data becomes fragmented. Even the best recruiting tools require defined process maps, clear logic, and agreement on what steps come first—or how edge cases like evergreen requisitions should be handled. Tools that promise to automate or simplify HR functions don’t eliminate the need for upfront process design and operation rigor. Requirements still need to be gathered. Standards still need to be set. New tech can’t fix broken operations; and it certainly can’t replace the need for strong documentation, change management, and cross-functional support. Armed with this knowledge, the leader now steps into an unexpected role: HR operations partner. They begin working side-by-side with ops teams to standardize workflows, map business logic, and define consistent practices for data entry. This upstream work directly shapes downstream configuration and data architecture decisions for People Analytics. It’s worth it. Clean data starts with clean processes. But just as progress is made, a deeper issue surfaces: there’s no clear workforce strategy guiding these efforts. You can't standardize your way out of confusion. Without a strategic direction, even the best operational improvements risk becoming disconnected, temporary fixes. The organization is at a crossroads. And the People Analytics leader is headed toward one final role. People Analytics Leader as Strategist By this stage, our People Analytics leader has learned that data standardization alone isn’t enough. Without a clear workforce strategy, operational consistency lacks direction. A documented strategy is needed to define how HR resources its programs, processes, and technology to achieve business goals. Strategy becomes the guiding light for People Analytics, enabling the team to align their work with organizational outcomes and measure its true impact. The most mature People Analytics teams don’t just respond to strategy–they shape, support, and influence it. While the CHRO sets the strategic vision, our leader collaborates closely to synthesize business needs, assess current HR capabilities, and prioritizes initiatives across HR. Leaders with strong skills in strategic planning and project execution bring immense value to their organizations and to their own People Analytics career. This strategic alignment makes it possible to scale operations, accelerate delivery, and optimize technology investments. With the right strategy, systems, and operational processes in place, the team finally gains access to clean, connected data that directly supports business priorities. And with that, the leader can return to the heart of their role. But this journey has revealed something deeper than the need for better data. It has revealed the entire people data supply chain and the critical role People Analytics plays in holding it together. A Guide for Charting the Path Ahead You’ve just seen how the People Analytics journey moves through data engineering, HR tech, operations, and ultimately into strategy. It’s not a straight line—and it’s not a solo effort. Within this environment, a new role is quietly taking shape: someone who can connect the dots. That role—the Workforce Systems Leader—is critical to helping People Analytics teams do their own jobs, which is to navigate complexity, close data gaps, and finally focus on delivering strategic insights. In From Data to Strategy: The New Role of Workforce Systems Leaders in Transforming HR, author Richard Rosenow covers: How to recognize this evolving role in your own org Where the data gaps really begin (and how to close them) Practical steps to align strategy, systems, and stakeholders If you’ve lived this journey—or are looking to the future—this guide is for you. Discover what it takes to fix your People Data Supply Chain—and how One Model empowers Workforce Systems Leaders to lead that transformation. Download Whitepaper Now
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8 min read
Jun 17, 2025
Turning headcount data into actionable budget insights can feel like magic, especially when you're trying to align people investments with changing business needs. While finance teams often lead budgeting, HR and People Analytics teams are uniquely positioned to bring the headcount context that makes workforce cost planning smarter and more responsive. That’s where cost allocation comes in. By understanding how workforce costs are distributed across departments, projects, or roles, you gain sharper insight into how labor impacts operations and strategy, not just the bottom line. It’s not just about financial reporting; it’s about supporting better decisions around hiring, planning, and prioritization. As Phil demonstrates in the video above, One Model simplifies this process using our flexible data orchestration model. Whether you're answering budget questions, supporting workforce planning, or enabling leadership with better insights, our People Data Platform helps you connect the dots. Why Headcount Alone Isn’t Enough for Budget Planning It is no longer enough to get a holistic cost allocation from your headcount. Organizations across the globe need to be able to slice and dice their data to really understand how those costs are changing over time and how to best build a thriving workforce. Traditional views showing headcount over time are excellent starters, but the main course? That's translating those numbers into actionable cost insights. After all, understanding not just the size but also the cost of your workforce over time is the key to informed decision-making for both finance and operations teams. For example, slicing and dicing dynamic cost allocation over time with a per-day breakout based on total days in month, filtered by department, supervisor hierarchy level, or length of time employed can lead to insights that can change policy or articulate critical headcount needs. How One Model Translates Headcount into Cost Allocation One Model possesses unique capabilities that can transform your traditional headcount chart into a sophisticated cost analysis tool. What makes us unique? It all has to do with the data model. Once your data is modeled, you gain access to a variety of metrics that you can use as is or modify to fit your specific business needs. Diving into your compensation grouping of metrics, you can replace the “headcount, end of period” metric with “headcount, beginning of period” or append it with the “average salary, end of period” metric. Delving deeper, the real magic happens as One Model enables you to convert that average salary into a robust cost allocation strategy. With the dynamic "compensation cost daily allocation" metric at your disposal, it's like having a personal assistant that adjusts effortlessly to varying time durations, including accommodating leap years. Furthermore, One Model recognizes the fluctuations in costs, especially during shorter months or leap years, ensuring a more precise and insightful view of your financial landscape. This capability allows you to make more informed decisions and gain a deeper understanding of your organization's financial dynamics. Drilling Into Workforce Cost Segments by Team, Time, and Tenure Each organization is akin to a mosaic, with numerous sections and subdivisions. With One Model, you can delve into each segment, examining the cost allocation intricacies at every level. The insights gleaned can empower both finance and operations professionals, offering clarity in strategy and resource allocation. Why Overhead Allocation Matters for Finance and HR Leaders Cost allocation is crucial for various reasons in business and financial management. Here are four key reasons why it's important to pay attention to cost allocation: Fairness and Equity Overhead allocation ensures that costs are distributed fairly among different departments, products, or projects. This fairness is essential for budget allocation and growth in each department. Performance Measurement Allocating costs accurately allows for better measurement of the performance of different departments or business segments. By attributing costs to specific activities, it becomes easier to identify areas of inefficiency and make necessary improvements. Profitability Analysis Cost allocation helps in determining the profitability of products, services, or business units. This information is invaluable for making strategic decisions about resource allocation, product pricing, and business expansion. However, read our other considerations when breaking down revenue in our average revenue per employee blog. Resource Allocation When costs are allocated appropriately, organizations can allocate resources more effectively. It helps in identifying where additional resources are needed and where resources might be overallocated, leading to cost savings. Visualizing Workforce Cost Allocation Over Time One Model lets you visualize your cost allocation journey over time through detailed charts. While this can present a plethora of data, each data point offers invaluable insights. For those who prefer a more structured representation, a tabulated view can provide clarity. All you need to do is create a data set that shows the amount of cost to allocate, along with the start and end dates of that allocation. From current headcount to cost allocation for recruiting, the process to get the answer is the same. For example, if you spent $10,000 on job advertisements on LinkedIn from Jan. 1, 2018, to Dec. 31, 2018, One Model can efficiently allocate that spend per day throughout the year. This becomes very useful when combined with other metrics over periods of time. For example, I can compare what I'm spending on LinkedIn with the number of applications I receive from LinkedIn during that period. This yields a "Cost Per Application" metric that I can use to compare the effectiveness of LinkedIn relative to other sources. The Takeaway: Smarter Workforce Cost Planning Starts Here If the daunting task of juggling countless spreadsheets, numbers, and formulas sounds all too familiar, there's a better way. One Model is designed to transform the perplexing world of cost allocation and overhead allocation and creating a tailored cost allocation plan into a more straightforward, efficient process. Tired of managing cost allocation through spreadsheets and guesswork? One Model simplifies workforce cost planning with smart metrics and actionable insights. Schedule a personalized demo to see it in action.
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Featured
10 min read
Jun 13, 2025
Ever played with a Magic 8 Ball? You'd ask it a question, give it a shake, and voilà, you had your answer. If you didn’t like it? Shake again. A fun party trick, but hardly the foundation for strategic decision-making. Today’s HR leaders and businesses need more than luck and guesswork. They need data-driven insights to anticipate challenges and plan for the future. Two key techniques helping organizations move beyond the realm of chance are forecasting and predictive analytics. Though they sound similar, they serve distinct purposes. And together, they’re a powerhouse combination. Let’s dive into what each approach offers and why both are essential. What is HR Forecasting? Forecasting, at its core, is about extending historical trends into the future based on known data. It’s a statistical process that estimates future outcomes using past and present information, no guessing allowed. In HR, forecasting is used to predict metrics like future headcount, turnover rates, or the need for new hires. It builds on patterns already visible in historical data, projecting them forward in time to help leaders plan strategically. For example, by analyzing trends from the last three years, you might forecast your organization’s attrition rate for the next six months. This helps with resource planning, budgeting, and setting realistic expectations for the business. In One Model’s One AI, forecasting is seamlessly embedded into storyboards. With a click, users can extend trends forward and visualize future states. Behind the scenes, One AI uses techniques like ARIMA, Curve Fit, or Prophet (depending on your data’s characteristics) to build highly configurable forecasts. Whether you're predicting headcount or engagement trends, One AI ensures you're working with a rigorous, statistically sound projection, not a hopeful guess. One key feature: forecasts include confidence intervals. These are shaded areas that represent the range within which the forecasted values are expected to fall. This equips HR teams to not just plan for a likely future, but to prepare for a range of potential outcomes. What is Predictive Analytics? While forecasting projects trends forward, predictive analytics digs deeper to uncover why future events might occur. It analyzes historical and current data to identify patterns and relationships, predicting not just what could happen, but offering insights into the underlying drivers. In HR, predictive analytics might help you anticipate which employees are most likely to resign or which candidates are most likely to succeed. Instead of simply showing that turnover might increase, predictive models can point to the factors driving that trend, like low engagement scores, lack of career progression, or team instability. With One AI, predictive analytics is taken a step further with explainability and transparency. One AI’s models provide not only predictions but also detailed explanations through techniques like SHAP values and feature importance scores . This ensures HR teams can understand the "why" behind each prediction, enabling trust and actionable insights. For instance, rather than offering a black-box prediction, One AI will tell you that tenure, performance ratings, or recent changes in team structure are key risk indicators for turnover. One AI ensures that predictive models are auditable and defendable, critical in today’s regulatory landscape. No hidden magic, just transparent, responsible AI. Why You Need Both You might wonder, do you really need both forecasting and predictive analytics? The short answer: absolutely. Forecasting provides the big picture. It tells you where the ship is headed if current trends continue. It’s crucial for setting budgets, workforce planning, and managing executive expectations. Predictive analytics provides the why behind the trends. It flags risks and uncovers opportunities, helping you to take corrective actions before issues become crises. Forecasting shows that your organization's attrition rate will spike in Q4. Predictive analytics digs in and reveals that the employees most likely to leave are those without recent promotions or learning opportunities. Now you’re not just bracing for impact, you’re adjusting course. Together, these approaches offer strategic foresight and targeted interventions. Forecasting prepares you for what’s ahead; predictive analytics empowers you to change the outcome. How One AI Supercharges Forecasting and Predictive Analytics At One Model, we’ve built One AI to empower HR and business leaders with both forecasting and predictive analytics in one platform. Here’s what sets it apart: Embedded Forecasting: Easily extend trends into the future with configurable settings with confidence intervals and period granularity right from your dashboards Predictive Modeling with Explainability: Generate models that not only predict but explain. Understand top drivers behind outcomes and explore detailed SHAP values to see how different factors contribute to each prediction. Transparency and Trust: One AI was designed with explainability at its core, helping you meet regulatory requirements and build stakeholder trust. Ease of Use: Whether you’re an HR leader, analyst, or data scientist, One AI’s intuitive workflows and One AI Assistant make it easy to request forecasts, configure models, and understand results without needing a PhD in statistics. Before you trust the predictions, understand the data. Watch One Model's Hayley Bresina and David Poe break it down in this insightful on-demand video walkthrough. Your Next Step Toward Smarter Decision-Making Predicting the future isn’t about crystal balls or Magic 8 Balls anymore. It's about leveraging your data intelligently. Forecasting and predictive analytics are two sides of the same coin, both vital for building resilient, future-ready organizations. With One AI, you can have both forecasts to set your course and predictive insights to steer it wisely. Curious how forecasting and predictive analytics work in One AI? We can help you figure it out and get started. Book your personalized demo. Forecasting vs. Predictive Analysis: Other Relevant Terms Machine Learning - Machine learning is a branch of artificial intelligence (ai) where computers learn to act and adapt to new data without being programmed to do so. The computer is able to act independently of human interaction. Read Machine Learning Blog. Data Science - Data science is the study of big data that seeks to extract meaningful knowledge and insights from large amounts of complex data in various forms. Data Mining - Data mining is the process of discovering patterns in large data sets. Big Data - Big data is another term for a data set that's too large or complex for traditional data-processing software. Learn about our data warehouse. Predictive Modeling - Predictive modeling is a form of artificial intelligence that uses data mining and probability to forecast or estimate more granular, specific outcomes. Learn more about predictive analytics. Descriptive Analytics - Descriptive analytics is a type of post-mortem analysis in that it looks at past performance. It evaluates that performance by mining historical data to look for the reasons behind previous successes and failures. Prescriptive Analytics - Prescriptive analytics is an area of business analytics dedicated to finding the potential best course of action for a given situation. Data Analytics - Plain and simple, data analytics is the science of inspecting, cleansing, transforming, and modeling data in order to draw insights from raw information sources. People Analytics - All these elements are important for People Analytics. Need basics? Learn more about People Analytics. About One Model One Model’s People Analytics solutions help thriving companies make consistently great talent decisions at all levels of the organization. Large and rapidly-growing companies rely on our People Analytics platform and One AI because they takes all of the heavy lifting out of data extraction, cleansing, modeling, analytics, and reporting of enterprise workforce data.
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Featured
6 min read
Jun 03, 2025
Knowing your data matters is one thing. Knowing how to build and sustain real confidence in it? That’s another challenge entirely. And that’s where many HR leaders are: stuck between rising expectations, constant change, and tools that don’t quite deliver. Transparency: The Core of Data Confidence Data confidence—the ability to trust your analytics—doesn’t happen by accident. It’s built through a combination of clear processes, strong data literacy, and transparency. Even the most accurate metrics won’t inspire trust if HR teams aren’t equipped to understand, explain, and stand behind them. Take, for example, a global retailer that struggled with inconsistent DEI metrics, undermining executive trust. To rebuild credibility, they launched a transparency initiative: opening their data dictionary to all HR business partners and holding regular reviews to clarify definitions and updates. Over time, HR leaders became more proactive—engaging deeply with the numbers, anticipating tough questions, and leading conversations with renewed authority. The result? A lasting culture of data fluency and confidence. Do you trust your HR analytics? Hear how Marcus Joseph, One Model's Customer Advisory Director, pinpoints the challenge for HR professionals who are being asked to act on people data they don’t fully trust. 5 Key Steps for Strengthening Data Confidence For People Analytics teams, the challenge isn’t just providing clean data—it’s creating the conditions where HR professionals can use that data with clarity and conviction: Strengthen data literacy Equip HR leaders with the ability to understand core metrics, analytical methods, and data governance principles. This empowers them to interpret numbers accurately and anticipate questions from stakeholders. Increase transparency Eliminate the “black box” by making data sources, definitions, and processes visible and traceable. Transparency enables HR to validate insights in real time and build resilience under scrutiny. Adopt intuitive tools Provide user-friendly, HR-focused platforms that allow exploration and validation of data without needing deep technical skills. The goal: make data confidence accessible, not exclusive. Align metrics across teams Ensure all departments work from the same playbook. Standardized definitions (e.g., for headcount, turnover, DEI metrics) prevent misalignment and mixed messaging. Encourage data-driven decision-making The more HR engages with data actively—rather than reactively—the stronger its confidence becomes. Regular use breeds familiarity and authority. The Competitive Advantage of Reliable-Data-Driven HR Data-driven precision is now table stakes—not just for finance and operations but for HR too. Without strong data confidence, HR risks being sidelined in critical conversations. But when HR shows up with clarity, consistency, and deep understanding, it becomes an essential architect of business success. Compliance also looms large: inaccurate reporting can trigger regulatory scrutiny or legal exposure—particularly around sensitive topics like diversity, pay equity, and workforce safety. The solution? Invest in people, processes, and purpose-built tools that allow HR to manage and own its data confidently. A robust data foundation unlocks not just better decisions but also stronger compliance, risk management, and long-term strategic value. Download our new Ebook or explore more blogposts about data confidence: The Confidence Gap: Why Data Confidence Defines HR's Strategic Influence One Model: Total Data Confidence for HR Teams. Ready to learn why One Model users trust their data? Let's talk.
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Featured
6 min read
May 21, 2025
HR data is messy—full of unique definitions, transactions, and constantly shifting processes. Tracking something as simple as headcount can get complicated fast, especially when you add in hiring, promotions, lateral moves, and leaves of absence. Every metric demands precision, consistency, and tight governance. (In this final post of 3, we share how One Model's approach enables total data confidence. Catch up with Part 1 and Part 2.) The Risk of Losing Context Traditional business intelligence (BI) tools aren’t built for the way HR works. They handle finance and operations data well enough, but HR is different. People don’t move through an organization in straight lines, and that creates a web of data that BI tools struggle to untangle. When HR teams hand off data management to external tech groups or rely on generic BI platforms, they lose critical context—the kind of business understanding that makes data truly useful. Worse, without clear ownership and governance, different teams start defining metrics in their own ways. That’s when inconsistencies creep in, confusion spreads, and trust in the numbers starts to erode. A Solution Built for HR A purpose-built HR model acts as a single source of truth—owned and governed by HR itself. It doesn’t just ensure accuracy; it makes the data actionable in real time. Take, for example, a talent acquisition team using One Model’s HR data framework. They noticed a surge in attrition risk metrics just weeks after launching a new hiring policy. Instead of scrambling months later to diagnose the issue, they were able to pinpoint the problem early and quickly adjust onboarding processes—saving both talent and reputation. This kind of proactive insight becomes possible when HR owns its data story. With a solid framework in place, teams can: Define metrics clearly: No more guesswork around headcount or movement tracking. Ensure consistency: Automated pipelines keep data aligned and up to date without manual “fixes.” Retain control: HR manages its own data definitions, rather than relying on external teams unfamiliar with HR’s complexities. Enable proactive decision-making: Spot red flags early and adapt processes quickly. Confidence in data is more than a technical goal—it’s a strategic imperative. When HR owns its data story, backed by clarity and precision, it earns a permanent seat at the table. By embracing transparency, literacy, and purpose-built solutions, HR leaders can step into their rightful role as strategic powerhouses—turning raw numbers into trusted insights that drive business forward. Download our new Ebook or explore more blogposts to learn how HR leaders actually gain data trust—and sustain it—in the real world. Why Rock-Solid Data Confidence is HR's Secret Weapon The Confidence Gap: Why Data Trust Definites HR's Strategic Influence No validation, no trust. That’s why One Model handles validation for you — so your team can act with confidence, not doubt. Our VP of People Analytics Strategy Richard Rosenow explains how it works. Tired of data doubt? Let's connect.
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Featured
6 min read
May 16, 2025
A small tech company wanted to know if their internal communications analytics could help them predict attrition. This wasn’t about tweaking emails or sending a better all-hands invite. This was about exploring whether activity data from their internal communications platform could give them a deeper, more predictive view of their workforce. Turns out, it could. From Comms Activity to Workforce Insight The tech company already had a robust internal comms platform in place — a space where employees, remote and on-site, could read company updates, interact with leadership, and connect with peers. On the surface, this data seemed simple: clicks, views, and replies. But underneath? A rich layer of internal communications analytics was waiting to be tapped. They brought in One Model to help integrate this data with their HR stack — combining traditional sources like their HRIS and ATS with this new layer of comms activity. Because One Model tailors its models to each client’s data ecosystem, the company retained full control over what data was included. Only aggregated, activity-based data was used — no message content, no personal identifiers. Privacy was baked in.* Right from the start, preliminary data exploration revealed exciting patterns in the usage of the internal communications platform across various segments, such as team, location, function, and level. These patterns provided a foundational understanding of employee engagement and interaction with the platform that immediately gave direction to the tech team on how to best support future engagement. Then One AI Took a Look Once the data was modeled, One AI — One Model’s flexible machine learning platform — was tasked with one thing: predicting attrition. Here’s what surprised everyone: When the model was given a choice between traditional HR data and internal communications data, it overwhelmingly preferred the latter. The way employees engaged with internal messaging was a stronger signal of their likelihood to stay or leave than even their tenure, department, or performance rating. That’s a bold takeaway: This project didn’t just prove integration is possible — it proved it’s valuable. Internal Communications as a Listening Post One of the most overlooked aspects of HR and internal communications is that comms data can tell us what surveys and dashboards can’t. It captures behavioral signals in real-time. This pilot showed how powerful that can be — helping leaders not only see trends faster, but act on them with precision. It’s not just about broadcasting; it’s about listening, analyzing, and adjusting in ways that align messaging with employee mindset. If your internal communications platform is just a bulletin board, it might be time to make it a listening post. And it raises a bigger question for other organizations: What stories are hiding in your internal communications data — and what might they be trying to tell you before it’s too late? What Comes Next For the tech company, this was just the start. They’re now looking at how comms data might enhance onboarding, engagement tracking, and even internal mobility. For other organizations, it’s an invitation to move past vanity metrics and begin using internal communications analytics to fuel smarter, faster, and more human decision-making across HR and internal communications teams. The tools are here. The data is ready. And if this pilot proved anything, it's that the right questions can lead to unexpected and transformative answers. Curious about more potential applications with connected HR and internal comms data? Download Enhancing HR & Internal Communications with People Analytics. Note: Activity data can be incredibly useful, but it's also highly sensitive. One Model takes data security and data privacy seriously. In this pilot, we were very careful to bring in only activity, not content. One Model worked with the partner to report only on segments of the company and not individuals. If you have questions or want to discuss how to approach ethical internal communications data use at your company, please reach out.
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Featured
12 min read
May 09, 2025
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
6 min read
May 08, 2025
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. Download our new Ebook or explore more blogposts to learn how HR leaders actually gain data trust—and sustain it—in the real world. Why Rock-Solid Data Confidence is HR's Secret Weapon One Model: Total Data Confidence for HR Teams Want to see what trustworthy data looks like? Connect with us.
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Featured
8 min read
May 05, 2025
At One Model, we know the best insights come from seamless HR data integration. That’s why we’ve doubled down on enhancing our connections with industry-leading platforms like Workday People Analytics, SAP SuccessFactors HR solutions, and Oracle PeopleSoft HCM. These integrations don’t just connect systems — they unlock the full potential of your workforce data, empowering you to make decisions that drive real impact. 1. Our Workday Integration: Driving Innovation Through Partnership In 2024, One Model became a proud Workday Innovation Partner! This milestone reflects our commitment to delivering cutting-edge solutions for Workday customers. Our enhanced Workday Connector is designed to ensure you can move beyond the basics and get actionable insights that truly make a difference. Why Workday + One Model stands out: Direct access to Workday’s core data sets without disrupting your workflows. Advanced analytics that make Workday data even more impactful. Innovation recognized by Workday itself through our official partner status. Curious about what this means for you? Take a deeper dive into the possibilities of liberating your data with our updated Workday People Analytics Playbook. 2. Our SuccessFactors Integration: Unlocking Your HR Powerhouse SuccessFactors is a go-to platform for HR leaders, known for its robust workforce data. But having great data is only half the battle — making it actionable is where One Model comes in. With our SuccessFactors integration, you get clean, consistent data flows and a foundation for high-impact analytics. What makes this integration special: Streamlined extraction of key people data like workforce demographics, performance, and learning metrics. Flexible data models that adapt to your business needs. Real-time insights that empower smarter decision-making. Explore how to make the most of your HR AND non-HR data. Download our guide to Unlocking SuccessFactors People Data. 3. Our Oracle Integration: Turn Complex Data Into Actionable Insights Oracle’s platforms are known for their depth and versatility, offering rich data opportunities. But managing that complexity can sometimes feel overwhelming. With One Model’s Oracle integration, you can cut through the noise, unifying data streams and simplifying access and empowering your team to focus on insights that drive results. Why this integration matters: Brings together diverse Oracle data sources for a unified view of HR metrics. Scales with your organization’s needs, no matter how complex your data ecosystem gets. Provides decision-ready insights so you can spend less time wrangling data and more time driving strategy.. Get the full story on maximizing Oracle’s potential and bringing your data to life. Download our Oracle People Analytics Playbook. Ready to Unlock Your People Data? Integrations are the backbone of effective analytics, and with One Model’s solutions for Workday, SuccessFactors, and Oracle, (and many other data platforms) you can empower your team with the insights they need to drive success. Whether you’re looking to simplify data extraction, enhance workforce planning, or align your HR strategies, our integration whitepapers are a great way to get started. Explore them now and see what more your data could be doing for you. Ready to take a look at One Model?
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Featured
4 min read
Apr 24, 2025
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
Apr 24, 2025
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
5 min read
Apr 23, 2025
Everyone on your team needs insights. Not just analysts, not just HR leaders, not just the People Analytics team. The more people who can access, understand, and act on data, the faster we move toward work that is more engaging, more aligned, more rewarding, and more productive. This isn’t about democratizing dashboards. It’s about empowering people with the right insights in the right context at the right time. It’s about breaking the cycle of a few experts interpreting data for the many and, instead, building a world where more people understand more data in more ways. That’s the shift that will define the future of work. Why Work Stalls: The Data Bottleneck For years, organizations have treated people data as something specialized—a tool for reporting, compliance, or leadership decision-making. It’s been locked in systems, controlled by a select few, and surfaced in static reports. The result? Decisions made without context People managers are asked to make workforce decisions based on gut feel or limited data access. Insights delayed By the time an analysis is complete and shared, the business has moved on. Missed opportunities The people closest to the work—employees, frontline managers, team leaders—don’t have access to the insights that would help them improve outcomes in real time. People Analytics teams have tried to solve this by building dashboards, sending reports, and embedding analytics into HR workflows. But the real opportunity isn’t just in making insights available; it’s in making them understandable and actionable across the organization. More People + More Data = Work That Works When more people can use more data in more ways, something powerful happens: work gets better, faster: Engagement improves when employees understand how their contributions connect to business outcomes. Alignment increases when leaders have a shared understanding of workforce dynamics, performance, and potential. Work becomes more rewarding when managers can make informed, data-driven decisions that support growth, retention, and well-being. Productivity accelerates when teams use real-time insights to optimize workflows, remove bottlenecks, and allocate resources more effectively. But to get there, we need more than just dashboards. The Shift: From Static Data to Agentic Workflows The future of People Analytics isn’t just better reporting—it’s better decision-making at every level. That’s where agentic workflows come in. At One Model, we’re focused on giving organizations not just data, but the ability to act on it—seamlessly, intelligently, and in the flow of work. That means: Ambient insights delivered where they’re needed, not buried in a dashboard Actionable recommendations that help managers and employees make better decisions, faster. Automated workflows that turn data into action—without the friction. Because when we stop treating data as something only for analysts, and start designing systems where everyone can engage with it, learn from it, and use it, we unlock the real potential of People Analytics. The Future of Work Is Already Here—If We Build It Organizations that embrace this shift won’t just see incremental improvements in HR metrics. They’ll see transformation: Higher retention, stronger teams, more strategic workforce planning, and a work environment that truly supports people. This isn’t about making People Analytics a bigger function. It’s about making it a bigger part of how work happens. The faster we move toward more people understanding more data in more ways, the faster we make work better for everyone. Let’s get to it. Explore how to break the bottleneck in your own org—request a custom walkthrough. Schedule a demo
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Featured
1 min read
Apr 17, 2025
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
Apr 14, 2025
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. HR 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. Explore more on adaptability: Visit blogpost: Adaptability in People Analytics: One Size Doesn't Fit All Or download our new eBook. Want to learn more? Request a demo today.
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Featured
5 min read
Apr 03, 2025
At One Model, we’re revolutionizing HR through Agentic AI, a powerful tool designed to automate workflows, enhance decision-making, and democratize data engineering. As organizations strive to make faster, more informed decisions, Agentic AI is reshaping how HR teams operate, offering unprecedented autonomy and efficiency. This blog serves as a recap of our previous discussions on Agentic AI and its impact on HR. Here’s a closer look at how Agentic AI is transforming HR departments, from streamlining data access to delivering real business value. Redefining HR Operations Agentic AI is more than a tool; it’s an intelligent assistant that automates routine tasks and handles complex workflows. Unlike traditional chatbots, it autonomously navigates tasks, connects data sources, and generates insights. This frees HR teams from mundane work, enabling them to focus on strategic decisions that drive business impact. Agentic AI reduces operational overhead by automating tasks like data analysis and report generation, enabling HR teams to make faster, data-driven decisions without relying on IT support. It provides real-time insights for proactive decision-making, such as performance forecasting and retention management. The Importance of a Solid Data Foundation For AI to work effectively, it needs access to high-quality data. This is where the importance of a robust data foundation comes into play. Without well-organized, structured data, even the most advanced AI tools can’t generate valuable insights. Agentic AI relies on precise data management to execute complex workflows and deliver actionable insights. At One Model, we provide a flexible, secure platform that ensures data is clean, structured, and accessible, empowering HR teams to leverage AI’s full potential. Democratizing Data Engineering with Agentic AI One Model is revolutionizing HR by democratizing data engineering through Agentic AI. This allows HR professionals, without technical expertise, to handle routine data tasks like transformations, analysis, and reporting. Traditionally, HR teams depended on data engineers and IT support, creating bottlenecks and slowing decision-making. With Agentic AI, One Model embeds data expertise into its platform, automating these tasks. HR teams can now access real-time insights, create metrics, and modify data models on their own, making the process more agile. However, data engineers are still essential for complex tasks like optimizing data architectures, managing security, and handling large-scale integrations. By automating routine data management tasks, One Model’s platform reduces reliance on external technical support, giving HR teams more autonomy to focus on strategic priorities. This shift boosts efficiency, improves collaboration, and empowers HR professionals to make faster, more informed decisions. Driving Value Through Automation Agentic AI is more than just a tool for automating repetitive tasks. It’s designed to accelerate business outcomes by enabling HR teams to focus on high-value activities. From reducing reliance on data engineers to speeding up the implementation of new data sources, Agentic AI drives operational efficiency across the board. By automating workflows and enhancing decision-making, organizations can see faster time-to-insight and more accurate predictions, which leads to better strategic decisions. For example, consider a scenario where HR teams need to address rising employee turnover. With Agentic AI, these teams can quickly access up-to-date insights, analyze external data, and take immediate action to mitigate retention issues. This level of agility and responsiveness is a game changer. The Future of HR with Agentic AI Looking ahead, One Model is committed to expanding the capabilities of Agentic AI, ensuring that HR teams have the tools they need to navigate the complexities of modern work. As the platform evolves, it will offer even more flexibility, security, and control, helping HR leaders unlock deeper insights and drive smarter business decisions. With the ability to reduce manual effort, improve data analysis, and democratize data engineering, Agentic AI is positioning HR departments for the future of work. Read more about Agentic AI for HR: Democratizing Data Engineering with Agentic AI How Agentic AI Will Generate Value for Organizations Why You Need a Good Data Foundation for Agentic AI Agentic AI and the Future of One Model Ready to learn more? Request a demo.
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7 min read
Mar 28, 2025
Accessing and making sense of data has traditionally required reliance on data engineers and IT teams. This often slowed decision-making and limited HR’s agility. Now, with Agentic AI, One Model is changing that by democratizing data engineering. This technology empowers HR teams to manage and analyze data independently, enabling faster, more informed decisions without waiting for technical support. Let’s explore how Agentic AI is unlocking new levels of autonomy and efficiency for HR teams. The Traditional Data Engineering Challenge Historically, HR teams have relied heavily on technical experts, such as data engineers, IT departments, or external vendors to manage and analyze complex data. These technical teams were the gatekeepers to valuable insights, responsible for cleaning, structuring, and transforming raw data into something actionable. While these specialists brought crucial expertise, the process was often slow, inefficient, and out of reach for non-technical users. For HR departments, this dependency meant delays, bottlenecks, and missed opportunities for real-time decision-making. Relying on data engineers for every transformation or analysis no longer makes sense. The need for speed and agility is more pressing than ever, and HR teams must have the ability to interact directly with their data and generate insights without waiting for a technical team to step in. This is where Agentic AI becomes a game-changer. However, this doesn’t mean that data engineers are no longer needed. Data engineers will always be essential for overseeing complex data architectures, optimizing performance, ensuring data quality, and managing security. Their expertise remains critical in larger-scale transformations and integrations. The real shift with Agentic AI is that HR teams can now handle routine data transformations and analysis independently, freeing up data engineers to focus on high-level, strategic work, such as building sophisticated data models, creating complex workflows, and ensuring the scalability and security of data systems. What is Democratizing Data Engineering? At One Model, democratizing data engineering means breaking down the technical barriers that have traditionally hindered HR teams from fully leveraging their data. With Agentic AI, we’ve embedded data expertise directly into our platform, enabling HR professionals to interact with and manipulate data without the need for a full dedicated data engineering team. This shift allows HR teams to gain deeper insights, run experiments, and make strategic decisions faster than ever before. Unlike traditional systems where data transformations and model developments require specialized knowledge, One Model’s Agentic AI automates these processes, empowering users with the tools to directly engage with their data. By embedding AI into the system, we are removing the technical complexity from the equation and making data engineering more accessible to all levels of the HR team, regardless of their technical expertise. How Agentic AI Empowers HR Teams One of the core benefits of democratizing data engineering is that it allows HR teams to operate with greater autonomy. In the past, if an HR leader wanted to change a data model, they would have had to rely on technical support, which could take days or even weeks to implement. With Agentic AI, HR professionals can now make adjustments in real-time, using a set of powerful yet easy-to-use tools that handle the complex tasks traditionally reserved for data engineers. For example, HR professionals can now create new metrics, perform data analysis, and design new workflows without needing specialized knowledge of data modeling or programming. This self-service approach significantly reduces the dependency on external teams and speeds up the process of gaining valuable insights. By automating data manipulation and analysis, Agentic AI ensures that HR teams can focus on higher-level tasks like strategic decision-making, employee engagement, and talent management. The Future of Data Engineering with Agentic AI As Agentic AI continues to evolve, its ability to automate complex workflows and democratize data engineering will only grow stronger. We are constantly improving the platform’s flexibility, security, and adaptability, ensuring that HR teams can continue to evolve and make smarter decisions. With the power of Agentic AI at their fingertips, HR teams can harness the full potential of their data without needing a large team of data engineers. At One Model, we believe that the future of HR lies in empowering teams to access, analyze, and act on their data without unnecessary delays. By embedding AI directly into the workflow, we are not only simplifying the data engineering process but also giving HR professionals the tools they need to make impactful decisions faster. As we continue to integrate more advanced AI capabilities into our platform, HR teams will have even more autonomy and control over their data, allowing them to drive greater value for their organizations. See the Full Potential of HR with Agentic AI With One Model’s approach to Agentic AI, HR professionals no longer need to rely on large, specialized teams to manage their data. Instead, they can directly engage with the data, make adjustments in real-time, and generate insights without waiting for technical teams to intervene. This is the power of democratizing data engineering, and it’s only the beginning. Read more about Agentic AI for HR or download our new Ebook: How Agentic AI Will Generate Value for Organizations Why You Need a Good Data Foundation for Agentic AI Agentic AI and the Future of One Model Ready to take a look? Request a demo.
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Featured
6 min read
Mar 20, 2025
At One Model, we’re committed to providing solutions that not only simplify HR processes but also drive measurable business outcomes. With Agentic AI, we're able to automate workflows and deliver data-driven insights with unprecedented speed and accuracy. Unlike traditional chatbots, which only respond to queries, Agentic AI has the autonomy to make decisions, navigate complex tasks, and execute multi step setup workflows. This capability is crucial for HR teams, enabling them to move beyond mundane tasks and focus on strategic initiatives that impact the business’s bottom line. Reducing Operational Overheads One of the most significant benefits of Agentic AI is its ability to reduce reliance on human resources for repetitive tasks. Traditionally, HR teams rely on large data engineering teams to manage complex data and produce actionable insights. With Agentic AI, this process becomes automated, allowing HR professionals to bypass bottlenecks in the system and access valuable insights faster than ever before. As a result, HR teams can shift their focus to high-value work, such as enhancing employee engagement, optimizing talent acquisition, and addressing retention issues. Automating Complex Workflows Another way Agentic AI creates value is through the automation of end-to-end workflows. Consider the process of generating topical storyboards or loading new and critical data. These tasks often require multiple steps, from data collection to analysis and presentation. In the past, these tasks would have taken considerable time, requiring HR staff to manually compile information and coordinate work across different functions. With Agentic AI, these workflows can be automated, a plan is developed, data is processed, storyboards and insights are created all with minimal human intervention. Improving Decision-Making with Real-Time Insights Agentic AI excels at accelerating time-to-insight, empowering HR teams to make faster, data-driven decisions. For instance, in an environment where employee turnover is rising, HR teams can instantly access reports on retention drivers or load external data to better understand the emerging trend, helping them take proactive measures to address the issue. With the ability to pull data from various sources and analyze it on the fly, Agentic AI ensures that HR decisions are always based on the most up-to-date information. Leveraging Customizable Data Tools for Business Needs One of the standout features of Agentic AI is its adaptability to diverse business needs. Whether it’s forecasting employee performance or analyzing workforce demographics, One Model’s platform can accommodate a wide range of data types and structures, ensuring that the AI agents are always working with relevant and well-organized data. This flexibility allows HR teams to tailor workflows to the unique needs of their organizations, increasing the precision and relevance of insights generated. The Role of Human Oversight While Agentic AI can automate many tasks, it still benefits from human guidance. For instance, HR teams can work alongside AI agents to ensure that the insights generated align with business objectives and ethical standards, or to review a proposed plan the Agentic AI wants to take in order to accomplish a task and provide corrective feedback. This collaboration between AI and human experts ensures that the AI remains a powerful tool for decision-making, rather than a source of potential error or misdirection. Accelerating Value with Agentic AI Agentic AI offers organizations a way to unlock greater value from their data and processes. By automating mundane tasks, improving decision-making, and empowering HR teams with customizable data tools, Agentic AI streamlines operations and accelerates business outcomes. As we continue to refine and expand the capabilities of Agentic AI, One Model is committed to ensuring that HR teams can move faster, make smarter decisions, and ultimately generate more value for their organizations. Explore more blogposts about Agentic AI for HR or download our new eBook: Democratizing Data Engineering with Agentic AI Why You Need a Good Data Foundation for Agentic AI Agentic AI and the Future of One Model Want to learn more? Request a demo today.
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Featured
7 min read
Mar 20, 2025
Securing budget for new HR technology often hinges on one question: What’s the return on investment? People Analytics, while universally acknowledged as valuable, can still raise skepticism among finance teams or executives who want hard evidence that it will pay off. The good news is that a well-structured approach can deliver both financial and strategic returns, and People Analytics platforms like One Model offer clear proof points that help justify the spend. Going Beyond “Nice-to-Have” Historically, HR initiatives have been labeled as “soft” investments that are tough to measure in dollar terms. Yet turnover, engagement, and productivity each have a direct effect on the bottom line. Consider the cost of replacing a mid-level employee—recruiting fees, onboarding expenses, and lost productivity can easily total 20% of that individual’s salary. By applying People Analytics to spot flight risks early and prioritize retention strategies, companies can significantly lower those replacement costs, creating a measurable impact executives can’t ignore. Tying Metrics to Business Outcomes Building a solid business case means linking your HR data to tangible business drivers: Reduced Turnover: If a department has a historically high churn rate, even a modest improvement can free up thousands—if not millions—of dollars annually.Here’s how to calculate turnover costs and their impact. Improved Productivity: Identifying the skills gaps causing performance issues can lead to targeted training programs that raise output and reduce error rates. Faster Hiring Cycles: A shorter time-to-fill for crucial roles means key projects don’t stall, potentially preserving revenue and customer satisfaction. Learn more about key People Analytics dashboards that visualize these metrics. When you show how these metrics directly influence revenue, profitability, or operational efficiency, leadership is far more willing to invest. Hypothetical ROI Scenario: One Model in Action Imagine an organization with 2,000 employees and an annual voluntary turnover rate of 15%. Replacing a single departed employee might cost 20–30% of their salary—let’s approximate $15,000 in combined hiring and onboarding expenses per exit. Over a year, 300 employees leave (that’s 15% of 2,000), costing around $4.5 million. By leveraging One Model to unify disparate HR data—engagement metrics, performance reviews, and compensation details—HR spots patterns that predict which employees are at risk of leaving. Targeted interventions reduce that turnover by just 2 percentage points (to 13%). The organization now loses 260 employees instead of 300, saving about $600,000 in direct turnover costs annually. Factor in intangible savings like retained institutional knowledge and smoother team dynamics, and the ROI climbs even higher. A Broader Strategic Value Hard-dollar savings are compelling, but they’re not the only factor to consider. People Analytics also provides strategic benefits that shape long-term competitive advantage: Enhanced Talent Strategy: Predict where the business might face skill shortages in the next year and invest in reskilling or recruiting accordingly. Elevated Employee Experience: Correlate engagement scores with performance metrics to ensure top talent remains challenged and motivated. Data-Driven Culture: Position HR as a credible, analytics-informed function that advises on broader business issues—not just hiring or compliance. By presenting both financial and strategic gains, you offer leadership a more holistic narrative about why this isn’t just another HR tool. It’s a catalyst for organizational resilience and growth. How One Model Simplifies the Pitch One Model helps make the ROI case crystal clear. Through pre-built dashboards, predictive modules, and real-time analytics, you get a transparent view of how workforce changes affect business outcomes. Instead of wrestling with spreadsheets or siloed systems, HR teams can quickly deliver the numbers executives want—be it turnover cost projections or scenario planning for expansion into a new region. This level of clarity can be the difference between a polite nod at budget time and an enthusiastic yes. Before making your case, it helps to prepare your approach to proving People Analytics ROI. Closing the Deal When crafting your final pitch: Quantify the costs of problems like turnover or low engagement. Highlight tangible savings and revenue protection—if you can reduce churn by a few percentage points, how much money is saved? Demonstrate strategic wins like improved succession planning or a stronger employee value proposition. Ultimately, People Analytics isn’t a gamble—it’s an investment that pays dividends through cost containment, culture improvements, and data-driven foresight. With One Model’s track record and clear ROI scenarios, you’ll have the proof points needed to secure the budget and propel HR to the forefront of strategic decision-making. See how measuring the value of People Analytics can be easier than you think. Curious About Your Potential ROI? Explore how One Model can unify your data, clarify the true costs of people challenges, and help you present a business case that resonates with every level of leadership. Request a Demo
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Featured
4 min read
Mar 18, 2025
HR is changing fast, and the need to make data-driven decisions is more urgent than ever. But let’s be real; many HR teams are still stuck in the weeds with endless spreadsheets and complicated reports. Enter One AI Assistant Insights: Your new secret weapon to cut through the noise and make more informed decisions with ease. What is One AI Assistant Insights? One AI Assistant Insights is a powerful new feature within One Model’s One AI Assistant. It automatically analyzes your data and uncovers key trends, outliers, and patterns, faster and more efficiently than humanly possible. No more spending hours digging through complex data or performing statistical research. Insights are generated in seconds, giving HR teams the clarity they need to make informed, timely decisions. Key Capabilities of One AI Assistant Insights Automatic Data Analysis One AI Assistant Insights harnesses the power of statistical expertise to instantly analyze your data. Whether you're tracking employee performance, turnover, or engagement, insights are delivered right away, with no more waiting for reports. With up to five insights generated per chart, you’re likely to uncover compelling trends. By automating data analysis, One AI Assistant Insights helps HR teams save valuable time and focus on strategic priorities. Transparency & Confidence One AI Assistant Insights offers complete transparency, showing exactly how each conclusion is derived. This visibility allows HR teams to make data-driven decisions with confidence, knowing every insight is backed by clear reasoning and calculations. By providing clear explanations, it fosters trust and ensures HR leaders can confidently present their decisions to stakeholders, aligning teams and departments. Ease of Use One AI Assistant Insights makes advanced analytics accessible to everyone, no statistician required. Its user-friendly interface lets HR professionals easily access insights with just a click, enabling quick action within minutes. In HR, timing is crucial, and One AI Assistant Insights delivers real-time information, eliminating the need for long waits on reports. This fast turnaround empowers HR teams to respond swiftly to challenges like turnover or engagement issues, preventing problems from escalating. Tailored Business Recommendations One AI Assistant Insights doesn’t just provide data; it delivers personalized business recommendations tailored to your HR needs. By transforming raw data into actionable insights, it empowers HR teams to drive real business outcomes, whether improving employee retention, optimizing hiring processes, or boosting workforce productivity. This ensures that the insights you receive are not just observations, but strategic, actionable advice. The Future of HR Analytics With One AI Assistant Insights, HR teams no longer have to rely on complex analysis to make important decisions. This feature enables HR professionals to transform their data into real business impact, quickly, accurately, and confidently. One AI Assistant Insights is paving the way for a future where HR teams can make smarter decisions faster, ultimately leading to better outcomes for employees and the organization as a whole. If you’re ready to elevate your HR impact, One AI Assistant Insights is the tool you’ve been waiting for. What to learn more? Request a demo today.
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Featured
6 min read
Mar 13, 2025
Agentic AI is revolutionizing business automation, particularly within HR teams. While the buzz around generative AI and AI agents is growing, there’s one thing we can’t ignore: Without a solid data foundation, these technologies won’t reach their full potential. As businesses increasingly use Agentic AI to automate complex workflows and enhance decision-making, a strong data infrastructure becomes indispensable in driving valuable outcomes. What Makes Agentic AI Different? Unlike traditional chatbots, Agentic AI agents have autonomy. They go beyond answering direct questions or performing isolated tasks; they can navigate multiple actions, planning and executing a sequence to accomplish broader goals. This ability to execute complex tasks makes Agentic AI ideal for HR functions like data analysis, employee engagement, and decision-making. But to achieve these outcomes, these agents need access to well-structured, granular data. Additionally, ensuring that data is securely managed and protected against improper use is critical to maintaining ethical and responsible AI practices. Without this foundation, there is a risk of misuse or breaches that could undermine the integrity of AI systems and the trust placed in them. One Model offers the ideal platform to support this evolution. Data is the fuel behind successful AI operations, and our solution is purpose-built to provide flexibility, security, and advanced data structures that can fuel the complex workflows required by Agentic AI. Unlike other systems that offer only limited, snapshot-style data that can’t keep up with dynamic, agent-driven operations, One Model’s platform handles complexity with ease. This means that businesses using Agentic AI can execute faster, smarter, and more agile HR workflows. The Importance of Data in AI Performance Data is the lifeblood of AI success. AI cannot answer complex questions unless the right data exists in a structured format. For example, if you ask generative AI to help you understand event data that led to a business outcome, but that event data isn't stored and structured properly, AI can't answer that question. This principle holds for Agentic AI as well. The agents powering these workflows need reliable, well-organized data to navigate through tasks effectively. If that data foundation is missing, AI will be limited, unable to answer business-critical questions, and potentially leading to inefficiencies. This highlights why it’s crucial to ensure that data is structured in a way that supports AI operations. One Model’s Unique Approach to Data One Model’s platform stands out by offering an adaptable, highly customizable data infrastructure that allows customers to integrate their unique data, ensuring AI agents can interact with it meaningfully. Our platform handles complexity and provides granular data structures and tools that AI can use with precision, maximizing the agility and impact of Agentic AI workflows. One Model excels in this customization, enabling HR teams to focus on high-value work while AI handles repetitive tasks. With a robust foundation tailored to your business needs, One Model empowers HR professionals to directly access data-driven insights and accelerate decision-making. Empowering HR with Agentic AI The combination of a solid data foundation and the power of Agentic AI is a game-changer for HR departments. It enables HR leaders to unlock faster, data-driven insights without the bottleneck of waiting for technical teams or external support. As we continue to evolve AI capabilities, One Model’s platform is the catalyst that bridges the gap between raw data and actionable insights. With One Model, businesses can harness the full potential of Agentic AI workflows, reducing manual effort, improving efficiency, and making smarter decisions. Our platform’s flexibility, security, and powerful data tools set it apart, ensuring that HR teams are well-equipped for the future of work. Ready to transform your HR operations? The foundation starts with great data. And One Model is here to support you every step of the way. Explore more blogposts about Agentic AI for HR or download our new eBook: Democratizing Data Engineering with Agentic AI How Agentic AI Will Generate Value for Organizations Agentic AI and the Future of One Model Want to learn more? Request a demo today.
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Featured
7 min read
Mar 06, 2025
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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Featured
7 min read
Mar 06, 2025
Imagine it’s Monday morning. Your HR team needs critical insights on employee turnover, but you’re still waiting for IT to validate new data sources. As the hours pass, the data arrives late, and you miss the opportunity to proactively address retention concerns. With Agentic AI, this scenario becomes a thing of the past. Agentic AI can automatically connect and validate new data sources, accelerating the process and delivering actionable insights in real-time. At One Model, we’re revolutionizing HR by automating complex tasks, allowing your team to focus on high-impact decisions. Our solution empowers you to implement your data sources 50% faster through a self service approach, enhancing operational efficiency and delivering real business value when it matters most. What is Agentic AI? Think of Agentic AI as a highly skilled, reliable assistant. It’s someone who anticipates what you need next and handles routine complexities effortlessly, allowing you to focus on strategic decisions. Unlike simple automation tools or chatbots, Agentic AI can make decisions, reason through processes, and carry out workflows with little to no human involvement. The AI agents are capable of interacting with multiple tools, gathering insights, and executing tasks independently. As they work with dynamic data and learn from their actions, Agentic AI adapts and adjusts workflows to better meet your organization’s goals. This autonomy delivers more flexible and powerful solutions to complex problems, ensuring that tasks are completed efficiently, and business outcomes are optimized. Key Insights on Leveraging Agentic AI for HR Transformation To fully harness the potential of Agentic AI in HR, organizations must address three critical areas: Why You Need a Good Data Foundation The power of AI, including Agentic AI, lies in its access to quality data. Without a well-structured data foundation, AI cannot provide valuable insights. One of the primary hurdles organizations face when implementing AI is ensuring that their data is clean, organized, and accessible. AI models, including generative AI, can only answer questions effectively when the right data is available. For instance, without structured event data, it’s impossible for AI to generate meaningful business insights. At One Model, we focus on creating the right data models that can be leveraged by AI agents to drive value and solve complex business problems. How Agentic AI Will Generate Value for Organizations Agentic AI can transform organizations by automating routine, mundane tasks and allowing human workers to focus on more strategic activities. By automating repetitive processes, agents free up HR teams to focus on higher-value work, like improving employee engagement, identifying drivers of performance and retention,and making data-driven decisions. One Model's approach leverages agentic workflows to accelerate tasks like insights generation, enabling faster, more accurate decision-making. This results in significant time savings and improved business outcomes across HR functions. Democratized Data Engineering One of the key advantages of Agentic AI is how it empowers HR teams to achieve more with less. Traditionally, HR teams relied on large data engineering teams, IT departments, or external vendors to handle data transformation and validation. With Agentic AI, One Model's platform brings data expertise directly into HR systems, enabling HR professionals to manage and analyze their data seamlessly without needing constant support from technical teams. As AI agents handle the complex technical work, HR teams can focus on what truly matters, strategic decision-making. This democratization of data engineering means HR leaders can now achieve the level of insight and impact typically reserved for large teams of analysts, boosting efficiency and enabling smarter business outcomes at a fraction of the effort. One Model’s Role in Agentic AI One Model is leading the integration of Agentic AI into HR and People Analytics. By automating tasks like data analysis, we make these processes faster and more impactful. While Agentic AI is still evolving, we’re dedicated to refining its capabilities to ensure effectiveness, security, and ethical integrity. Our focus includes expanding the scope of agentic workflows to give HR teams more autonomy and flexibility. One Model is focused on: Creating real value with AI agents Enhancing AI agents' ability to manage diverse HR data Strengthening data security and privacy Ensuring collaboration between AI agents and HR teams while maintaining transparency and data integrity As our AI agents mature, One Model will continue to enhance their capabilities, helping clients fully leverage Agentic AI while retaining control over their processes. One Model and the Future of Agentic AI in HR As Agentic AI continues to evolve, One Model is positioning itself at the forefront of this revolution in HR technology. By embedding AI agents into our platform, we are helping HR teams move faster, make smarter decisions, and ultimately generate more value for their organizations. As we continue to refine and expand the capabilities of Agentic AI for HR, we remain committed to empowering HR professionals with the tools they need to unlock the full potential of their data. With a solid data foundation, a focus on value-driven automation, and the ability to reduce reliance on external data engineers, One Model is ready to lead the way in the next phase of People Analytics. The future of HR is here, and it’s powered by Agentic AI. Explore more blogposts about Agentic AI or download our new eBook: Democratizing Data Engineering with Agentic AI How Agentic AI Will Generate Value for Organizations Why You Need a Good Data Foundation for Agentic AI Ready to learn more about One Model's Agentic AI? Request a demo today.
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Featured
11 min read
Mar 03, 2025
As the world emerges from years of working from home, some organizations are facing the complex challenge of crafting an effective Return to Office (RTO) strategy. This task is not as simple as flicking a switch; it involves a labyrinth of data-driven decisions that must factor in various aspects such as employee engagement, facilities management, and overall workplace safety. For leaders in finance, facilities, and human resources, the stakes are remarkably high. The pitfalls of a poorly executed RTO strategy can lead to disgruntled employees, high turnover rates, overcrowded facilities, and potential public relations nightmares. Wherever You are in Your RTO Journey, One Model has You Covered Many companies have already implemented RTO, while others are just starting to transition (and some are staying home). Either way, we’re here to help. Whether you’re refining a fully operational office-based schedule or still weighing the pros and cons of remote vs. hybrid approaches, One Model’s platform delivers the insights needed to make informed decisions at every stage. In this intricate puzzle, One Model emerges as a crucial partner, adept at unifying and streamlining the disparate data sources that are essential for tracking time, attendance, badge data, leave of absence, operations data, and more. Our platform is uniquely positioned to tackle the massive data integration challenge, providing you with the actionable insights needed to navigate this complex journey. Want to learn more specifics for your RTO strategy? Join our March 18 webinar: Return to Office - Creating Positive Impact. Why RTO Planning is a Herculean Task The years spent working from home globally have permanently altered the landscape of work, making the transition back to physical offices anything but straightforward. Many reports and research studies indicate that a significant portion of employees prefer the flexibility of remote or hybrid work arrangements. However, many organizations still believe in the benefits of having employees in the office for at least part of the week, citing productivity, company culture, and team cohesion as key considerations. Let’s set that debate aside for a minute. If you are looking to head back, here are some ideas and tips to help you accomplish that goal easily, efficiently, and from a data-informed perspective. With multiple stakeholders involved—finance, facilities, and HR—each bringing their own set of needs and expectations, coordination can be immense. The data needed to effectively manage this transition is often scattered across various systems, making integration a colossal task. Here’s why RTO planning presents such a challenge: Diverse Data Requirements: A successful RTO strategy requires a holistic view of numerous data points, including time and attendance records, badge access logs, facilities usage, and even employee preferences regarding remote work. Cross-Functional Coordination: Aligning different organizational units like HR, finance, and facilities management toward a cohesive plan is inherently complicated without the right tools and data insights. Risk of Misalignment: Ineffective planning can lead to underutilized or overcrowded spaces, lapses in safety protocols, reduced employee satisfaction, and damaging headlines. Complex Data Integration: Consolidating data from various HRIS, time management systems, and facility usage trackers into a unified, actionable format is a monumental task. Real-World Consequences of Poor RTO Execution Countless organizations have stumbled in their attempt to bring back their workforce, suffering severe repercussions. For instance, Apple faced employee backlash when it insisted on returning to a predominantly office-based work structure, resulting in public resignations and bad press. According to an article by The Verge, the company had to deal with resignations from several high-profile employees who cited the rigid RTO policy as a critical factor in their departure. Similarly, companies like JPMorgan Chase encountered hurdles when their initial RTO policies were met with discontent, leading to a re-evaluation of strategies to better accommodate employee demands. Multiple news outlets reported on the tensions this friction created between management and employees, highlighting the delicate balance organizations must strike to avoid such controversies. These examples underscore the potential pitfalls of an inadequate RTO strategy: They not only disrupt operations internally but can also tarnish an organization’s external reputation. The Risks of RTO Missteps The pressure to “get it right” is immense because the downside of errors is particularly stark: Employee Dissatisfaction and Turnover: A mismatched RTO strategy can fuel dissatisfaction, prompting a mass exodus of talent—a risk that’s especially heightened in today’s competitive job market. Operational Inefficiencies: Inaccurate planning can lead to poor space utilization, thereby increasing operational costs and diminishing productivity. Public Relations Challenges: In the age of social media and 24-hour news cycles, a poorly managed return to office can quickly turn into a PR crisis. Health and Safety Concerns: Failing to consider updated health guidelines or employee comfort levels can pose real safety risks. How One Model Powers Data-Driven RTO Success The challenges of RTO planning demand a strategic, analytics-driven approach rather than guesswork. One Model acts as your essential command center for HR, finance, facilities, and operations teams, simplifying how you manage and leverage scattered data, ensuring that decisions are grounded in reality, not assumptions. Integrating Disparate Data Sources for a Unified View One Model excels at consolidating a wide range of data from multiple functions—bringing the data you have, whether it be time-tracking systems, attendance logs, badge usage data, or employee sentiment, into a single cohesive view accessible by all of the teams, but with role-based security to ensure data privacy. This central hub facilitates smooth, informed decision-making processes for finance, facilities, and HR departments, ensuring alignment across all stakeholders. Actionable Insights to Craft an Effective RTO Plan Through advanced analytics and intuitive dashboards, One Model can provide a detailed, accurate picture of your workforce’s dynamics. With customizable dashboards tailored to track key metrics such as attendance trends and space utilization rates, you gain the clarity needed to design policies that meet both business objectives and employee needs. AI-Powered Predictive and Proactive Management Our predictive analytics capabilities can allow you to forecast trends. By understanding potential future issues—whether it's employee turnover risks, facility overcrowding, or engagement declines—you can make proactive adjustments to your RTO strategy, ensuring minimal disruptions and fostering higher levels of employee satisfaction. Comprehensive Support for Successful Execution Beyond just providing a platform, One Model offers expert guidance to help integrate and interpret your workforce data effectively. From automating reporting processes to identifying trends that shape long-term policies, our tools empower you to build a sustainable RTO strategy that adapts to evolving needs. By leveraging One Model’s analytics capabilities, organizations gain the ability to create structured, data-driven RTO plans that align with operational goals while prioritizing employee well-being and retention. The Ripple Effect of a Successful RTO Strategy By effectively leveraging One Model’s capabilities when implementing RTO, you stand to gain numerous organizational benefits: Reduced Risk: Don’t get caught without data when it comes to reporting on progress or making decisions related to RTO. Sensitive topics require careful attention to details. Improved Employee Engagement: A well-crafted RTO strategy acknowledges and respects employee preferences, which can enhance engagement and retention. Optimized Space Usage: Strategic data insights allow for better use of facilities, reducing overhead costs while improving the workplace experience. Bolstered Corporate Image: Successfully managing the return to office showcases your organization as thoughtful and proactive, boosting its reputation internally and externally. The Path Forward with One Model Navigating the complexities of a Return to Office strategy might seem daunting, but with One Model’s comprehensive data integration and analytics platform, you’re equipped to overcome challenges with confidence. Our tools and expertise help you build a strategy that supports business goals, employee satisfaction, and efficient facility usage all at once—no matter where you stand in your RTO journey. Are you ready to take the next step in your RTO journey? Reach out to schedule a demo, and discover how our dashboards and data engineering solutions can illuminate your path forward and ensure a smooth transition back to the office. Together, we can translate complex data into actionable strategies that foster a resilient and adaptable organizational environment. This comprehensive approach not only positions you to avoid common pitfalls but empowers you to make informed, strategic decisions that bolster both your workforce and facilities management. Trust One Model to guide your organization toward a successful RTO strategy that meets the demands of the present and sets the foundation for future operational agility. See how One Model can support your unique approach to RTO. Request a demo of One Model.
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Featured
5 min read
Feb 25, 2025
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. Download our new eBook to explore how adaptable systems are the strategic advantage behind resilient organizations. Want to see it in action?
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Featured
4 min read
Feb 14, 2025
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
3 min read
Feb 12, 2025
The Only Constant in HR is Change Every year, HR leaders face a new workforce crisis. The Great Resignation, hybrid work shifts, talent shortages, return-to-office debates—the list goes on. Just when you think you've got a handle on things, a new challenge emerges, demanding fresh insights and real-time action. The problem? Too many People Analytics platforms look good on the surface but fall apart when real-world complexity hits. The Limitations of Traditional People Analytics Many platforms promise fast answers, but they often come with hidden constraints: Inflexible Data Models: Predefined frameworks make it difficult to align with your organization’s unique needs. Slow to Adapt: When a new workforce issue arises, you’re stuck waiting for vendor updates instead of getting the insights you need. Opaque Processes: If you can’t see how the numbers are built, how can you trust them? These limitations force HR teams to operate reactively, leaving them struggling to provide leadership with clear, accurate workforce insights when they need them most. Why One Model is Different One Model is designed for organizations that need flexibility, transparency, and control over their workforce data. Here’s how: Your Data, Your Way: No black-box models—One Model integrates with your unique data sources, definitions, and business logic. Full Transparency: You can see the underlying data sources and logic used to generate insights, ensuring accuracy and confidence. Flexibility Built-In: When the next workforce crisis hits, you won’t be stuck with rigid, prebuilt reports. One Model’s adaptable framework lets you build the reports you need, when you need them—with our team ready to support custom reporting as required. The Strategic Advantage of a Flexible Partner Organizations using flexible People Analytics solutions gain several advantages: Better Decision-Making: Real-time insights empower HR to take proactive action. Improved Employee Experience: Data-driven strategies help HR teams identify factors that drive engagement, satisfaction, and retention. Optimized Workforce Planning: With visibility into trends and risks, organizations can allocate resources more effectively. By leveraging these benefits, HR leaders can move from reacting to crises to staying ahead of workforce trends. Change is inevitable. Your analytics should be ready for it.
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48 min read
Jan 27, 2025
Insights from Practitioners: Where Are We Heading This Year? The field of People Analytics continues to grow and so does the conference circuit! To help practitioners and HR leaders navigate the crowded landscape, we put together our second annual People Analytics Conference survey for professionals to find out which events they plan to attend in 2025 and which ones they would attend and prioritize if budgets were limitless (one can dream!). Responses from ~120 professionals reaffirm the importance of conferences for advancing knowledge, networking, and shaping the future of People Analytics. Watch our Conferences Webinar to learn about events happening in 2025 Join Richard Rosenow from One Model and Cole Napper from Lightcast as they discuss the most popular People Analytics events this year. Register for OnDemand Webinar Quicklinks: 2025 Events PA Meetup Groups Find One Model in Person Top Conferences Practitioners Are Attending in 2025 The first question we asked respondents was "Which conferences are you planning to attend in 2025". The responses were varied as the conference circuit is diverse, but there were a few standouts! We've rank ordered the top five responses below: 1. Local People Analytics Meetups Attending: 42% of respondents (up from ~33% in 2024) Why It’s Popular: You can’t beat a meetup. This year’s survey proved that meetups have taken hold in the People Analytics space, springing up across the US from their start in NYC to the Bay Area to many other cities like Columbus and Salt Lake City. Globally there’s also been an explosion of new events from Sweden to Brisbane and Nigeria to Latin America. These grassroots gatherings are the heart of community-driven professional development. With minimal costs and maximum networking opportunities, meetups are perfect for both early career professionals looking to enter the field and seasoned practitioners looking to share experiences and ideas. About This Conference: While each meetup is run locally by a different volunteer team, People Analytics meetups generally offer a community feel and collaborative space for People Analytics professionals to exchange practical insights. They’re especially beneficial for those who may not have the budget or time to attend larger, national conferences. Communities in NYC and the Bay Area lead the pack in maturity and tenure (10+ years running for some!), but many other cities have run with the format and meetups are spreading rapidly to other cities worldwide. Repeat or New: Local meetups saw a surge in attendance on our 2024 survey and continued that growth in 2025. More info: Make sure to visit SPA’s meetup list to see if there’s a meetup in a city near you. And if you can’t find one, reach out to me (Richard) and I’d be happy to connect you with some friends in the area. People Analytics people are everywhere now. 2. SIOP 2025 - Denver Attending: ~30% of respondents Why It’s Popular: In terms of “official” conferences, the Society for Industrial and Organizational Psychology (SIOP) annual conference continues to draw the attention of respondents to this survey from People Analytics professionals with its robust mix of academic research and practical insights. Known for fostering connections between IO Psychology and People Analytics, this conference is a must-attend for leaders seeking evidence-based approaches to workforce challenges. About This Conference: Scheduled this year from 4/2 to 4/5 in Denver, Colorado, SIOP is a massive gathering of over 5,000 attendees. Sessions span diverse topics, from AI ethics to leadership assessments. The format includes as many as 10 concurrent sessions per hour, along with posters to see, masterclasses to attend, and people to meet – all of which creates an atmosphere of exciting FOMO. For People Analytics professionals, SIOP bridges academic rigor and workplace application, making it an unparalleled learning experience. Important note:Everyone can attend SIOP! There’s a rumor that it is only for IO Psychology PhDs and that’s just not the case. I’ve got an MBA background and have attended for two years running now (and they haven’t thrown me out yet). I can’t recommend this conference enough to anyone interested in deep conversations about applications of analytics to the workplace. Repeat or New: SIOP was the most attended conference in 2024, and its popularity shows no signs of waning. More info: SIOP Annual Conference Bonus info: My 2023 SIOP travelogue 3. People Analytics World NYC & People Analytics World London Attending: ~26% of respondents Why It’s Popular: People Analytics World (PAW) has been the premier stage in Europe for People Analytics for ten years running and, with groundfall in NYC in 2024, the event has officially gone global. This event is the true home for practitioners in People Analytics and has the feel of a family reunion and a team brainstorm all mixed together. It’s the perfect blend of networking, learning, and practical applications for People Analytics. About This Conference: The events are scheduled for London (4/29 to 4/30) and New York City (10/15 to 10/16). The PAW conference is run by the Tucana team (who additionally run some fantastic SWP events globally). People Analytics World NYC and London both combine global thought leadership with practitioner-led insights. Known for their deep dives into storytelling with data, scaling analytics, and cross-functional collaboration, this event caters to mature analytics teams who want to stay in touch with what’s going on across the industry. Repeat or New: PAW London had a great showing in last year’s event survey and the NYC conference debuted in late 2024, building on Tucana’s strong legacy with its London event. More info: People Analytics World 4. TALREOS Chicago Attending: 21% of respondents Why It’s Popular: Cited as a “hidden gem” in 2024, TALREOS Chicago (Talent Analytics Leadership Roundtable & Economic Opportunity Summit) had a breakout year with People Analytics professionals. Praised for its round-table “Chatham House Rule” approach to dialogue and strong controls over vendor involvement, this conference has quietly become a must-attend destination for People Analytics leaders of advanced teams who want to meet peers and dive deep into analytics and the future of work. About This Conference: Scheduled for June 4-6 in Chicago, TALREOS has been running for over 10 years out of Northwestern as part of the Workforce Science Project. It offers a balance of practical workshops and thought-provoking keynotes. The smaller size and invite-only nature of the event fosters meaningful networking and provides attendees with actionable frameworks they can implement immediately. Repeat or New: TALREOS appeared on the radar in last year's survey and has quickly gained momentum. With more practitioners prioritizing it in 2025, it’s transitioning from a “hidden gem” to a recognized pillar of the People Analytics community. More info: TALREOS 5. Wharton People Analytics Conference Attending: 17% of respondents Why It’s Popular: Led by Matthew Bidwell and a team of renowned academics, the Wharton People Analytics Conference offers a unique blend of strategic insights for HR leaders and groundbreaking academic contributions. Consistently ranked as a favorite among People Analytics professionals, the conference is known for its rigorous content and engaging sessions. A standout feature is the annual People Analytics Case Competition, which continues to be a highlight for participants and attendees alike. About This Conference: Scheduled for April 10-11, 2025 at the University of Pennsylvania, this two-day event features a diverse lineup of speakers, including experts from academia and industry. The conference offers sessions on the latest advances in People Analytics, complemented by networking opportunities with students, academics, and industry professionals. Highlights include keynote addresses, panel discussions, and competitions that showcase innovative research and applications in the field. Repeat or New: Wharton was a top wish-list event in 2024, and this year’s data shows its appeal has only grown. I hope to see you there this year! More info: Wharton People Analytics What’s on the Wish List? Even when budgets are tight, practitioners still dream big. Here are the events topping their aspirational lists (where they wish they could attend if budget wasn’t an issue): 1. People Analytics World NYC & People Analytics World London Wish List Interest: 31% of respondents Why It’s Desired: As covered above, People Analytics World (PAW) is a tremendous event series. After a sold-out and breakout year in New York City, PAW has swept up the People Analytics space with excitement. I was delighted to see this one top the charts this year and I hope all of you who wanted to attend are able to get your budget approved! About This Conference: The London event is scheduled for 4/29 to 4/30 and New York City from 10/15 to 10/16. The PAW conference is run by the Tucana team (who additionally run some fantastic SWP events globally). People Analytics World NYC and London both combine global thought leadership with practitioner-led insights. Known for their deep dives into storytelling with data, scaling analytics, and cross-functional collaboration, this event caters to mature analytics teams who want to stay in touch with what’s going on across the industry. Repeat or New?: This conference was a top wish-list item in 2024 and has proven to be highly sought-after in 2025. More info: People Analytics World 2. Wharton People Analytics Conference Wish List Interest: 27% of respondents Why It’s Desired: Covered above as well! For the second year running, Wharton appears prominently on the wish list for People Analytics leaders. The history and long-running presence of this conference has firmly established it in the People Analytics world as a pillar of the conference circuit. About This Conference: Scheduled for April 10-11, 2025 at the University of Pennsylvania, this two-day event features a diverse lineup of speakers, including experts from academia and industry. The conference offers sessions on the latest advances in people analytics, complemented by networking opportunities with students, academics, and industry professionals. Highlights include keynote addresses, panel discussions, and competitions that showcase innovative research and applications in the field. Repeat or New?: A repeat favorite from 2024, Wharton remains one of the most highly sought after conferences in the field. More info: Wharton People Analytics 3. HR Technology Conference Wish List Interest: 19% of respondents Why It’s Desired: The HR Technology Conference (HR Tech) stands out as a pinnacle event for those looking to stay ahead in the rapidly evolving realm of HR tech. From AI-driven talent management solutions to cutting-edge analytics platforms, the showcase of emerging trends draws both tech-savvy HR leaders and People Analytics professionals alike. It’s a one-stop-shop for learning everything you need to know to stay on top of HR technology advances or, if you’re feeling bold, to purchase your entire HR Tech stack. About This Conference: Scheduled for September 16-18, 2025 at Mandalay Bay in Las Vegas, the HR Technology Conference offers hands-on access to the latest technologies from over 500 leading and emerging providers. With over 200 sessions, attendees can discover industry trends and gain actionable strategies to leverage technology for success in various HR functions. The conference also provides numerous networking opportunities with peers and industry experts. Repeat or New?: HR Tech featured as a must-attend conference in the 2024 survey and repeats here in 2025 More info: HR Technology Conference 4. HR Analytics Summit London Wish List Interest: 17% of respondents. Why It’s Desired: The HR Analytics Summit London has become a pivotal event for professionals eager to harness the power of data in HR. With a focus on practical applications of People Analytics, the summit addresses critical areas such as employee engagement, HR operations, and the future of work. Attendees are drawn to its comprehensive agenda, featuring inspiring keynotes, interactive panels, and deep-dive workshops led by industry thought leaders. About This Conference: Scheduled for September 4, 2025 in London, the HR Analytics Summit offers a turbo-charged day of learning and networking. The conference brings together over 300 HR and workforce leaders from a variety of industries with 20+ expert speakers. Sessions delve into innovative approaches to workforce analytics, empowering strategic decision-making processes to tackle pressing issues in human capital management. The event also emphasizes the ethical use of AI in HR, balancing data-driven insights with human empathy. Notably, 5% of all ticket sales are donated to the charity Mind, reflecting the summit's commitment to mental well-being. Repeat or New?: A newcomer to the list for 2025 and an exciting one to watch going forward! Website: HR Analytics Summit London 5. Insight222 Global Executive Retreat Wish List Interest: 15% of respondents Why It’s Desired: The Insight222 Global Executive Retreat is highly coveted among HR executives and People Analytics leaders for its exclusive, invite-only format. Offering meticulously curated sessions, the retreat offers deep dives into strategic topics, fostering an environment where executives can "Think, Reflect, and Plan" their future initiatives. Participants value the opportunity to engage with top-tier business speakers and peers in a distraction-free setting, enhancing their leadership journey. About This Conference: The retreat is held annually at spectacular venues, such as the historic Duin & Kruidberg estate near Amsterdam. It features a select number of in-depth discussions and workshop-style activities led by world-class speakers. The 2024 theme, "The Changing Role of the People Analytics Executive," focused on the evolving influence of People Analytics leaders within organizations (2025 theme TBD). Attendees gain strategic insights, engage in peer learning, and develop actionable plans to drive value in their roles. Repeat or New: A consistent favorite on the wish list for many People Analytics leaders, the Insight222 Global Executive Retreat continues to attract senior leaders seeking a premier, immersive experience in the People Analytics domain. More info: Insight222 Global Executive Retreat And these are just the top 5 from each category! The survey was close and there are MANY more incredible events in our space. Please jump to the end of the blog to see the full list of conferences included in the study. Each one represents incredible community, sessions, and exciting ideas and experiences. New (and Noteworthy) Conferences for 2025 As the People Analytics conference landscape continues to expand, 2025 introduces some exciting developments, from fresh additions to reimagined formats. These events stand out as either brand-new opportunities or evolving platforms that are reshaping the way HR and People Analytics professionals engage with the community. RedThread Research's ELEVATE Conference New for 2025: This year marks the debut of ELEVATE, a highly anticipated conference led by industry thought leaders Stacia Garr, Dani Johnson, and the team at RedThread Research. Known for their influential insights and industry research on talent, learning, and People Analytics, this event is going to be incredible. Why Attend: ELEVATE aims to deliver an intimate, invite-only, high-value experience by bringing together industry thought leaders, actionable insights, and forward-thinking strategies with an emphasis on a Director+ audience. About the Conference: Scheduled for June 17–19, 2025 at Snowbird, Utah, Elevate promises a mix of interactive sessions, collaborative problem-solving, and exclusive research findings. The conference is deliberately designed to spark innovation and build stronger bridges between data, decision-making, and people. Learn More: Find details and join the waitlist HERE! SIOP Leading Edge Consortium (LEC): People Analytics New for 2025: Chaired by Cole Napper and Stephanie Murphy, this year’s SIOP LEC focuses on People Analytics, providing a deep dive into the strategic and operational challenges facing today’s analytics teams. Why Attend: The LEC’s smaller, specialized format encourages targeted conversations and emphasizes practical applications. Attendees can expect to engage directly with experts, participate in robust discussions, and leave with actionable strategies tailored to their unique challenges. About the Conference: Scheduled for Oct 23-24th in Atlanta, Georgia the LEC combines SIOP’s academic rigor with emerging trends in People Analytics. The event brings together researchers and practitioners for a collaborative exchange of ideas, making it an essential gathering for teams looking to refine their approaches and elevate their impact. Learn More: Keep an eye on SIOP LEC Cole Napper shares, "The SIOP Leading Edge Consortium is focused on People Analytics this year. It has a stellar lineup of speakers (soon to be revealed), and is welcome to I/O psychologists and non-I/O psychologists alike. It should be one of the most practical, scientific yet fun conferences to date - chaired by myself and Stephanie Murphy." UT Austin Voice Conference 2025 New for 2025: Making its debut this year, the Employee Voice Conference at UT Austin is an exclusive, invite-only gathering led by Ethan Burris. Hosted at the McCombs School of Business, this inaugural event brings together leading academics, VP Talent/CHROs, and People Analytics leaders to explore cutting-edge employee voice research and practice. Why Attend: Compared to other conferences, there is significant focus on building bridges across boundaries. Attendees will participate in intimate roundtables, thought-provoking discussions, and carefully curated sessions designed to foster cross-disciplinary collaboration. With a highly targeted attendee list, participants gain unparalleled access to peers and thought leaders who are driving innovation in employee voice strategies. About the Conference: Scheduled for April 17–18, 2025 in Austin, Texas, the event focuses on understanding methods and advances in employee feedback and innovations around employee listening. A joint-effort founding team of academics and leaders from across the space have come together to innovate within this conference ensuring actionable insights and meaningful relationship-building opportunities. Learn More: Follow announcements from UT Austin’s McCombs School of Business or reach out to Ethan Burris for updates. A few seats are remaining, so if you have a passion for employee listening, be sure to reach out! These events offer unique opportunities to gain fresh insights, connect with leading thinkers, and stay ahead in the ever-evolving world of People Analytics. Whether you’re attending for the second iteration of a rising star or diving into a brand-new experience, these conferences are set to make a lasting impression in 2025. Insights from Practitioners: Enhancing the Conference Experience This year, we also included two new questions in our survey to uncover deeper insights into why professionals attend conferences and how organizers can improve the overall experience. 1. Why do professionals attend conferences? 2. What do you wish conference organizers knew (from practitioners) Here’s what we learned from the responses: Why Do Professionals Attend Conferences? Networking emerged as the dominant reason for attending conferences, with respondents emphasizing the value of connecting with peers, exchanging ideas, and learning from others in the People Analytics community. Beyond that, learning and staying on top of industry trends were also top priorities. Here’s a breakdown of the common themes: Networking and Collaboration: Many professionals highlighted the importance of meeting others in the field to exchange ideas, build relationships, and discover potential collaborators. Conferences provide unique opportunities to engage with peers facing similar challenges and working on similar initiatives. Learning and Staying Current: Respondents consistently mentioned the need to stay informed about the latest trends, research, and technologies in People Analytics. Many seek practical solutions, detailed use cases, and innovative ideas to bring back to their organizations. Sharing Knowledge and Giving Back: Several practitioners also view conferences as a platform to share their expertise, present their work, and contribute to the growth of the field. Professional Growth and Inspiration: The excitement of gaining new perspectives and sparking fresh ideas was another frequently cited reason. Attendees look for moments of inspiration that push their thinking and help them grow professionally. Discovering Emerging Tech and Best Practices: Keeping an eye on emerging technologies, methodologies, and strategies remains a key goal for many attendees. Key Takeaway: Conferences are not just about presentations—they are critical hubs for community building, knowledge sharing, and inspiration. Make sure to build in time for networking sessions or conference organized networking events. What Practitioners Wish Conference Organizers Knew When asked how conferences could improve, attendees provided thoughtful and candid feedback. These insights highlight areas where organizers can refine the experience to better serve the needs of the People Analytics Community Here’s a breakdown of the common themes: Networking is Key Similar to above, respondents want more intentional, well-designed networking opportunities. Suggestions included planned 1:1 matchups, structured group discussions, and color-coded badges to help identify peers with similar roles or goals. Longer lunch breaks, dedicated networking periods, and informal social activities were also suggested to facilitate meaningful connections. Balance Content with Connection Many participants expressed a desire for fewer sessions and more opportunities to connect with others. "Less content and more connection" was a recurring theme. Hands-on interactive sessions and workshops were highly valued over traditional panels or theory-heavy presentations. Accessibility and Inclusivity Virtual attendance options were a popular request, with many noting the value of hybrid formats for professionals with limited budgets or travel constraints. Suggestions included offering livestreams, post-conference breakout access, or on-demand recordings at a reduced cost. Respondents also emphasized the need for conferences to cater to neurodivergent attendees, introverts, and individuals from underrepresented groups through thoughtful design, diverse speakers, and accessible content. Thoughtful Vendor Participation A common frustration was the prevalence of vendor-led presentations that felt like sales pitches. Attendees vastly prefer sessions, even from vendors, that focus on sharing insights, research findings, and practical applications rather than direct product promotion. Demonstrations that show rather than tell, along with panels featuring practitioner voices, were seen as more effective. Content Design and Variety Respondents want more practical case studies, detailed use cases, and real-world examples, especially from industries like manufacturing and low-margin businesses. There was also a desire for broader representation in speakers, both in terms of backgrounds and company sizes, to better reflect the diversity of the field. Pre-Conference Resources Pre-shared attendee lists, session itineraries, and other preparatory materials were highlighted as valuable tools for more intentional networking and better conference planning. Acknowledge Real-World Constraints Budgeting challenges were frequently mentioned, with many participants noting that their organizations approve conference budgets the year prior. Providing earlier information on dates, costs, and speakers would help attendees secure funding. Some respondents also mentioned a sense of "conference fatigue," suggesting that organizers consider consolidating events or ensuring differentiation in their offerings. Key Takeaway: Conference organizers have an opportunity to create more inclusive, impactful, and engaging experiences by prioritizing networking, balancing content, and addressing accessibility and budget challenges. Conclusion: Building a Better Conference Experience The feedback from this year’s survey offers a roadmap for conference organizers looking to elevate their events. By focusing on community building, providing diverse and practical content, and addressing accessibility concerns, conferences can better serve the evolving needs of People Analytics professionals. For practitioners, these insights reinforce the importance of carefully selecting events that align with their goals—whether it’s connecting with peers, learning about the latest innovations, or gaining inspiration for new challenges. Takeaways for 2025 The People Analytics conference circuit is more dynamic than ever. Whether you’re a seasoned leader or a practitioner just starting your analytics journey, there’s a conference tailored to your needs. From the academic rigor of Wharton to the accessibility of local meetups, these events offer a mix of inspiration, networking, and actionable insights. If your budget is tight, prioritize meetups and virtual sessions; if you’re looking for deeper insights, conferences like SIOP and People Analytics World are worth the investment. What’s Next? If you’re attending any of these conferences, we’d love to connect and hear about your experiences. And if you’re still deciding which events to prioritize, I hope this guide can be your roadmap for 2025. And if you’re on the fence, reach out to me (Richard) and let me know what you’re thinking and hoping to achieve! I’d be happy to weigh in with experiences from the field. See you out there! Connect with us in person in 2025? Tell us which events you plan to attend and let's meet up! What to stay in the loop? Follow One Model on LinkedIn Follow Richard on LinkedIn Events list from Survey (non-vendor specific events that were included in the survey or mentioned in survey results) People Analytics Summit Toronto - February 26-27, 2025 - Toronto, Canada - Link HR Data Analytics and AI Summit - March 4, 2025 - Atlanta - Link HR West 2025 - March 11-12, 2025 - Oakland Marriott City Center, Oakland, CA, USA - Link Transform US - Las Vegas - March 17-19 - Link SHRM Talent Conference & Expo 2025 - March 24-26, 2025 - Music City Center, Nashville, TN, USA - Link SIOP 2025 Annual Conference - April 2-5, 2025 - Denver, CO, USA - Link Wharton People Analytics Conference - April 10-11, 2025 - Philadelphia, PA, USA - Link UT Austin Voice Conference - April 17–18 - Austin, TX, USA - Link TBD People Analytics World - London (Tucana) - April 29-30, 2025 - London, UK - Link Unleash America - May 6-8, 2025 - Las Vegas, NV, USA - Link TALREOS Chicago - June 4-6 - Chicago, IL, USA - Link RedThread Research: ELEVATE - June 17-19 - Snowbird, Utah, USA - Link People Analytics Exchange (Minneapolis) - June 24-25, 2025 - Minneapolis, MN, USA - Link SHRM Annual Conference & Expo 2025 - June 22-25, 2025 - San Diego - Link AHRI National Conference - August 19-21, 2025 - Sydney, Australia - Link HR Analytics Summit London - September 4, 2025 - London, UK - Link HR Technology Conference (Las Vegas) - September 16-18, 2025 - Mandalay Bay, Las Vegas, NV, USA - Link HR L&D Tech Fest - September 22-23, 2025 - Sydney, Australia - Link Gartner ReimagineHR Conference (London) - October 7-9, 2025 - London, UK - Link People Analytics World - NYC (Tucana) - October 15-16, 2025 - New York City, NY, USA - Link Unleash World (Paris) - October 21-22, 2025 - Paris, France - Link SIOP Leading Edge Consortium: People Analytics - October 23-24, 2025 - Atlanta, GA - Link Gartner ReimagineHR Conference (Orlando) - October 27-29, 2025 - Orlando, FL, USA - Link Nordic People Analytics Summit - November 2025 (Exact dates TBA) - Stockholm, Sweden - Link Gartner ReimagineHR Conference (Sydney) - November 17-18, 2025 - Sydney, Australia - Link Dates TBD: CIPD People Analytics - UK - TBD - London, UK - Link Insight222 Global Executive Retreat - TBD - TBD - Link Learning Forum People Analytics Council - TBD - TBD - Link Which events did we miss? Send Richard an email at Richard.Rosenow@onemodel.co (Note: we do not include single vendor (hosted by one vendor) or tech sales events in this review of conferences) 2025 People Analytics Meetup Groups And now time for the meetups! These meetups happen frequently throughout the year, so the best way to be involved and stay involved is to connect with their local site / meetup / LinkedIn group. Where we can, we’ve included some details about how to connect and when there was not a site yet available, we’ve added in local organizers. Definitive list from the Society for People Analytics: https://societyforpeopleanalytics.org/meet-ups Brisbane (AU): (Link to event) New York: https://lnkd.in/gbfu_Mjc (Jeremy Shapiro / Stela Lupushor) Bay Area: https://lnkd.in/gnrgRBnH (Annika Schultz / Mariah Norell) Chicago: https://lnkd.in/ghgc3EDb - (Chris Broderick) Philadelphia: https://lnkd.in/g-bWmX5y - (Fiona Jamison, Ph.D.) Pittsburgh: https://lnkd.in/eCdP7KFC (Ken Clar / Richard Rosenow) Minneapolis: https://lnkd.in/eS2aUH3W (Stephanie Murphy, Ph.D. / Mark H. Hanson) Seattle: Bennet Voorhees / Marcus Baker / Philip Arkcoll Denver: Kelsie L. Colley, M.S. ABD / Zach Williams / Gabriela Mauch Boston: Hallie Bregman, PhD / Noel Perez, PMP Dallas: Jordan Hartley, MS-HRM / Cole Napper Austin: Ethan Burris / Roxanne Laczo, PhD Houston: Amy Frost Stevenson, PhD / Jugnu Sharma, SHRM-CP Atlanta: Sue Lam Nashville: Dan George Orlando: James Gallman / Danielle Rumble, MBA Omaha: Justin Arends Salt Lake City: Willis Jensen Toronto: Danielle Bushen / Konstantin Tskhay, PhD Washington DC: Rewina Bedemariam Portland: Rosanna Van Horn
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6 min read
Jan 10, 2025
When it comes to leveraging workforce data for strategic decision-making, organizations need tools that go beyond simple reporting. People Analytics Platforms collect, clean, and analyze data, deliver valuable insights, use predictive analytics, and integrate with other business systems. While major Human Capital Management platforms like SuccessFactors, Workday, and Oracle offer analytics capabilities, One Model stands out as a critical addition to these built-in solutions. Here’s why: Achieve More Accurate Insights with Effortless Data Integration The ability to integrate diverse data sources effortlessly is critical for gaining accurate insights. By pulling real-time data from multiple sources, organizations can make more informed decisions that directly impact their overall strategy. Workday Prism: Workday Prism aims to provide a unified analytics layer but has challenges with integrating data from third-party sources. The platform requires users to publish individual data sets, and the data preparation logic is often inconsistent. This can lead to confusion and inefficiencies, specifically for organizations that need to bring together diverse data sources. SuccessFactors Workforce Analytics: One of the major drawbacks of SAP SuccessFactors WFA is its limited data integration capabilities. While it offers workforce analytics, it primarily only works within the confines of specific SuccessFactors modules. Integrating external data from other systems or non-HR business data is either not possible or costly and cumbersome. Oracle Fusion: Similarly, Oracle’s analytics solution is tightly coupled with its HCM platform. While it provides robust tools for managing HR data, the system lacks flexibility with integrating with non-Oracle systems. This creates data silos. Additionally, Oracle’s analytics solution comes with hidden costs, such as fees for custom analytics, data storage, and specialized expertise. One Model: One Model excels at integrating all of your HR data and external business data into a cohesive platform. With pre-built connectors to popular systems like Workday, SuccessFactors, and Oracle, as well as the ability to integrate non-HR data, One Model creates a unified data warehouse that provides deeper insights across all business functions. This flexibility ensures businesses have a complete, real-time view of their workforce and business performance. Quick Time to Value for Faster Decision-Making When speed is critical for decision-making, long implementation timelines can hinder an organization’s ability to take action. One Model’s quick deployment ensures that companies don’t have to wait months or years to start seeing value from their analytics investments. Workday Prism: Workday Prism involves an in-depth implementation and the need for specialized BI developers to maintain the system after implementation. SuccessFactors Workforce Analytics: The time to value with SuccessFactors Workforce Analytics can range from 12 to 24 months, as the system requires significant customization, data preparation, and integration efforts. Oracle Fusion: Similarly, Oracle Fusion's analytics solution requires extensive customization, and companies often face long implementation times before seeing value. One Model: One Model has the ability to deliver actionable insights in as little as 6-12 weeks. With a user-friendly platform and expert support, One Model allows organizations to experience value faster, making it the ideal choice for businesses that need rapid insights to drive strategic decision-making. With robust role-based security and pre-built, customizable HR metrics, users can access the insights they need without waiting for IT or BI teams. Empower HR Teams with a Tailored Solution to Align with Evolving Goals HR teams need to be agile and adapt their analytics strategies to meet shifting organizational priorities. Solutions that allow for easy customization can empower HR professionals to act quickly and align their insights with business needs. Workday Prism: Customizing reports and data models in Prism requires the involvement of skilled BI developers, leading to delays in decision-making. SuccessFactors Workforce Analytics: While SuccessFactors offers basic reporting capabilities, customizing reports and metrics is time-consuming and requires significant technical expertise, slowing down the ability to generate actionable insights. Additionally, the platform's rigid data architecture can make it challenging to create highly customized solutions that meet unique organizational requirements. Oracle Fusion: Oracle Fusion’s self-service capabilities are limited, and users need specialized skills to customize reports and analytics models. In contrast, One Model stands out for its ease of customization. One Model is designed to allow users to quickly create and customize dashboards and metrics in minutes. By offering a platform that simplifies customization, One Model enables HR teams to take full ownership of their analytics and ensure they are always aligned with evolving organizational goals. Understand Workforce Behaviors and Drive Efficiency with Predictive Analytics and Machine Learning As organizations continue to rely on data to shape their strategies, predictive analytics and machine learning are becoming increasingly vital for understanding and forecasting workforce behaviors. Solutions with built-in AI capabilities can provide deep insights into areas like turnover, performance, and employee satisfaction. Workday Prism: Prism offers basic reporting and visualization, but predictive capabilities are limited, and the platform is not optimized for machine learning applications. SuccessFactors Workforce Analytics: SuccessFactors lacks built-in machine learning and predictive analytics, leaving HR leaders with limited capabilities to forecast trends like attrition and performance. Oracle Fusion: Oracle’s analytics tools fall short in providing integrated predictive insights. Oracle offers a visualization layer, but predictive modelling requires significant customization. One Model: One Model sets itself apart with its powerful machine learning engine, One AI, which enables predictive insights across key HR metrics like attrition and performance. One Model offers complete transparency into its AI models, ensuring that businesses can maintain control over algorithms and stay compliant with legal and ethical standards. Why One Model is the Perfect Partner for Enhancing Your Existing HCM Solution When compared to Workday, SuccessFactors, and Oracle Fusion’s built-in analytics modules, One Model stands out as the most flexible, cost effective, and customizable solution for People Analytics. With faster time to value, data integration across multiple HR and business systems, and advanced predictive analytics, One Model helps businesses use their people data as a strategic asset. For organizations seeking fast, effective data-informed decision-making, One Model is the clear choice for enhancing your existing HCM solution.
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6 min read
Dec 18, 2024
The integration of artificial intelligence into HR practices has begun to transform how companies engage, support, and optimize their workforce. By adopting a holistic, employee-centric approach to AI deployment, organizations and People Analytics teams can foster a culture of innovation, boost productivity, and ultimately create a workplace where employees are empowered to thrive. A Strategic and Employee-Focused Approach to AI Integration Implementing AI within an HR organization demands a strategic and well-orchestrated approach. A key insight from companies successfully embracing AI is to prioritize change management for AI and employee engagement. Rolling out AI solutions shouldn’t feel like a top-down directive imposed on People Analytics teams but rather a collaborative, bottoms-up process that centers on empowering employees. When People Analytics leaders take a gradual, people-first approach, they can ease anxieties associated with AI adoption. The fear that AI will replace jobs has been pervasive, and addressing this concern upfront is essential for cultivating trust. Leaders need to clearly communicate that AI is a tool to enhance, not replace, human effort. This message must resonate throughout the HR department and company, helping employees view AI as a career-enabling partner rather than a threat. Celebrating AI Adoption as a Driver of Change Measuring initial AI adoption within the HR department is a critical first step. Successful teams have shown that fostering a culture where AI usage is celebrated can accelerate adoption. One effective strategy is creating department-wide channels where People Analytics teams can share their experiences using AI and the benefits it has brought to their workflows. Highlighting these success stories not only reinforces positive engagement but also builds momentum as teams see real examples of AI delivering tangible value. Recognition programs that reward early adopters can further stimulate interest and promote active participation. This step aligns with AI change management best practices, which emphasize that for organization transformation with AI to take hold, the individuals most affected must feel supported and valued. Key Areas of AI Implementation in HR Leading organizations have deployed AI across several HR functions to drive efficiency and enhance decision-making. Below are examples of impactful implementations of AI in HR departments: 1. Recruitment and Interview Processes Integrating AI in recruitment has revolutionized how interviews are conducted and evaluated. AI-powered tools can assist hiring managers by recording interviews, generating time-stamped notes, and linking key interview moments to questions asked. This capability alone can save managers substantial time—up to 30–40 minutes per interview—by automating note-taking and providing instant access to video highlights. 2. HR Chatbots for Employee Services Advanced, AI-powered HR chatbots are streamlining routine tasks by handling knowledge base inquiries and processing transactional requests. Integrated within platforms like Slack, these bots can facilitate actions such as submitting time-off requests or accessing benefits information, freeing up HR teams to focus on more strategic work. This integration also simplifies data access and enhances the overall employee experience. 3. AI-Enhanced Learning and Development AI’s application in L&D involves deploying intelligent tools that help curate learning content, suggest development paths, and assist employees with feedback and coaching tailored to organizational competencies and values. The introduction of AI coaches for career growth discussions or navigating difficult conversations empowers employees with customized guidance that aligns with company culture and goals. 4. Democratize People Data Across Management Teams Team leaders at any level of the organization are better leaders when they better understand the workforce under their care. Business and People Analytics teams are instrumental in that goal through building dashboards and answering big complex questions. But what happens when there are too many ad hoc manager questions across the organization for the people analytics teams to answer? Originally, that meant either questions went unanswered or people analytics teams were pulled from larger, more impactful projects to help. With solutions, like One Model, that is no longer an issue. One AI Assistant is helping companies today by giving managers the ability to ask questions on the people data they can access. HR analytics teams can have confidence in the results of One AI Assistant because it provides clear explainability and transparency of outputs. Laying the Foundation for Long-Term Success Sustainable AI integration in HR and People Analytics is not just about deploying new technologies but about embedding AI into the company culture. From day one, leaders need to ensure that the tools are intuitive, accessible, and aligned with the company’s core values. Building trust in AI begins with demonstrating how these tools support employees' roles, making HR tasks less burdensome and enabling teams to tackle more strategic initiatives. HR leaders should collaborate with engineering and data teams to customize AI solutions that fit specific organizational needs. This might involve developing unique AI assistants or prompts that streamline operations and ensure consistency across processes. For instance, internal tools that summarize interview notes or assist with coding can enhance productivity without fundamentally altering job responsibilities. Creating a Legacy of Innovation and Engagement The ultimate goal of integrating AI into HR is not just to boost efficiency but to foster an environment where employees feel excited about leveraging cutting-edge tools. Organizations that prioritize a culture of continuous learning and innovation will find that their employees are more engaged, adaptive, and capable of driving the company forward. By recognizing the transformative potential of AI and implementing it thoughtfully, HR and People Analytics leaders can elevate their people strategy with AI and position their organization as a leader in the future of work. Learn more about One AI Assistant If you would like to learn more about One AI Assistant and how other One AI tools can help your team empower your entire company with business-driving insights into their workforce, reach out. Your Data. Real Answers.
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Featured
6 min read
Dec 18, 2024
The data doesn’t lie—these are the One Model resources your peers keep coming back to. We’ve rounded up our top three most-downloaded whitepapers, plus a bonus newcomer that’s already making waves. Whether you’re searching for fresh strategies or sharpening your existing skills, these resources have proven to be invaluable to People Analytics professionals like yourself. If you haven’t explored them yet, now’s the time to join the conversation. 1. Why Data-Informed Storytelling is the Future of HR As the field of People Analytics becomes increasingly data-savvy, this whitepaper has resonated with readers across industries, earning its place as our most downloaded resource. If you’ve struggled to tell a meaningful narrative around your data and its objective but sometimes hidden insights, data storytelling is the missing link. Stories, human anecdotes, and yes - even emotion - can help bring your data to life. It’s a powerful combo that can truly drive action for organizations. But how do you tell a meaningful data story? And why is it such a valuable skill for today’s HR teams? Download this eBook today to learn: The evolution of storytelling in HR How to craft data-informed HR stories Examples of impactful data-informed HR stories How to tell better data stories with One Model Learn how to turn raw data into compelling narratives that engage stakeholders and drive better decisions. 2. People Analytics 101 Coming in a close second in popularity, this fundamental guide is the perfect entry point for getting started in People Analytics. But even seasoned HR professionals sometimes wonder what to prioritize when establishing a strong People Analytics foundation. This content is meant for everyone, from CHROs to HR leaders looking to upskill, providing foundational knowledge that aligns your people data with your organization’s goals. Download this eBook today to learn: What People Analytics is, and why it's important How to prepare your organization for People Analytics Why employee attrition is a good starting point Steps for completing your own People Analytics projects Discover how to tailor People Analytics to your organization’s unique needs. 3. Measuring the Value of People Analytics Prove the ROI of your efforts with this comprehensive, tactical guide to measuring the tangible impact of People Analytics. A must-read for leaders seeking to align HR initiatives with business outcomes or make a business case for People Analytics. Download this whitepaper today to learn: How to redefine and measure the value of People Analytics beyond traditional ROI metrics. The three levels of People Analytics impact—direct, indirect, and induced—and how they drive better talent decisions. A practical formula for assessing the value of analytics deliverables and prioritizing resources effectively. Strategies for scaling People Analytics impact through self-service solutions and fostering a data-driven decision-making culture. Confidently calculate and articulate the impact of your HR analytics on organizational performance. Bonus: From Data to Strategy: The New Workforce Systems Leaders Transforming HR Our newest whitepaper, authored by our VP of People Analytics Strategy Richard Rosenow, recently launched to an enthusiastic reception. Clearly, it struck a chord. Focused on the emergence of a new People Analytics role that aligns the flow of data through an organization (which Richard dubbed the people data supply chain), this highly anticipated resource provides insight into the typically uncharted path of People Analytics leaders. Download this eBook today to learn: Key challenges in People Analytics (it’s not just you!) Actionable strategies for mastering the People Data Supply Chain, including an real-world example for managing attrition Who are Workforce Systems Leaders and what do they do? Get prepared to lead the next evolution of workforce management. Next steps? Contact us with your questions or to schedule a One Model Demo.
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15 min read
Dec 18, 2024
Transcript: Hi, everyone. Let’s dive right in. Today, we’re going to talk about unlocking the power of people data platforms—what that means, how to access your data, and how to connect with it in meaningful ways to drive insights across the workforce. Introduction I’m Richard, and for those I haven’t met, it’s great to meet you. A bit about me: I started my career in nonprofits—shout out to others here with a nonprofit background! From there, I moved into HR, focusing on workforce planning at GE Capital, followed by roles at Citibank. Eventually, I discovered my passion for People Analytics, which shaped the trajectory of my career. I’ve had the privilege of working on People Analytics teams at companies like Facebook, Uber, Nike, and Argo AI. Each experience taught me something new about building scalable teams and leveraging technology to solve big challenges. For example, at Facebook (when it was still Facebook), our People Analytics team grew from 15 to 150 people. It was an exhilarating time, but not every organization can afford that kind of scale. So, when I moved to Uber, the focus was on how to scale smarter—how to build products and platforms instead of large teams. At Nike, I also helped build data foundations and worked closely with data engineering teams to develop a more robust HR data hub. When I moved to Argo AI, I worked across HR tech, People Analytics, and project management. I was heavily involved with Workday and began exploring One Model, which shaped my approach to building scalable analytics solutions. Fast forward to today: I’m now VP of People Analytics Strategy at One Model, where I get to connect with hundreds of People Analytics teams annually. This has given me a unique perspective on what’s working, what’s not, and where we’re all heading. Why is People Analytics So Hard? Let’s start with a key question I ask every time I talk about People Analytics: Why is this so hard? People Analytics as we know it is still relatively new. The modern function emerged maybe 15 years ago, and while it’s evolved a lot since then, we’re still figuring it out. There are so many names, frameworks, and definitions out there—it’s confusing for everyone. If you’re struggling to make sense of this within your organization, know that you’re not alone. From Facebook to smaller companies, everyone finds this hard. Defining People Analytics When I talk about People Analytics, I use three definitions: Community People Analytics is a community of practitioners working to create better workplaces through data. If there’s one thing you take away today, it’s this: there is a thriving People Analytics community out there. It’s full of nerdy, passionate people who love this topic. If you’re curious about data or looking to use it more effectively in your organization, find these people—they’re everywhere and eager to connect. The Act The act of People Analytics is simply using data to support workforce decisions. This isn’t just an HR responsibility—everyone in the organization, from managers to the CEO, makes workforce decisions. They should all be using data to do it. The Function The People Analytics function is the team within HR that supports this work. They build systems, provide guardrails, and help the organization use data effectively. The Invisible Work of People Analytics Leaders One challenge for People Analytics leaders is that they’re often hired for one job but end up doing another. Their job descriptions focus on descriptive, predictive, and prescriptive analytics. But once they start, they realize IT and HR haven’t spoken in years. They’re stuck cleaning data, navigating politics, and trying to get access to systems—none of which were in the job description. This invisible work is critical but goes unrecognized. If you have a People Analytics leader, send them a note and let them know you see and appreciate their effort. The "Skipped Step" in HR: People Data Here’s where the real problem lies: HR skipped a step. We went from strategy to operations to technology without fully addressing people data—the process of extracting, cleaning, and organizing data into a comprehensive HR data hub. Analytics teams are left backtracking to fix foundational issues before they can deliver insights. This skipped step creates pain for everyone. And it’s becoming even more critical now that HR data systems are feeding not just analytics but also Generative AI. The Little Red Hen Moment This brings us to what I like to call the "Little Red Hen Moment." You might remember the story: the Little Red Hen finds some corn and asks the other farm animals, "Who will help me plant the corn?" They all say no. So she plants it herself. Later, she asks, "Who will help me harvest the corn?" Again, no one helps. She does it herself. Then she bakes the bread and asks, "Who will help me eat the bread?" And, of course, suddenly everyone wants in. This is exactly what happens with People Data in many organizations. HR leaders ask, “Who will help us build the business case for People Analytics?” The data engineering team says, "Not I," because they’re busy maintaining data pipelines for sales. The IT team says, "Not I," because they’re focused on streamlining the vendor landscape. The enterprise analytics team says, "Not I," because they’re prioritizing metrics for finance. So HR is left to plant the corn, harvest it, and bake the bread on its own. We pull together data manually, build foundational systems, and lay the groundwork for analytics and insights—all while trying to establish a sustainable workforce data supply chain. But once those insights are ready—once the bread is out of the oven—everyone shows up to eat. The same teams that didn’t prioritize People Data suddenly want the insights it produces. They’re eager to see workforce metrics, predictive models, or generative AI results, but they don’t recognize the effort it took to get there. This isn’t just an HR problem; it’s a structural issue. HR has been underinvested in and systemically held back. Other business functions—like finance, marketing, and operations—have robust platforms and strong executive support. HR deserves the same level of investment to drive business outcomes. The message here is simple: it’s time for HR to demand a seat at the table and take HR data ownership People Data and build a robust HR data hub to succeed.. We need to make the case for why this work matters—not just for HR, but for the entire organization. The Framework for People Data Platforms Let’s talk about People Data platforms—which are essentially the foundation for a workforce data supply chain. A platform has two key components: The Data Foundation This is where data is extracted, modeled, and organized. It’s the backbone of everything, including generative AI. The Application Layer This is where data is visualized, analyzed, and put to use. At One Model, we’ve developed a framework that covers every stage: extract, model, store, analyze, and deliver. Each step has detailed requirements, and we provide tools to help organizations navigate them effectively. Dive deeper into the 5 Steps to Get Data Extraction Right. Conclusion People Analytics is hard, but the opportunities are enormous. By investing in People Data platforms and supporting our teams, we can create better workplaces and drive smarter decisions. Q&A Q1: During the modeling phase, are you prioritizing data? Is all of it being stored, or are you storing it in multiple places? Are you saying, “This is the most useful for dashboards,” and keeping other data as a backup in case it’s needed for KPIs later? What does that look like? A: That’s a really good question. Here’s how it typically works: You have data that sits in your core HR tools, the data you extract from those tools, and the modeled data you use for analytics. Along the way, you need to maintain copies—raw files, for example—for audits. But it’s the modeled data that should be driving your business decisions. What often happens in HR is that we’re told, “Just pick what you need,” because we aren’t given the resources to extract and store everything. This is one of the things One Model addresses—we create a single, unified data model where all your data is combined and accessible in one spot. This approach is becoming the norm for mature People Analytics teams. They no longer accept being limited to a single report from Workday or any other system. Instead, they demand full access and make sure their data is modeled and ready for any use case. And this is important because features in your data can play into your models in surprising ways. For instance, data from internal communications platforms like FirstUp can be remarkably effective for attrition prediction, but it’s often difficult to get access to that data. Q2: So, you’re doing predictive modeling as well. Can you use the same scripts or frameworks and apply them to different datasets? A: That’s a great question. Another key point to understand here is the difference between data extracts for reporting and data extracts for data science. For example, Workday provides daily snapshots of data. That works fine for reporting, but for predictive modeling, you need data over time. HR data is especially time-sensitive—more so than in many other functions—because of how events like transfers, exits, and tenure affect workforce insights. You can’t have someone transferring after they’ve already quit. The sequence of events really matters. This is where taking raw file snapshots and turning them into analytical feeds becomes critical. The ability to extract data for machine learning and predictive modeling is fundamentally different from extracting data for reporting. It’s something HR teams need to be aware of and push their IT teams to support because I’ve seen too many teams pressured into settling for reporting-level extracts, and it’s just not enough. Q3: When working with highly customized platforms like Workday or your ATS system, you often can’t—or don’t—make changes. For example, adding regrettable versus non-regrettable turnover as a data point can require defining those terms and assigning someone to audit that information. What advice do you have for making the business case for these changes? A: That’s an excellent question. Two things come to mind. Bring the stakeholder’s pain with you Let’s say you have a stakeholder downstream who’s really feeling the pain from a lack of data, like not knowing whether turnover is regrettable or not. Often, HR tries to solve this issue internally, on behalf of the stakeholder, by negotiating changes with upstream teams like HR tech. The problem is, the tech team doesn’t feel that pain firsthand, so they don’t prioritize the change. Instead, bring the stakeholder along with you to these discussions. Let them articulate their challenge directly to the HR tech team. When the tech team sees how their choices—or lack of action—are impacting the business, they’re more likely to respond. Create a new umbrella function The other solution I’ve seen gaining traction is hiring a leader to oversee People Analytics, HR technology, HR strategy, and operations as a single function. We call this the “workforce systems leader.” About 40 Fortune 50 companies have already started building roles like this. This umbrella leader can help navigate the politics and make tough decisions more efficiently. For instance, they can prevent unnecessary internal friction, like the head of People Analytics clashing with the head of HR tech. Instead, this leader would coordinate those efforts to drive progress forward. Q4: How do you recommend building a relationship with IT so they understand HR’s needs without seeing it as interference with data governance? A: That’s a fantastic question. I’ll give you two approaches—one "nice" and one a little more assertive. The nice way A lot of times, IT leaders (and finance leaders too) don’t fully understand HR’s technical needs. But they do understand their role as people leaders. So, start by framing the conversation in terms they’ll relate to. For example, you might say, “You’re leading a 400-person organization. Do you have visibility into what’s happening in your own team? Do you know where your pain points are?” This can help them see how better data access benefits not just HR but also their own leadership. The assertive way Here’s the reality: Other functions, like IT or finance, have no problem saying "no" to HR. But when they need something—like hiring 50 new project managers—they come to us, and we almost always say "yes." HR is often the ultimate team player, taking on more than its share of the load. While that’s great in theory, it can sometimes weaken our bargaining position. To build a stronger relationship with IT, we need to be more assertive about our needs. For example, we might say, “If you want to continue working the way you are, we’re going to need support from you. Let’s come to the table and figure this out together.” In short, it’s about clear communication, mutual accountability, and, sometimes, standing our ground to get what we need. Thanks, everyone! Ready for a deeper dive? Download Achieving People Analytics Maturity with a People Data Platform today for more insights on maturing your workforce data for actionable insights.
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3 min read
Dec 12, 2024
Workplace gender equality is a critical focus for Australian employers, supported by initiatives like the Workplace Gender Equality Agency (WGEA). Established to promote and improve gender equality in workplaces, WGEA regulations require organisations with 100 or more employees to report gender data across six gender equality indicators. These indicators include key aspects such as pay equity, workforce composition, and representation in management roles. The reporting process is detailed, requiring a combination of point-in-time employee data and aggregate metrics spanning the reporting year. While this mandate enables organisations to reflect on and address gender equality, the process itself is challenging. Teams often spend weeks—or even months—sourcing data from disparate systems, aligning it with WGEA’s strict criteria, and meticulously validating it. However, with the right tools, this complex task can be streamlined with One Model. Tackling the Challenges of WGEA Reporting For most organisations, WGEA reporting is not just about compliance—it’s about leveraging the reported data to foster deeper insights into workforce gender equality. Yet, the process is notoriously cumbersome. Compiling detailed employee information, annualised salary data, and metrics such as leave and movement patterns often requires manual intervention and significant cross-team collaboration. This can lead to inefficiencies, inaccuracies, and limited time to analyse the results. But what if the data collection and validation process could be automated? This is where One Model comes in. Automating WGEA Reporting: How One Model Makes it Simple The One Model People Analytics platform transforms the arduous WGEA reporting process into a streamlined, automated operation. By centralising workforce data, aligning it with WGEA’s submission templates, and enabling robust validation, One Model allows organisations to achieve compliance efficiently while focusing on what matters most: understanding and acting on the insights derived from the data. Customer Spotlight An Australian customer recognised the platform’s potential to simplify their WGEA reporting. Some of the required workforce data was already housed in One Model, but integrating the full dataset—including annualised salary details and movement metrics—was the next step. By ingesting and modelling all the necessary data, One Model provided the customer with: A single source of truth: Data was centralised, validated, and securely accessible to analysts across various teams. Streamlined workflows: Automation reduced the need for manual data manipulation and cross-referencing. Tailored insights: The customer leveraged One Model’s analytics capabilities to create Storyboards and executive reports, turning raw data into actionable insights. The outcome? The customer not only completed their WGEA submission in one day instead of five, but also unearthed valuable insights for their leadership team, who were impressed by the quality and depth of the analysis. Beyond Compliance: The True Benefits of Automating WGEA Reporting Automating WGEA reporting increases efficiency and confidence while shining a light on what’s going well and areas for improvement. Efficiency: Teams save weeks of effort through automation, ensuring submissions are accurate and on time. Data Integrity: By centralising data and applying consistent validation, organisations can trust their numbers. Insights-Driven Culture: Once the reporting is complete, the data can be repurposed to drive conversations around workforce planning, pay equity, and diversity initiatives. With One Model, you can transform a complex compliance process into a streamlined, automated workflow. This not only saves time but also provides valuable insights that drive meaningful progress in workplace gender equality across the country. Ready to simplify your WGEA Reporting?
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Featured
4 min read
Dec 11, 2024
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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Featured
3 min read
Dec 04, 2024
What Exactly is a Golden Ticket Query? Is it the data science counterpart to the elusive Golden Ticket from Charlie and the Chocolate Factory—a one-way pass to an all-encompassing insight? At first glance, these queries promise ease and instant answers, but do they truly deliver the nuanced understanding your organization needs? Let's dive deeper into what makes these pre-built queries so appealing yet limited, and why embracing a more adaptable approach might be the key to smarter, data-driven decision-making. Golden Ticket Queries, also known as Hard-Coded Queries, are pre-built, standardized queries designed to pull data in the same way every time. Think of them like preset questions that a system will always answer in the same way, without flexibility or context. These queries are often very basic, structured to address common use cases, but they don’t adapt to the unique needs of different users or businesses. Why Are Golden Ticket Queries Often Criticized by Practitioners? Golden Ticket Queries are frequently called out by analytics and AI professionals for a few key reasons: Lack of Flexibility: These queries are static and don’t adapt to new business priorities or shifting data. For example, a query tracking headcount changes might miss important nuances like department-specific trends or seasonal fluctuations. Surface-Level Insights: They often provide basic answers without digging deeper. For example, simply knowing how many employees have high performance ratings doesn’t help you understand what factors contribute to their success or how you can foster high performance across the organization. Missed Opportunities: By sticking with preset queries you miss out on the chance to ask more nuanced specific questions that could reveal new opportunities or solutions tailored to your organization's goals. For instance, turnover metrics might look fine on the surface but miss patterns could emerge when broken down by tenure, engagement, or location. At first glance, Golden Ticket Queries might seem like a quick win, but they rarely provide the depth needed for effective, data-driven decision-making. Our Approach at One Model At One Model, we take a different approach to People Analytics and AI, one that embraces flexibility, customization, and the ability to ask the right questions, tailored to your unique needs. Context Matters: People Analytics isn’t one-size-fits-all. The questions you ask and the insights you seek should be shaped by the specific challenges and goals of your organization. One Model’s One AI Assistant lets you ask dynamic questions that reflect your context, providing answers that are more relevant and actionable. Deeper Insights, Smarter Decisions: By moving away from rigid, canned queries, you open the door to deeper, more thoughtful exploration of your data. Custom queries allow you to uncover insights that are directly tied to your business objectives. Scalable and Adaptable: As your business evolves, so should your analytics. The flexibility built into One Model ensures that as your organization grows, your data exploration and insights grow with it. This adaptability means that your analytics can stay ahead of trends, adjust to new business strategies, and continuously inform smarter decisions. Golden Ticket Queries may seem convenient, but their limitations can hold your organization back from achieving its full potential. Their rigidity and surface-level approach to analytics make them an imperfect fit for today’s complex business environments. One Model believes that AI should help you ask the right questions, not just the easiest ones. With tools designed for flexibility, customization, and actionable insights, we help you uncover the deeper patterns that lead to smarter decisions and better outcomes.
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Featured
3 min read
Nov 25, 2024
The holiday season is here! And with it comes family, food, and perhaps a little friendly debate. This year, let's set aside the usual topics that lead to eye rolls and heated discussions with Uncle Bob. Here are 3 people analytics topics to elevate the dinner table banter and avoid an argument over the turkey. 1. Overworking in a post-pandemic world Did you know that employees are clocking in three extra hours each day compared to pre-pandemic times? It's a surprising revelation that demands our attention, especially when coupled with the fact that workplace burnout is affecting a significant 41% of workers. The extended work hours may be a consequence of the blurred lines between professional and personal life in the remote work era. As you gather around the holiday table, consider discussing how businesses can strike a balance between productivity and well-being, fostering a work environment that prioritizes both. 2. Lacking engagement leads to less profits Here's a captivating statistic: highly engaged teams exhibit a remarkable 21% greater profitability. Yet, only 20% of employees report being engaged at work. So how can organizations cultivate a culture that not only satisfies employees but truly engages them? Discussing the impact of workplace culture on employee engagement can open up avenues for exploring innovative approaches to create environments where employees feel valued and motivated. 3. Staying remote has increased productivity With over 50% of employers citing increased productivity as the primary benefit of remote work, the landscape of work is undergoing a significant transformation. As you dive into that pumpkin pie, consider exploring the potential of remote work in the long run. How can organizations harness this newfound productivity without compromising collaboration and team dynamics? Delve into the dynamics of remote work and share insights on striking the right balance for sustained success. Incorporating these people analytics topics into your holiday conversations can provide a fresh perspective on the evolving nature of work. As we reflect on the challenges and opportunities of the past year, let's toast to a future where work not only sustains our livelihoods but also enriches our lives. Cheers to a Thanksgiving feast filled with engaging conversations, and happy Thanksgiving from One Model! Did Uncle Bob ask, "What in the world is people analytics"? Download this eBook for an enjoyable read that covers everything from understanding the basic foundation of people analytics to advanced HR strategy. Guaranteed to start a productive family conversation on how data can drive meaningful change across any organization.
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Featured
14 min read
Nov 15, 2024
As a people analytics leader, you’re going to be confronted with some not-so-simple, horribly open-ended questions: “Hey, so what do you want to measure?” Where should we start?” or… “What HR dashboards should we build?” Perhaps these words have been uttered by a well-intentioned business analyst from IT, peering at you from behind a laptop, eager to get your items added into an upcoming sprint before all their resources get tied up with something else. What do you say? Something really gnarly and fancy that shows your analytic savvy? Something that focuses on a key issue confronting the organization? Something basic? Fear not. In this blog post, we’ll walk you through eight essential people analytics dashboards. You should be able to get the HR data for all of these from your core HRIS or HCM solution, even if they’re in different modules and you have to combine it into one dataset. The key performance indicators (KPIs) in these views will give you the highest impact: Headcount Metrics Dashboard Span of Control Dashboard Employee Turnover Dashboard or Attrition Dashboard Talent Flow Dashboard Career Growth / Promotions Dashboard Diversity (DE&I) Dashboard Employee Tenure Dashboard …see below… 1: Headcount Metrics Dashboard Headcount metrics are the foundation of people analytics. Headcount speaks volumes. Trend it over time, break it out by key groupings, and you are well on your way to doing great people analytics. Here’s an initial view that captures the basics. Here’s what’s included in this dashboard so you can get a handle on headcount. In the upper right, you’ve got what I call the “walking around the number”. It’s not anything that will help you make an informed decision on anything. But this is the stat that you would feel embarrassed if someone asked you and you didn’t know off the top of your head. Here it’s the total number of employees as of the current point in time. (EOP is shorthand for End of Period. Be precise in how you define things. More on this at the end.) Next, you’ll want to see the headcount trended over time. Here we have a monthly trend paired with the same period last year. Boom. Now you can see how things are changing and how they compare with the previous year. Also, these two visuals are a great test run for your existing reporting and analytics capabilities. In the bottom right, here you have headcount broken out by org unit (or business unit, or supervisory org for you Workday types). Here you want not only the total counts but ideally a stacked column view so you can see the proportion of contractors, part-time, co-op, or other employment types. Different orgs might get their work done in different ways. You should know the differences. Finally, a map view of headcount by geography. It’s not a basic visual, but it has certainly become essential. Things happen in the world. You need to know where your workforce is so you can quickly estimate the impact and plan support. In just the past two years, employees have been impacted by wildfires, heat domes, political unrest, blizzards, cold snaps, flooding, and, of course, COVID. Geo maps have officially gone from fancy visual to essential view. 2: Span of Control Dashboard I’m going to change things up a bit by elevating the span of control to the second slot on this list. Don’t worry. We dive into attrition and representation later in the article. As a people leader, you’ve got to maintain some perspective on how efficiently your workforce gets work done. There are many ways to do this. You could calculate the total cost of your workforce. You could align those costs against revenue over time. By all means, do that. But this list is also there to help you get started. With just the data from your core HCM / HRIS system, your team should be able to show you the span of control and organizational layers. These metrics always remind me of stepping on a scale. If your span of control is ticking down, you’re getting less lean. If you’re adding more layers, your internal coordination costs are going up. There could be good reasons for this– but there sure as heck can be bad reasons for this. Here you’ll find your key Span of Control Metrics, your trend over time, and your layers and org units visualized. The real killer metric – if you’ve got the stomach for it – is a simple list of the number of managers in your organization that have only one or two direct reports. Use these views to keep your talent management processes grounded in business reality. If your existing team/technology can’t produce these views then shift them back. 3: Employee Turnover Dashboard or Attrition Dashboard Ok, we can’t go any further without employee turnover. Attrition if you’re feeling fancy. Turnover is the strongest signal you get from your workforce. Someone worked here and– for one reason or another– it didn’t work out. Changing jobs and firing an employee are both major events. Your workforce is telling you something and you need to listen to help you with employee retention. Here’s a basic view to get you started. Again, get your rolling 12-month termination rate up at the top and trend it out with the previous year for context. Below that, you see a breakout of voluntary and involuntary termination rates. Then, you can see breakouts by business unit, location, and org tenure groupings. Now with a glance, you can see how turnover rates are changing, where they are high, and whether it’s you or the employee forcing the change. Learn more how to calculate the cost of turnover. 4: Talent Flow Dashboard Once you’ve got a turnover view squared away, you can move into broader views of talent movement within your organization. Here’s a high-level talent flow view to get started. It leads off with a consolidated view of hires, terms, and net hires trend over time. I love this view because it lends itself to discussions of churn and the cost of turnover. The top area (green) shows external hires. The bottom (red) shows exits/terminations. The dark bars show the difference: net hires. The big question. How much of that time and money that you put into recruiting is just to replace the people who leave the company? A great variation on this view is to limit it to women or underrepresented groups. Are you working hard to attract these demographics, only to have them leave because they don’t find the organization to be a fit for them? We’ll get to more workforce representation views below. Next to the Net Hire Trend, you can mix in a growth metric and a helpful breakout by “business unit, so you can keep an eye on what segments of the organization are growing/shrinking. Are they the ones you expect? Later when you bring in data from other systems like learning, this view can be a place to collaborate with the learning team to answer questions like: Are you adding more employees, when you could be upskilling? Finally, get a solid crosstab view of promotions or movements. This will help you optimize talent development and answer questions like: Do people move from function to function? If so, what are the common paths? What paths don’t exist? Should they? 5: Career Growth / Promotions Dashboard After you get the big picture on movements, dig into promotions. In my mind, the movement and span of control views are about what the organization is experiencing. Promotions put you more in the mind of your employees and what career opportunities look like in your organization. I’ve added two of our key metrics to the top of this one. What’s the rate at which people get promoted and how long is the typical wait for promotion? Once you know the typical (average or median is fine) wait time, keep your ears out for high potential / high performers who have run past that mark. They’re probably keeping a rough estimate of that metric in their minds as well. Below that are two breakout views. The first one - “Manager Hires vs. Promotions to Manager” - is meant to look at a key milestone in career growth. I’ve used promotion to manager, but you might have unique ones. Then for each business unit, I’ve compared the number of promotions into that key group with the number of outside hires in that group. Are you growing your own leaders (or another key group)? If not, why? Filling out the bottom row is the “Termination Rate and Headcount by Time since Last Promotion” view. Look for two things here: 1) Do people leave if they don’t get promoted? 2) Do people leave right after they get promoted? 6: Diversity (DE&I) Dashboard It’s past time we brought in views of the diversity, equity and inclusion (DE&I) in your workforce. Many of the views in the dashboard below are split out versions of the metrics introduced above. Above is a sample diversity dashboard using male / female breakouts. Use this as a template for other representation breakouts including ethnicity, gender identity, age, etc. Any of these views could be modified to incorporate multiple, rather than just two, groupings. The top bar shows activity differentials over time. Hires are done simply as counts. Do you hire more men than women? Are promotions and terminations handed as rates to monitor for disproportionate outcomes?, i.e. are men promoted more often than women? The second row shows representation by key grouping in stacked horizontal bars. I like organizational layer and salary band to show if high career outcomes are disproportionate. I’d recommend the inclusion of tenure as well, however. If your organization had a history of disproportionate staffing, you will get a clue in this view. That could explain why today’s initiatives have not yet balanced out outcomes in level or pay. Or differences in tenure might be explained by differences in termination rates, depicted directly above in this view. This is a multifaceted issue. 7: Employee Tenure Dashboard Confession. I love tenure. I’ve come of age in my career amid data telling me that I’ll work for something like 11 companies before I retire. And, to be honest, I’ve done my share of career hopping. But it turns out that when you stick around somewhere, you learn things. You make connections with your co-workers. Employee tenure represents the accumulation of invaluable knowledge and connections that help you measure the value of your human capital. Next to average tenure, this dashboard shows the total accumulated workforce tenure in years. While not exactly a “walking around number,” you can use this to impress your fellow leaders into thinking about your workforce like the treasured asset it is. “Hey, our team has x millennia of accumulated experience!” Rounding out this view is a sorted view of positions or job titles with lots of accumulated experience as well as a stacked trend over time to see how tenure groupings are changing. 8: Dashboard Definitions and Details This final section is not a specific dashboard suggestion. Rather, it’s intended as a sobering reminder that none of the dashboards above will make an impact in your organization if you can’t explain your logic and build trust in your data. I like to build little glossary style views right into the dashboards I create. For example, at the bottom of our standard attrition storyboards, I’ve added breakouts showing which termination reason codes are included as voluntary and which are involuntary. Next to my glossary, I’ve created a table that breaks out the subcomponents of turnover rate, such as total headcount and days in period. I like to include at least one leap year for a bit of showmanship. “Look, I’ve even accounted for the fact that 2020 had 366 days, so back off.” Ready To Learn More? Get All Your Questions Answered One-on-one. Finally, if your security models and technology support it, drill to detail. This is the number one, all-time champion feature of people analytics. Click on headcount, terminations, whatever and see the actual people included in the data. Bonus points for adding the definition and “bread crumb trail” for metrics that build off of other metrics. Below is a view of how we do that in One Model. If you’d like to see these people analytics dashboards in action or learn more about people analytics software for your organization, reach out to us!
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Featured
7 min read
Nov 05, 2024
As you know, People Analytics has evolved far beyond basic HR metrics like turnover rates or headcount tracking. As organizations seek to make smarter, proactive decisions about their workforce, they’re turning to more sophisticated People Analytics techniques. Moving beyond foundational metrics, advanced analytics — like predictive modeling, sentiment analysis, employee journey mapping, and ethical AI considerations — offer HR professionals the opportunity to play a powerful, proactive role in shaping their organization’s future. With these tools, HR leaders can anticipate challenges, influence key decisions, and drive meaningful, strategic change. This blog explores four advanced analytics techniques that help People Analytics leaders move from basic reporting to making decisions that resonate throughout the organization. 1. Predictive Analytics: Looking Ahead with Confidence What It Is: Predictive analytics leverages historical data to forecast future workforce trends, such as turnover risks, performance outcomes, or employee engagement levels. By identifying patterns within existing data, organizations can make proactive decisions, positioning them to address issues before they arise. Applications: Predictive analytics is highly valuable for HR teams aiming to prevent turnover in high-risk teams, pinpoint factors that impact employee engagement, or understand potential productivity trends. For instance, if a team shows signs of elevated turnover risk, leaders can intervene early — offering targeted support or resources to improve retention. Example: Consider a company that uses predictive analytics to identify teams with high burnout risk based on previous data trends, like prolonged overtime hours or low engagement scores. With this foresight, HR can intervene with support initiatives, helping employees recharge and boosting retention. Learn more about our One AI and One AI Assistant predictive analytics. 2. Sentiment Analysis: Understanding Employee Emotion at Scale What It Is: Sentiment analysis uses natural language processing (NLP) to interpret the emotional tone behind employee feedback, open-ended survey responses, and internal communication channels. By analyzing this data, companies gain a real-time understanding of employee morale and can detect early signs of dissatisfaction. Applications: Sentiment analysis can track morale trends across the organization, identify engagement dips, and help HR better understand employee needs. This technique allows for “pulse” insights, where sentiment can be monitored continuously, alerting leaders to shifts in morale. Example: A company might use sentiment analysis to monitor feedback on a recent policy change. If negative sentiment spikes, leadership can quickly address concerns, maintaining trust and morale by responding with empathy and transparency. 3 Keys to Effective Listening at Scale 3. Employee Journey Mapping: Visualizing the Employee Experience What It Is: Employee journey mapping visualizes each stage of an employee’s experience, from recruitment to exit, identifying critical touchpoints that affect engagement, satisfaction, and retention. By mapping these interactions, HR can see where employees thrive or struggle, allowing for targeted interventions. Applications: Journey mapping is valuable for tracking specific experiences such as onboarding effectiveness, career development paths, and retention at pivotal moments. It provides insights into the employee lifecycle, helping HR design initiatives that enhance satisfaction and reduce turnover. Example: Using Sankey diagrams, a company could visualize the journey from onboarding through various career milestones — revealing, for instance, that many employees exit after two years in certain roles. This insight enables HR implement targeted engagement or development programs during critical points in an employee’s journey. 4. Ethical Considerations in Advanced Workforce Analytics Why It Matters: As People Analytics methods become more advanced, ethical considerations grow in importance, especially around data privacy and employee consent. Ensuring responsible data use is essential for maintaining employee trust and aligning with broader organizational values. Best Practices: To conduct People Analytics ethically, companies should anonymize data wherever possible, obtain clear employee consent, and maintain transparency about data collection and usage. A commitment to ethical guidelines isn’t just about compliance — it strengthens trust and encourages openness to analytics-driven initiatives. Example: Organizations risk overstepping by monitoring too closely, which can lead to feelings of surveillance among employees. Ethical People Analytics is about balance: using data to benefit the organization while respecting employees’ privacy and autonomy. Conclusion: Moving from Insight to Impact The field of People Analytics has grown into a powerful strategic tool, and advanced HR analytics techniques like these (and others) enable HR leaders to anticipate, understand, and enhance the employee experience in proactive, strategic ways. Ready to take your People Analytics impact to the next level? Measuring the Value of People Analytics dives even further into implementing these advanced analytics strategies and gaining a sustainable advantage. Complete the form below to download it today and empower your People Analytics team with the tools needed for meaningful, data-driven change.
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Featured
5 min read
Nov 01, 2024
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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Featured
10 min read
Oct 30, 2024
As Halloween approaches, it's time to gather 'round the virtual campfire and hear some spine-tingling tales of people analytics gone awry. Here are six not-so-fictional stories sure to scare even the bravest of HR teams. But never fear ... One Model can help you avoid these spooky situations. 1. The Deceptive AI As the clock struck midnight on the first night of budget season, Melinda, a weary HR director at a tech startup, huddled over her laptop. Shadows deepened under her eyes as she scanned the AI-generated analytics dashboard, watching as trusted employees were flagged as “at risk” or “low achievers.” With time running out, she pushed forward, blaming the strange anomalies on minor glitches. But as the night wore on, her nerves frayed. It felt as though the AI had taken on a sinister life of its own, turning top performers into liabilities and boosting unremarkable staff. Every adjustment she made only deepened the web of confusion, as the data twisted into more surreal distortions. Relentlessly, the algorithm spun its tale, indifferent to the human impact of its errors. By dawn, her team arrived, horrified to see that the AI’s deceptions had led them down a dangerous path. Layoff plans were made based on false conclusions, with a list targeting their best employees. In the aftermath, Melinda realized the cost of trusting an unchecked AI and the need for a human touch—an eerie reminder that digital solutions aren’t always what they seem. The One Model Solution: With One Model's reliable One AI Assistant, Melinda's team could have seen clearly through the data fog. Built specifically to circumvent and control for hallucinations that other AI tools suffer from, One Model dispels ghostly errors, ensuring critical decisions stay firmly rooted in truth. 2. The Phantom Data Breach Nightmare Tom, an HR director at a major retail chain, awoke to a chilling alert: thousands of employee records had been compromised. Private details—salaries, addresses, even social security numbers—were scattered in the digital winds, accessible to unknown eyes. As word spread, a sense of dread crept through the office. Employees whispered, wondering which of their secrets might surface next. Tom and his team scrambled to contain the breach, yet every solution seemed futile. Like a ghost haunting their systems, the source of the leak remained elusive, lurking just beyond their reach. As panic intensified, trust eroded, and Tom knew that restoring confidence would be no easy feat. Days blurred into nights as Tom sought answers, each failed attempt weighing heavily on him. The breach had shaken their foundation, revealing the frightening truth: Any system that is improperly secured risks being haunted by unseen vulnerabilities, putting both data and trust in jeopardy. The One Model Solution: With the most recent ISO certification, One Model offers a secure platform, prioritizing IT security and limiting security risks to safeguard your data. Your sensitive information is locked away, protected from malicious entities. 3. The Mysterious Analytics Abyss It was a tense meeting with finance leadership, and John’s people analytics team was on the spot. When asked a straightforward question about headcount, John confidently turned to their analytics platform. But as he searched, an unsettling realization struck—key insights lay hidden beneath a murky interface, unreachable and obscured in a vast sea of metrics. Frustration mounted as John sifted through shallow reports, aware that the answers were somewhere in the depths of the system, but the platform seemed intent on keeping them out of reach. Then the frightful question came from the CHRO, "Can we break these numbers down by cohorts or the recent satisfaction survey to see if there are any correlations?" No! John began to sweat. By the end of the meeting, John was left with his head hanging and nothing but vague numbers and a growing sense of unease. The analytics abyss had swallowed his insights whole, and he feared they’d invested in a tool that could never deliver the in-depth insights they needed. The One Model Solution: One Model's platform is transparent and flexible, empowering you to get urgent answers HR metrics answers fast for actionable insights to solve your business problems. It can also empower HR teams and Finance teams to have two different, yet both accurate, views of headcount. 4. The Haunted (In-)House Build At her tech startup that had grown into a robust company, Lydia had pinned her hopes on a custom-built analytics system that would satisfy her every desire. The project began with fervour, but even after two grueling years, her dream of a seamless solution had turned into a nightmare. Reports froze mid-load, she could not get important data out of her systems and into her tool, back-dated updates made recent insight vanish, and each new fix only seemed to unearth another glitch lurking in the code. As the cycle of troubleshooting dragged on, Lydia felt trapped, her team ensnared in a haunted system that consumed resources without mercy. The "tailored" solution she’d envisioned had become a never-ending horror show, draining time and morale. Each day she feared the day IT would say, "No More!" With each passing day, Lydia faced a harsh truth: the in-house build that was supposed to empower her team had instead bound them to endless repairs, a ghostly presence sapping their productivity from the shadows. The One Model Solution: One Model offers the only people analytics platform that truly combines the benefits of both buying and building, delivering a best-of-both worlds scenario. Teams get the flexibility of leveraging pre-built elements as is, or they can opt to build within the platform and configure it to fit their organization’s unique needs, on their own, or with the help of our highly-skilled team of data engineers and customer success professionals. With this choose-your-own-adventure approach, enterprises can enjoy both the simplicity of buying and the optional customisation of building. Learn more in this whitepaper. 5. The Regulatory Non-Compliance Graveyard Ben, the CHRO of a healthcare startup, trusted his new AI tool to ensure compliance. But during a surprise audit, a skeleton of missed requirements surfaced, revealing that non-compliance could cost the company up to €20M—or worse, a 4% hit to annual revenue. The AI, bought in haste to stay competitive, had failed to meet regulatory standards, leaving them exposed. The findings felt like a graveyard of compliance oversights, each one a potential pitfall that threatened the company’s reputation and future. Ben’s stomach churned as he realized the scale of the error—his trust in the AI had led them down a costly, dangerous path. Facing penalties and a potential PR crisis, Ben knew they couldn’t afford another misstep. The allure of an “untested” AI had come with a hefty price, haunting both the company and its once-trusted compliance team. The One Model Solution: One Model's robust HR data governance protocols keep you up to date on AI regulations and ensure your platform remains compliant through the approaching wave of regulatory scrutiny. No need to fear the consequences of non-compliance when you have One Model as your trusted people analytics partner. 6. The Legend of the Unguarded Data Vault Mia, the People Analytics director at a biotech firm, was horrified to discover employees were building reports and using generative AI that was giving them incorrect data. She learned that Bill had accidently gained access to salary data in their analytics system. Linda had produced a report that said there was 124K headcount and Susan a report that said there was 54K. As word spread, trust in Mia’s people analytics tool unraveled, and employees grew anxious, fearing that that no place was a safe harbor knowledge. The teams reverted back to changes based solely on "guy instinct". Dashboards with sensitive information were deleted. Mia felt the weight and mourned that she ever believed the fantastic pitch made by the people analytics software company, knowing that the lapse could haunt her team for months, if not years. Determined to repair the damage, Mia began implementing stronger safeguards. But the incident left a lasting reminder that without mechanisms to assign customized security clearance, even the most secure vaults can hold hidden backdoors, ready to unleash chaos at the worst possible moment. The One Model Solution: One Model's role-based security guarantees that only authorized personnel access sensitive data, maintaining the confidentiality and integrity of your information. One Model does what is promised and is the most trusted people analytics software. Illuminating the Path Forward No matter the pitfalls your people analytics team may face, rest assured that One Model's people analytics platform is your guiding light in the darkness. We offer a secure, transparent, flexible, and customizable platform, backed by dedicated support to navigate the most challenging HR scenarios. Don't let these spine-chilling tales haunt your dreams. Reach out for a demo and discover how we can lead you to data-driven HR success. This Halloween season, ensure your HR analytics journey is a treat, not a terrifying trick!
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Featured
5 min read
Oct 11, 2024
In his talk, "Under the Hood: AI-Driven Engineering Workflows for Future of Work," Chris Butler, CEO of One Model, addressed what's coming and how it will impact everything. The key takeaway? AI is about to change the game for productivity by enabling what Chris calls "agentic workflows." Here’s a peek at what that means and why it’s a big deal for your workplace. The AI Ecosystem is Opening Up The enterprise AI ecosystem is evolving quickly. Imagine an AI at work that doesn’t just answer questions but can also take action—accessing tools, managing processes, and optimizing workflows. According to Chris, the likes of Microsoft’s Copilot, Apple’s assistant, and other major players like Salesforce are poised to become the AI linchpins of the workplace. Soon, AI won’t just assist; it will be seamlessly integrated into every facet of your digital workspace. Enter Agentic Workflows One Model is currently building agentic workflows to turbocharge workplace efficiency. Think of a group of specialized AI agents, each with its own job description, working collaboratively—just like a project team. From gathering data, analyzing it, and critiquing results to creating dashboards, these agents mimic the roles of a traditional data team. The result? Faster, smarter outputs that scale without needing more people. Chris gave real-world examples of ai agents in action: An AI project manager, data engineer, and analyst worked together to gather compensation data, clean it, and create insightful reports—tasks that normally take days were completed in hours. The AI agents interact with each other in natural language, refine each other’s work, and iterate until the job is done right. From Four Agents to a Swarm What started as four distinct agents evolved into a swarm—a scalable network of specialized agents able to handle increasingly complex projects. By shifting to a directed graph model, One Model made it possible for multiple agents to work in parallel, dramatically reducing project time. Chris shared an impressive example: A task that two senior data engineers estimated would take twenty days was completed by AI in just 45 minutes. Another key takeaway is that the more specialized the agents work, the higher quality the output. Therefore, having more specialized agents is better than a few multi-purpose ones. What Does This Mean for Productivity? The implications are huge. AI-driven workflows mean fewer manual tasks, faster data processing, and a deeper focus on insights that matter. Companies can double down on their core missions while relying on AI to handle tedious, data-intensive work. Chris predicts that enterprise AI will become the interface we use to ask questions and get work done—a one-stop assistant that pulls insights from different tools and presents them in a digestible way. Dashboards Are Dead—Almost In the future dashboards as we know them may become secondary. Instead of static reports, enterprise AI will generate dynamic, on-demand insights and even make recommendations. Dashboards will still exist, but they’ll be an interface controlled by the AI—just one of many tools in the box. The first point of interaction will be the AI itself, which will decide what tools to use to provide you with answers. Securing the AI Frontier Chris also highlighted a critical concern: securing enterprise AI. As these AIs gain more access to tools and data, the risk of improper usage grows. HR and People Analytics teams need to partner closely with IT to ensure that the right security measures are in place—because once access is lost, it’s hard to regain control. Welcome agentic workflows to the team. Agentic workflows are reshaping the future of work. The enterprise AI of tomorrow won’t just assist employees; it will be an active participant in getting work done—faster, smarter, and more securely. Are you ready to work with your new AI teammates? Are you thinking about using AI? You'll need a solid data platform. Learn why that is so critical and see how you can achieve success by reading our whitepaper.
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Featured
8 min read
Oct 10, 2024
Imagine you’re preparing your team for a project involving cutting-edge AI tools that didn’t even exist five years ago. You’ve assembled the best people you can find, but you quickly realize there’s a significant skills gap. Or perhaps you’re ready to expand into new markets, but the local talent is scarce, and your remote work policies are outdated. You need the right people, with the right skills, at the right time—yet it’s no small feat to make that happen. This is the reality for organizations today. Rapid technological advancements, especially in artificial intelligence, mean that the skills needed to stay competitive are constantly evolving. And while it’s tempting to address these challenges with reactive hiring and quick fixes, that approach only goes so far. What companies need is a sustainable, proactive approach to workforce planning—one that ensures your team isn’t just equipped for today’s challenges, but positioned for future growth as well. Here’s a quick overview of a modern workforce planning methodology for doing exactly that. STRATEGY: Start with the Vision Before diving into the details of talent acquisition strategy, take a step back and ask: What’s our long-term vision, and how should our workforce evolve to support it? As AI transforms job roles across industries, workforce strategy must adapt by aligning every decision about hiring for skills gaps, development, and retention with the company’s future needs. For example, a tech startup embracing AI-driven innovation might prioritize flexibility and tech-savviness in its talent acquisition strategy, looking for individuals who can work alongside AI tools and understand how to leverage them for greater efficiency. OPERATIONS: The Infrastructure that Keeps Things Moving With AI tools entering the workplace, operations play a critical role in ensuring that systems and processes keep pace. Imagine operations as the logistical backbone of workforce planning—it encompasses the workflows that handle headcount requests, onboarding protocols, and ongoing workforce management, but also integrates AI where it can streamline processes and enhance efficiency. A manufacturing company, for instance, might utilize AI-driven scheduling tools to manage production ramp-ups more effectively. Strong operations allow organizations to react to immediate needs—such as ramping up production or hiring for skills gaps—without compromising on strategic goals. ANALYTICS: Gaining Insight into the Workforce AI-driven analytics now enable organizations to gather workforce planning insights with unprecedented speed and precision. Leveraging analytics allows companies to track workforce trends, assess AI’s impact on skill requirements, and even forecast future needs based on anticipated AI developments. For instance, a healthcare organization might use workforce analytics powered by AI to predict staffing needs, identify high-turnover roles, and uncover insights that guide decision-making. By using AI-enhanced analytics, leaders can transition from intuition-based decisions to data-driven strategies that keep the workforce planning process aligned with evolving business needs. PLANNING: Mapping the Path from Today to Tomorrow Planning is where strategy and analytics converge to form a clear, actionable roadmap, especially crucial in an AI-powered world. With AI transforming industries at breakneck speed, organizations need planning that not only fills immediate gaps but also anticipates long-term shifts. Consider a retail company that uses AI to predict customer demand for the holiday season. By using this data to create a workforce planning strategy, they can assess the skills needed, optimize staffing levels, and allocate resources efficiently. A well-defined plan helps organizations stay a step ahead, allowing them to allocate talent where it’s needed most—both today and in an AI-driven future. INTELLIGENCE: Looking Beyond the Company Walls A strong workforce planning methodology also demands a focus on external intelligence. This means staying attuned to shifts in the talent market, industry developments, and the competitive landscape—especially as AI reshapes the types of skills that are in demand. By gathering insights on AI-related trends, organizations can make better-informed decisions about where and when to invest in talent. For instance, a company might discover that its competitors are investing heavily in AI training programs for employees. This intelligence can drive proactive decisions, like launching an internal AI upskilling initiative to stay competitive and attract tech-forward talent. Putting It All Together By taking a holistic approach to workforce planning, companies can move from being reactive to AI-driven change to proactively leveraging AI’s potential. Through the pillars of Strategy, Operations, Analytics, Planning, and Intelligence (what we call the SOAPI framework), leaders can create a workforce that’s equipped to not only meet today’s demands but thrive in the AI age. In a world where technology is reshaping work at every level, those organizations that take a proactive, integrated approach to workforce planning will be best positioned to lead. Whether you’re preparing for an AI-driven project, expanding into new markets, or future-proofing your team, it’s time to move beyond quick fixes and build a workforce that’s truly ready for what comes next. The One Model Difference Effective workforce planning is powered by data and AI, and One Model offers the tools to make it seamless. With One AI and the One AI Assistant integrated into the People Data Cloud™, One Model provides a powerful people analytics platform that consolidates, cleanses, and models workforce data. This AI-enhanced solution equips HR teams with real-time insights, enabling smarter, faster decisions across every stage of workforce planning. Whether forecasting talent needs or optimizing current roles, One Model ensures organizations can proactively build a workforce that’s aligned with AI-driven change while upholding high standards of data security and privacy. Ready to dive into the full SOAPI framework structure and set a foundation for a thriving workforce planning strategy? Download now!
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5 min read
Oct 03, 2024
Workforce planning and forecasting have become paramount for finance leaders to navigate market uncertainties and stay ahead of the competition. One Model's advanced People Analytics platform enables finance leaders to make smarter data-driven decisions, propelling their business toward sustainable growth and increased profitability. Centralise HR and Finance data for accurate predictions. The foundation of effective workforce planning lies in the ability to consolidate data from various sources into a single, reliable location. One Model achieves this by seamlessly integrating HR data with finance data, creating a centralized hub of valuable insights. By breaking down silos and allowing for data collaboration, finance leaders can gain a comprehensive understanding of their workforce, leading to more accurate predictions and tactical strategies. Slice and dice data more efficiently. Traditional ERP systems often struggle to handle the sheer volume and complexity of workforce data, leading to sluggish reporting and analysis. One Model, on the other hand, offers the ability to slice and dice data with ease, providing real-time insights and a granular, employee-level detail. Finance leaders can effortlessly examine the cost and productivity drivers at a departmental or individual level, empowering them to implement strategic initiatives with surgical precision. Identify high performers and which roles deliver the most value. Understanding the contribution of each role within an organization is crucial for effective workforce planning. One Model's advanced analytics capabilities offer improved visibility into productivity, revealing which roles deliver the most value to the organization. By identifying top-performing roles and focusing on their development, companies can reduce costly turnover, unleash the full potential of their workforce, and bolster overall performance. Better prepare for mergers, acquisitions, and divestitures. The financial services sector often witnesses mergers, acquisitions, and divestitures, which can lead to complex organizational changes and talent restructuring. With One Model, finance leaders can confidently embark on these transformations by leveraging the platform's capabilities. One Model can provide quick insight into topics such as your spans and layers that would traditionally involve high-cost and time-consuming consulting projects. From developing clear organizational structures to conducting talent audits to retain key personnel, One Model ensures a smooth transition and alignment of talent with strategic goals. Make more data-informed business decisions. Quick and informed decisions are critical for CFOs. With One Model, you can build your own metrics and definitions for headcount, FTE (full-time equivalents) updated daily, and other performance indicators to assess the return on investment from talent programs. And if Finance and HR can’t agree on how a certain metric (e.g., headcount) is calculated, One Model can support both variations. With clear insight into headcount and FTEs, you can better measure performance and plan labor needs. One Model delivers a holistic view of talent distribution and performance so Finance leaders can optimize headcount for the company’s needs, maintain cost-efficiency, and strike the perfect balance between talent and resources. Facilitate deeper conversations between HR and Finance. HR and Finance teams can have more meaningful and pointed conversations using One Model — where all the workforce data is captured, data quality is managed, and all related dimensions (e.g., hierarchies, employee attributes) are available for analysis. Bringing HR and Finance teams together can help your company accelerate your People Analytics journey and more easily identify opportunities to turn a profit. With One Model you can gain insight into more advanced metrics like Return on Human Investment Ratio (the ratio of operating profit, adding back total compensation expense, returned for every dollar invested in employee compensation and benefits) and hundreds of others to level up your HR and Finance decision making. Two examples of content specifically designed to align HR and Finance teams and empower them to make smarter data-driven decisions are: Headcount Storyboard — Setting up a storyboard which shows headcount represented in multiple ways: FTEs vs. employee counts, variations of which statuses are included/excluded, etc. This information becomes readily comparable with the metric definitions only a click away. Even better, the storyboard can be shared with the finance and HR partners in the discussion to explore on their own after the session. One Model is the best tool for counting headcount over time because it can support multiple variations. Hierarchy Storyboard — Providing views of the headcount as seen using the supervisor and cost hierarchies side-by-side will help to emphasize that both are simultaneously correct (i.e., the grand total is exactly the same). This can also provide an opportunity to investigate some of the situations where the cost and organizational hierarchy are not aligned. In many cases, these situations can be understood. Still, occasionally there are errors from previous reorganizations/transfers which resulted in costing information not being updated for a given employee (or group of employees). One Model is your partner for profitable growth One Model stands out as the ideal People Analytics partner for companies seeking to drive profitability through data-driven decision-making. If you’re ready to learn more, download our eBook 4 Ways CFOs Can Increase Profitability with One Model’s People Analytics Platform to discover even more ways our platform can enhance your profits.
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Featured
5 min read
Sep 19, 2024
One AI Assistant is a generative AI tool designed to revolutionize the way you work. Let's take a closer look at how One AI Assistant saves the day for enhancing the daily routines of two key personas in the HR world: an HR Data Analyst and an HR Manager. Meet the Team Mark, the HR Manager Role: Mark blends strong leadership with a keen sense of employee needs to lead HR initiatives that build a positive workplace culture. As an HR Manager, he oversees all aspects of human resources, from recruitment and onboarding to employee development and retention. Mark is known for his empathetic approach and his ability to navigate complex situations with fairness and tact, making him a trusted advisor to both employees and senior management Pain Points: Mark finds it challenging to keep up with administrative tasks while maintaining a strategic focus. He needs real-time insights on workforce analytics, performance metrics, and compensation to make informed decisions. Maria, the HR Data Analyst Role: Maria uses her HR data expertise to uncover insights that help guide key decisions and shape the organization’s direction. As an HR Data Analyst, she’s the bridge between raw data and strategic HR initiatives, expertly translating complex datasets into clear, meaningful reports and dashboards. Maria is known for her keen attention to detail and her ability to align data analysis with the company's goals, making her an invaluable partner to the HR team. Pain Points: Maria frequently gets interrupted throughout the day to handle quick analysis tasks on top of the larger projects she’s involved in. Her workload is heavy, so efficiency is key. Mark and Maria’s Day with One AI Assistant 8:00 AM - Starting His Day Mark starts his day by reviewing his to-do list. He sees an urgent request from senior leadership for a 12 PM meeting to discuss impacts of performance ratings on turnover. He reviews some of his existing Storyboards in One Model and finds a few charts that will be useful. He pins them to a new Storyboard and reaches out to Maria for assistance in completing the analysis. 8:30 AM - Starting Her Day As Maria logs in for the day, she immediately sees three messages from Mark on Slack. One of them is an urgent request: Help prepare a detailed One Model Storyboard on employee turnover rates for a leadership meeting scheduled for 12 PM today. 10:00 AM - Generating Insights in Minutes Using natural language, Maria asks One AI Assistant to display her organization's turnover rate broken out by performance rating and department. One AI Assistant’s intuitive interface ensures Maria can create charts and graphs quickly and easily without training. The visualization is generated immediately and is easy to interpret. With the selections clearly displayed, she’s able to drill through to the employee level data available. Maria confirms that the selections are correct, pins the chart to the Storyboard Mark shared with her, and repeats the process for a number of other breakouts. She also prompts One AI Assistant for areas of the company with the highest turnover and the lowest turnover. She adds these insights to the Storyboard along with a few notes as she preps for the approaching deadline. 11:00 AM - Quick Chat Mark and Maria meet to review the Storyboard they collaborated on. As usual, Mark is impressed with the quality of Maria’s work, especially considering the quick turnaround. He realizes though that they did not include any trend data. Maria reminds him that they can do even better. She asks One AI Assistant for a turnover trend for high performers for one of the key breakouts including a forecast. The chart is generated and includes forecasts years into the future. She pins it to the Storyboard and Mark is ready for the upcoming meeting. 12:00 PM - Strategic Planning Meeting Mark meets with senior management to discuss turnover at their organization. Using the Storyboard he created with Maria, he presents data-driven recommendations on talent acquisition and retention strategies. Senior leadership has more questions about retention in California. Mark isn’t fazed as he opens his One Model instance to leverage One AI Assistant. He's able to find his answer within seconds. He confidently shares results, knowing the insights come solely from his organization's data, ensuring accuracy without hallucinations. 2:00 PM - Crisis Averted As the meeting wraps up, Mark and Maria exchange relieved glances. Thanks to their quick thinking and the support of One AI Assistant, they’ve once again turned a tight deadline into an opportunity to shine. Senior leadership leaves the room with actionable insights, and the company’s HR strategy is stronger than ever. Mark and Maria walk out as the heroes of the day, proving that with the right tools and teamwork, no challenge is too great to overcome. -------------------------------- Request a demo to see for yourself how One AI Assistant can help you work faster, smarter, and empower your entire organization. Experience the future of people analytics today - One AI Assistant is included with all enterprise licenses.
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Featured
8 min read
Sep 10, 2024
Are you as intentional about measuring the value of your data infrastructure and models as you are about building them? In the video below, our Solutions Architect Phil Schrader recently revealed at People Analytics Summit in Toronto the importance of (and some strategies for) using analytics to evaluate the impact of your analytics investments. From leveraging machine learning to track improvements to thinking creatively about integrating predictive models into everyday workflows, you'll gain insights on how to apply analytics to your own analytics. Short on time? We’ve summarized his presentation for you below. The Core Problem: Evaluating Data Investments When we talk about people analytics, we often focus on the tools, processes, and models that drive better decisions. But what happens when we turn that lens inward—when we use analytics to assess the very work of analytics itself? The idea is simple: if we’re investing in building data infrastructure and models, we should be just as intentional about measuring the value of those investments. Anyone leading a people analytics team knows the balancing act. On one side, there’s the pressure to deliver quick insights, the kind that keeps operations running smoothly. On the other side, there’s the longer-term need to build out robust data systems that support advanced analytics. Yet, as essential as these data initiatives are, we often struggle to quantify their value. How do we measure the ROI of building a data lake? How do we ensure that the data we’re collecting today will pay off down the road? Solution: Analytics About Analytics Here’s where we can take a different approach—by applying analytics to our own analytics. The falling cost of technical work in machine learning (ML) has opened up new possibilities, allowing us to embed these tools within our day-to-day operations. Instead of just using ML models for predictions, we can use them as a means to measure how good our data is and how effective our processes are. Essentially, we can start to think analytically about how we do analytics, especially when it comes to creating a predictive model that measures improvements over time. A Concrete Metric: Precision, Recall, and the F1 Score The foundation of this approach lies in the well-known metrics used to evaluate machine learning models: precision, recall, and the F1 score. In brief: Precision asks: When the model makes a prediction, how often is it correct? Recall asks: Out of all the events that should have been predicted, how many did the model actually identify? The F1 score strikes a balance between these two metrics, offering a single number that reflects how well your model performs overall. By tracking this metric, we can gauge the quality of our data and see how incremental improvements—like adding new data sources—translate into better predictive power. This kind of measurement becomes crucial as we think about the future of machine learning and how it integrates into everyday operations. Building Analytics for Growth This method doesn’t just give us a way to measure progress; it gives us a framework to demonstrate that progress in tangible terms. Start with the basics—core HR data like job titles, tenure, and compensation. As you layer in additional data points—learning metrics, performance reviews, engagement scores—you can observe how each new addition boosts your model’s F1 score. It’s a practical way to quantify the value of your data and justify continued investment. The Changing Landscape: Embedding Predictive Models Predictive modeling no longer needs to be a separate, resource-intensive project. As the tools become more accessible, we can embed this capability directly into our workflows. Think of it as using predictive models the way we use pivot tables—regularly, as a quick check to see how well our data is performing. This kind of embedded analytics allows us to experiment, iterate, and find creative ways to leverage machine learning without overcommitting resources. With AI continually reshaping business practices, this shift will allow teams to use predictive models in increasingly versatile ways, driving more efficient decision-making. Beyond Traditional Metrics: Rethinking the Value of Data By adopting this approach, we’re able to ask—and answer—a critical question: How valuable is our data, really? If we can demonstrate that our data is increasingly effective at predicting key outcomes like employee turnover or high performance, we’re no longer just talking about data quality in abstract terms. We’re providing a concrete metric that resonates with stakeholders and gives us a way to collaborate more effectively, whether it’s across HR functions or with external vendors whose data feeds into our models. Looking Ahead: Embracing Innovation as Costs Fall The future of AI and the workplace is advancing quickly, blurring the line between strategic and routine applications. What was once a complex, time-consuming effort will soon be something we do without a second thought. This shift requires a mindset change—being open to ideas that may seem wasteful or unconventional today but could become standard practice tomorrow. The key is to embrace this shift and look for new, innovative ways to use predictive analytics. In summary, by taking an “analytics for analytics” approach, we gain more than just better models—we gain clarity on the value of our data investments. The ability to measure progress in predictive power isn’t just a technical exercise; it’s a strategic advantage that drives smarter decision-making across the board. Not sure where to start? Download Key Questions to Ask When Selecting an AI-Powered HR Tool to get the answers you need. Download Your Buying Guide Now
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3 min read
Sep 10, 2024
One Model was founded around a goal of helping teams tell data-informed stories that lead to brilliant, data-driven talent decisions. By leveraging data and story, we can help teams communicate a deeper understanding of the tangible benefits of diversity, equity, and inclusion initiatives, and how they contribute to the success of a business. Data-informed stories can be a powerful tool for uncovering how the work environment is impacting our employees. Through data, we can demonstrate the positive impact of treating people well, and how this can drive business success. Let’s walk through one of the “classic stories” we hear in HR and people analytics for a fictional organisation called Innovative Enterprise. We’ll start with the story, introduce the data, and then apply One Model’s data-informed storytelling framework to the story to show how our platform easily weaves the narratives together. This is a common story that HR teams are asked to tell around employee experience and the impact that a positive work environment can have on the overall business. Story alone: Within Innovative Enterprise, while we have a diverse workforce, this diversity is yet to permeate our leadership effectively. Our leadership team, although competent and committed, does not fully represent the diverse perspectives present within our broader team. This lack of representation in leadership could potentially influence our culture and engagement levels. Data alone: Internal data at Innovative Enterprise shows that while 49% of our workforce identifies as ethnically diverse, only 15% of our leadership does. Recent industry studies that the people analytics team analysed indicate that organisations with diverse leadership teams outperform those without by 35% in terms of innovation and creativity. Moreover, organisations that boast diverse leadership report a 25% higher employee satisfaction score compared to companies with less diverse leadership teams. Data story: At Innovative Enterprise, the lack of diversity in our leadership team becomes evident. Our internal data reveals that while our workforce is 49% ethnically diverse, only 15% of our leadership reflects this diversity. It's clear we're falling short, and this is a challenge that we share with many organisations across our industry. However, industry data provides a clear directive: organisations with diverse leadership teams are more innovative and creative by 35%. They also report a 25% higher employee satisfaction score, indicating a more engaged and motivated workforce. This compelling combination of our internal situation and broader industry data paints a powerful argument for enhancing diversity, equity, and inclusion at the leadership level. The data provides clear guidance — it's time for us to take action. Ready to learn more This example from Innovative Enterprise demonstrates the power of data-informed storytelling in HR. For more impactful stories and detailed analysis, download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organisation tell compelling, data-driven stories.
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7 min read
Sep 02, 2024
If you're preparing for people analytics, there’s a lot to do before you hire that first data scientist. To build the right foundation for success, there are five important steps you should follow that don’t even involve data, insights, or statistics. Following these steps will help you establish and support an efficient and impactful people analytics practice at your organisation. 1. Find your why Understanding why you're pursuing people analytics is vital to your journey. This not only means identifying the specific business needs that would benefit from a better understanding, deeper insights, or more precise analysis of your workforce, but also exploring the underlying reasons behind those needs. You could start by asking questions like: What are the biggest challenges or pain points we're facing as an organisation? What are the key areas where we could improve our workforce, and how would we measure success? What are the most critical business decisions we need to make, and what do I need to know to help us make them more effectively? What are the specific gaps in our knowledge that we need to fill in order to make better decisions? Without taking the time to find the why for your organisation, you risk getting lost or going off course before you even begin. By finding your why early and holding onto it through the process, this will keep you focused throughout your people analytics journey. 2. Look upstream When starting your people analytics journey, it’s important to remember that the data you’ve generated is only as good as your processes and technology. There’s a flow we like to think about from process to tech to data to analytics. When people analytics teams run into challenges, there’s likely an upstream challenge in one of these steps to address. Begin by examining your processes. Technology is only as good as the process it’s automating, so if your processes are poorly designed and documented, your technology is unlikely to be implemented correctly. Technology should reflect how you want your business to run. If it doesn’t, you’ll likely end up with incomplete or incorrect data flowing out of the technology — making it difficult or impossible for people analytics teams to create value.This is not to say “don’t start on people analytics until the rest is done”. People analytics teams can absolutely provide great value, and some of the best teams out there are scrappy with what they have on hand. This is more of an acknowledgement of the flow and a callout that if you want long-term success of your people analytics team and to unlock that next level of value, you’ll have to address these upstream challenges. A strong people analytics leader will also be able to help you identify and navigate these challenges upstream. So begin by ensuring that your processes are well-designed and documented. Next, double check on your technology implementation and ensure that it matches your processes. Finally, check in on the data. The data ultimately doesn’t lie, so it will tell you if the processes and tech are clean. Doing so will ensure that data flows smoothly and accurately from the technology preparing you for analytics. 3. Address data management Another early focus for starting down the path of people analytics is data management. Without data, there’s not much for people analytics teams to do. It’s the oil to the people analytics engine. We’ve seen a number of teams get started, but then plateau around a lack of good data. At times the resources to fix data problems sit outside of HR, which makes it all the more important to navigate and commit that resource request up front when pursuing people analytics. Making sure your data is accessible is critical, but raw data extraction is also only the beginning. A robust workforce-specific data model, proper data architecture blending your different systems data, and HR-led workforce data privacy and workforce data governance are also part of your people analytics foundation. This may require marshalling what are typically scarce internal resources, capabilities, and priorities from IT or data engineering teams to ensure that your data is clean, systematically organised, and readily analysable. Or you can save those internal resources by working with people analytics platforms like One Model. We were founded to make this upstream challenge easier. We provide named data engineering resources, have experience developing business-specific workforce data models, and provide the data foundation that people analytics teams need to thrive. If you skip this step, you may experience the following problems: Missing data: Without the right data management structure in place, you may find it difficult to extract the data you need for a given project. This can lead to incomplete or incorrect data and difficult analysis. Slow data: Improper data management can leave you with only monthly (or quarterly!) snapshots and that pace just doesn’t reflect how fast your business moves — let alone back-dated changes, which are frequently found in HR. Inability to build predictive models: Data management is critical to building predictive models. To develop predictive models, you need to extract data in a very specific way (e.g. time-stamped changes). It’ll be difficult or even impossible to build accurate and effective models without this proper data management. By addressing data management early on in your people analytics journey, you can avoid these symptoms and ensure that your people analytics initiatives are successful. To learn more, here are five tips for getting HR data extraction right. 4. Set the tone Setting the tone at the top is crucial for demonstrating that data-driven decision making is the way forward. This involves garnering support from your organisation's senior leaders, as well as regular reminders, activities, and actions from the CHRO or HR head. If you’re in a leadership position, setting the standard that data is required for new projects and investment decisions goes a long way. Cultivating a data-minded culture will trickle down from the top, setting a precedent for the entire organisation. Without this high-level endorsement and sustained backing, making significant strides in people analytics can prove challenging. 5. Find help Consider engaging with a seasoned people analytics leader either full-time or as a consultant to spearhead your people analytics initiatives and education within your function. Experienced people analytics leaders, with their unique combination of data analysis skills, HR orientation, ethical understanding, and team management expertise, can provide invaluable guidance. They’ll work to ensure alignment between your analytics efforts and broader business objectives. Remember to also tap into the people analytics community. This strong and enthusiastic network can provide invaluable support. Engage with professionals on LinkedIn, ask questions, and use the expertise of vendors in the space. The team here at One Model is always willing to connect and assist at every stage of your people analytics journey. New to people analytics or ready to enhance your existing program? Either way, our eBook People Analytics 101 covers everything you need to know about establishing a strong people analytics foundation for smarter HR strategies and meaningful change across your organisation.
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6 min read
Aug 27, 2024
We’re celebrating! One Model is an ISO-certified company and has recently completed the latest certification: 27001:2022. What does it mean to be ISO-certified? ISO certification provides voluntary third-party validation that a company's internal systems align with internationally recognized standards for quality and consistency. The International Organization for Standardization (ISO), a non-governmental entity responsible for developing and publishing these standards, ensures that businesses worldwide adhere to best practices. ISO compliance provides a structured approach to identifying, managing, and reducing information security risks. It helps organizations systematically assess threats and vulnerabilities and implement appropriate controls to mitigate them. The Importance and Rigorous Process of Becoming ISO-Certified Achieving ISO 27001:2022 certification is no small feat. This rigorous process involves comprehensive audits, meticulous documentation, and a thorough evaluation of an organization's information security management system (ISMS). It's not just about ticking boxes but ensuring every aspect of data security is up to international standards. This certification demonstrates a commitment to continuous improvement and accountability in managing sensitive information. For People Analytics vendors, being ISO-certified means they are dedicated to protecting your data with the highest level of security. It’s a clear signal that they are serious about maintaining robust data protection practices, giving you peace of mind that your information is in safe hands. Is Your People Analytics Vendor ISO-Certified or Simply ISO “Adjacent”? Don’t be fooled. Your People Analytics vendor may claim to follow ISO 27001 standards or they may even be certified – but with an earlier version (27001:2013). The absence of a current certification may lead you to think that it doesn’t matter, that... - It’s just a cherry on top of your data security, and not all that critical. - Not much has changed in cyber fraud in the 9 years since the previous certification. - If it was really important, they WOULD have it (and maybe even brag about it) The fact is, cyber fraud is ramping up exponentially. Now, simply being certified to the most current standard (27001:2022) may not even be enough. A certification may only cover a single system within their organization. To safeguard security effectively, it's imperative to demand certification that encompasses the entire organizational scope, leaving no room for ambiguity or vulnerability. Who’s Minding the Data? Take it a Step Further with a CISSP While it’s not required for ISO certification, if you’re really taking security seriously, it’s good to know whether or not your People Analytics vendor’s Information Security Officer is a Certified Information Software Security Professional (CISSP), which is the gold standard in cybersecurity certifications. One Model is Now ISO27001:2022-Certified We think passing the rigorous verification process is a big deal and we’re proud to say One Model has recently completed the challenging ISO 27001:2022 certification! And, with One Model, you’ll find that we take your HR data security seriously… As the Information Security Officer at One Model, I’m a certified CISSP. We don’t sell your data. Your data never leaves our company. We have data servers in key regions, like the US, Ireland, Canada, and Australia. Only approved, background-checked, full-time employees have access to your data. Your data never leaves your One Model instance. Explore our infographic: IT security risks in the People Analytics space and how One Model works to limit those security risks. Leading companies like John Deere, Blackrock, Coinbase, Kellogg, and Colgate-Palmolive trust One Model’s cutting-edge analytics to elevate their HR strategies and superior security protocols to keep their data safe. To explore how One Model’s ISO-compliant software can solve your People Analytics challenges and lock down your security concerns, reach out with your questions or request a demo. Connect with One Model Today!
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Featured
4 min read
Aug 26, 2024
What should your first people analytics project be? Many teams start with employee attrition because it has clear outcomes, it has a direct impact on the company, and attrition data is already in your HRIS. So to understand the five steps you need to follow during people analytics projects, let’s walk through an example of how Penelope, a fictional people analytics practitioner, might approach an employee attrition project, from start to finish. Step 1: Define the problem The first step in any people analytics project is to define the problem you want to solve. In this case, the problem is employee attrition. Specifically, we want to understand why employees are leaving the company and what we can do to reduce attrition. "As an HRBP, I noticed a trend of high employee turnover in the company. I began to investigate why employees were leaving and how we could reduce this trend. My goal was to identify the underlying causes of this issue and develop a plan to address it," says Penelope. Step 2: Gather the data The next step is to gather the data you need to analyse the problem. In this case, you'll need data on the employees who have left and their reasons for leaving (if available). This data can often be found in your HRIS, as well as employee surveys or exit interviews. "To gather the necessary information, I dove into the company's HRIS system, as well as employee surveys and exit interviews. I collected data on employee demographics, job history, performance metrics, and reasons for leaving. I made sure to gather as much relevant information as possible to ensure a comprehensive analysis," shares Penelope. Step 3: Analyse the data Once you have the data, it's time to analyse it. There are a variety of statistical methods you can use to analyse attrition data, including survival analysis, logistic regression, and decision trees. But you can also start with descriptive methods. Your choice of method will depend on the nature of your data and the questions you want to answer, and you don’t always need advanced methods. "I took a look at attrition trends across each of the major groups within the company. Using descriptive statistics, I found that some teams were experiencing higher attrition than others within similar business units. I wanted to identify why the attrition rate was high, so I looked for factors that were strongly correlated with attrition," notes Penelope. Step 4: Tell the story After analysing the data, it's time to tell the data story. This is where data visualisation and data storytelling come in. You'll want to create charts, graphs, and other visualisations that help you communicate your findings to stakeholders. You'll also want to craft a narrative that ties the data together and explains what it means for the company. "Using the results from the data analysis, I created charts, graphs, and other visualisations that I could use to communicate my findings to stakeholders. I crafted a narrative that brought my business knowledge into the story and explained the factors contributing to the high attrition rate and the steps we could take to address it. I presented the data and narrative to the company's leadership team," explains Penelope. Step 5: Implement solutions Finally, it's time to implement solutions based on your findings. This might involve changes to HR policies, changes to compensation structures, or changes to management practices. Whatever the solution, it should be informed by the data you've gathered and analysed. "Based on the data and narrative, I recommended changes to HR policies, compensation structures, and management practices. I presented the recommendations to the company's leadership team and worked with them to implement the changes. Over time, we saw a decrease in the attrition rate and an increase in employee satisfaction," says Penelope. Overall, attrition is a great starting point for any people analytics team. It's a universal problem that every company faces, and the data is often readily available. By analysing attrition data, you can gain valuable insights into your workforce and make data-driven decisions that improve retention and reduce turnover. New to people analytics or ready to enhance your existing program? Either way, our eBook People Analytics 101 covers everything you need to know about establishing a strong people analytics foundation for smarter HR strategies and meaningful change across your organisation.
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Featured
6 min read
Aug 14, 2024
One Model is revolutionizing the landscape of people analytics with the introduction of One AI Assistant. Designed to make your people data more accessible and actionable, One AI Assistant uses generative AI to deliver quick and intuitive insights for anyone in your company. What sets One AI Assistant apart is its commitment to transparent AI. We built One AI Assistant specifically to circumvent and control for hallucinations that other AI tools suffer from. It shows its work, enabling your team to make informed, data-driven decisions quickly with confidence. Get Instant Answers with One AI Assistant One AI Assistant embodies the core mission of One Model – we believe your people are your greatest asset. We want to empower you with the insights you need to truly understand your workforce, leveraging all the information you have to enhance better business outcomes and drive success. One Model excels in data orchestration, extraction, and modeling, supported by a team of top-notch data engineers. By integrating One AI Assistant, we've made it easier than ever to access and utilize this data foundation and get answers about your workforce, instantly. Whether you have a simple or complex request, One AI Assistant allows you to interact with your data effortlessly. Receive precise answers accompanied by auto-generated visualizations, through an interface that is both modern and intuitive. Imagine this: Simple Request: "Show me our current headcount" Complex Request: "Show me hires for the past 12 months broken out by gender in the financial services department with a forecast” With One AI Assistant, adding metrics and modifying selections is straightforward. Our user-friendly interface ensures that you can generate insights with ease, enhancing the overall efficiency of your data analysis processes. Empower everyone on your team with self-service capabilities. Creating data visualizations is now as simple as typing a few words, making data analysis accessible to a broader range of users — from generalists to seasoned analysts. Not ready for a full deployment just yet? No problem. Our tools are fully configurable to align with your security and privacy requirements. You can roll them out at your own pace, deploying to the right people whenever you’re ready. Powerful features such as drill through, forecasting, and the ability to pin content to Storyboards enhance the analytical and collaborative experience. One AI Assistant is immediately intuitive, requiring no training, and empowers individuals within organizations to access, visualize, and understand workforce data. This ease of use enhances collaboration and informed decision-making at all levels. Powered by Generative AI The core of One AI Assistant is powered by the sophisticated language understanding of large language models. These models interpret your input, identify key metrics, dimensions, and time selections, and use a vector database to match your requests with your data accurately. This process ensures that the answers you receive are based specifically on your data, not generic knowledge. One AI Assistant is built to prevent hallucinations, delivering only accurate and relevant insights based on your tailored data model, ensuring trust and reliability in every response. Integration Across One Model One AI Assistant's generative AI capabilities are accessible from anywhere within One Model. Whether you're working within a Storyboard or navigating different pages, you can generate charts and insights without disrupting your workflow. This integration enhances the overall user experience, making data-driven decision-making more efficient. Transparent and Secure With One AI Assistant, the query structure is displayed alongside the resulting visualization, providing complete transparency. It adheres to One Model's industry-leading role-based security, ensuring that users only access data they are authorized to see. Ethical and Configurable AI We believe in "ethical AI, with you in control." One AI Assistant is configurable, allowing administrators to manage access and set parameters according to your company's needs. You can control which metrics and dimensions One AI Assistant is able to access as well as which users can access the new feature. Continuous Learning and Improvement One AI Assistant is designed to improve over time. A built-in feedback loop allows users to indicate whether the results are accurate. Users can easily add detailed notes, which will help improve models and inform enhancements. Your Data, Real Answers One Model has always been a leader in data integration, modeling, security, and machine learning for people analytics. With One AI Assistant, we've made these insights more easily accessible to a wider audience. One AI Assistant empowers everyone from HR professionals to team managers to make data-driven decisions based on answers they can trust, leading to better business outcomes. Discover how One AI Assistant can help you work faster, smarter, and empower your entire organization. Experience the future of people analytics today - One AI Assistant is included with all enterprise licenses.
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Featured
13 min read
Aug 14, 2024
For HR teams, especially new people analytics leaders, a hidden danger lurks in the shadows that can significantly hinder success: It's your Workforce data architecture. This danger comes from a substantial blind spot for organizations between HR and IT, making it difficult for new people analytics leaders to be successful and for HR teams to effectively leverage data. Understanding and bridging this gap is essential for unlocking the full potential of workforce analytics, which is increasingly vital in today's data-driven business environment. Troubleshooting the People Analytics Ecosystem When an HR organization embarks on a journey into people analytics, or when a new people analytics leader establishes a team, one of the first tasks is understanding and assessing the HR data landscape. Alongside stakeholder meetings, team assessments, and making the case for the necessary tech stack, evaluating the data infrastructure is crucial within the first 90 days. Initially, teams might try to manage with available system reports and surveys. Those teams end up relying heavily on manual data wrangling, which in turn brings human error, bias, and friction into processes and often involves complex and messy spreadsheets. Maintenance of those manual systems will eventually hold the team back. This approach carries significant risks, as it is prone to errors and inefficiencies. Moreover, if the key person managing these processes goes on leave or resigns, the entire operation could fall apart, leaving the team holding the bag on an impenetrable data model. When it comes to advanced analytics, the main trouble HR finds itself in at these companies is that people analytics teams can’t run on raw data from system reports alone. In order to reach beyond reporting and into analytics, people analytics teams require architected data, which means raw data must be converted into usable metrics and dimensions. An investment in data architecture forms the bedrock upon which advanced analytics and insightful decision-making are built. Access to clean, well-architected data is essential for the success of People Analytics teams. Bridging the “Invisible Gap” in Building a Solid People Analytics Data Infrastructure So the people analytics leader starts their journey: What data do I have, what data do I need, and what technologies produce data across our workforce ecosystem (HRIS, ATS, Survey, etc). Who do I have that can help me? But they quickly face a two-fold political problem: 1. On the HR side Their leaders and peers on the HR leadership team may be just starting to get familiar with analytics, and data architecture is a step beyond that comfort zone. HR education generally doesn’t cover data engineering and, to give that some credit, why should it? Most HR leaders will not need to engage in data architecture conversations. And to that point, most people analytics professionals are even downstream from these conversations or have to learn it on the job, too. There are very few, if any, courses for HR professionals on the nuances of workforce data architecture. Additionally, data engineering for analytics is a unique need specifically and almost entirely for people analytics within an HR team. People analytics regularly centralizes and handles this work on behalf of their HRLT peers, which inadvertently hides this work – and the pain of this work – from their peers. 2. On the IT side One might assume that IT or central data teams could provide the necessary support for people analytics leaders. While this is true in some organizations, the reality often is that IT and enterprise engineering teams, despite their data expertise, lack understanding of the unique nuances of slow-changing dimensions of workforce data and HR processes. That’s why we provide resources to help, like this 5 Tips for Getting Data Extraction Right blogpost. Additionally, IT teams are frequently overwhelmed with demands from various departments such as product, marketing, sales, and finance, making it challenging to prioritize HR-related data projects. The other hard truth is that we are still in a political reality where teams outside of HR don't readily recognize the value or prioritize this work, as we illustrate in The Little Red HR Team: A modern retelling of a timeless classic. So the people analytics leader, who needs workforce data to be extracted, architected, and modeled to do their assigned job, now has a problem. Why this gap is even more detrimental if you’re moving toward AI in HR Navigating Data Architecture Hurdles Securing buy-in, resources, and priority for data architecture work can be challenging, especially when it's often hidden from key teams and not part of the typical job description. Historically, people analytics leaders have faced two main options, each with its drawbacks: 1. Educate and influence campaign To get this work done, the people analytics leader embarks on an extended period of education for both HR and IT to explain what's going on and why they need to spend time, resources, and priority building an analytical data warehouse, not just reports from the core HRIS. This is thankless work trying to upskill and educate teams who do not want to know or need to know about this area to do their day jobs. These campaigns are long journeys. 2. Just get it done The people analytics leader advances into this “invisible work” by themselves or with the team they have, and just tries to get it done. People analytics leaders take the work on, upskilling in data engineering and doing the best they can. This results in a “good enough” but ultimately shaky foundation. And while that’s happening, people analytics has to wait until you have data to work with. So you put your head down and get work done. Unfortunately, when it's done or “good enough,” – and this is the hardest part – no one else will notice. The first option means you lose the critical window of time when new leaders need to show effectiveness. But the second option means you lose visibility and guarantee a long term problem with maintenance. Potentially even more dangerous for a new leader. With both options, success is far from guaranteed. Both HR and IT teams just want you to get your work done; they don't necessarily want to learn about why their current setup of technology is not working. There's good news, though. The vendor landscape supporting people analytics has been evolving to meet this need. How One Model Helps Move the World Forward One Model is uniquely designed to address the 'invisible work' of data engineering and data architecture in people analytics. This was clearly demonstrated in our work with Elastic, a leading tech company. The One Model platform enabled Elastic to streamline their data processes and significantly enhance their people analytics capabilities. Read more about our partnership with Elastic. Data Orchestration One Model stands alone when it comes to the levels of support we offer for data architecture. Our data orchestration layer is One Model’s crown jewel within the product suite. One Model seamlessly extracts, transforms, and loads your data into a secure, tailored data model within our People Data Cloud (effectively, a sophisticated data warehouse built specifically for your people data). The automation we establish ensures that your data is consistently updated daily without the need for manual intervention. By eliminating the need for manual data loads or loading files yourself, we provide a reliable and efficient solution for maintaining up-to-date, high-quality data for analytics. This was a game-changer for Elastic, enabling them to maintain accurate data without the burden of manual updates. Additionally, our platform features direct connectors that go beyond mere extraction of raw files or reports, providing fully modeled data ready for analytics. Whether dealing with flat files or complex data sources, our system integrates and unifies data into a cohesive analytical model, which updates daily, and streamlines your access to data. Data Engineering Support And you're not going at this alone or upskilling your team with additional expensive training to make this happen. One of the biggest reasons I chose One Model when I was a buyer in people analytics was that One Model provided data engineering support as part of the subscription. Named resources support your team, but above and beyond that support, the One Model platform and One Model team members maintain the data pipelines. No more calling IT teams that don't prioritize HR and no need to hire unique and expensive resources for data engineering. With One Model, you will have a partner you can call who not only picks up the phone, but who cares about your success in this “invisible” space. In Elastic's experience, this support allowed their team to focus on strategic analytics rather than getting bogged down in the technical details of data engineering. Seamless Integration from Connection to Dashboard Most importantly, this orchestration happens quickly and securely. You don't have to spend months or years trying to unlock your data. We can extract and create a tailored data model for your HRIS rapidly – from connection to dashboard! This quick implementation enabled Elastic to quickly transition to leveraging high-quality analytics, accelerating their time to value, and enhancing their overall data strategy. One Model stands as the global leader in our space, uniquely positioned among people analytics providers as the premier partner for data architecture. Don’t feel like you have to navigate the complexities of data architecture alone. Partner with One Model and leverage our expertise to unlock the full potential of your workforce data. Reach out to us today to see how we can transform your data management and analytics capabilities. Glossary of Terms When exploring the complexities of data architecture and engineering, it's helpful to familiarize yourself with key terms frequently encountered in this field. The following glossary provides a concise overview of essential concepts and terminology: Data architecture: The overarching strategy, rules, and principles governing the collection, organization, transformation, and storage of data in a specific environment. Raw data: Unprocessed digital information extracted from a technology, often in a format that's difficult to understand without processing. Data integration: The process of combining data from different sources and providing users with a unified view of these data; sometimes referred to as ETL, which is to Extract data from a source, Transform it to fit your needs, and Load it into the end system. At this point, it becomes an analytical data model (see #6). Trending data: Data showing changes and patterns over a specified period of time, often used to predict future events or behaviors. Data warehousing: A large store of data collected from a wide range of sources within a company and used to guide management decisions. Analytical data model: A set of interconnected tables (or fact tables) ready for use in analytics. (a.k.a. a Galaxy schema) Unified data model: A framework that unifies multiple data types from different sources into a consistent and universally accessible format. Download a resource for your IT team that helps explain why they should care about people analytics. Why Tech Leaders Prefer One Model's People Analytics Platform Download today
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5 min read
Aug 13, 2024
A well-crafted data-informed story can effectively influence decision-making, foster understanding, and drive meaningful change within the organization. A data-informed story blends the art of storytelling with data-driven insights, creating a compelling narrative that resonates with the audience and inspires action. Here's a framework for how to develop data-informed HR stories: Business Objective: Setting the Stage Every compelling data-informed story begins with a clear business objective. It's essential to know what you want to convey and the actions you want to inspire from your audience. Defining your objective gives direction to your story, shaping its structure and maintaining its focus. A well-articulated objective ensures your story remains purposeful and impactful, driving the narrative towards your desired outcome. “Using people data to get to a scientific insight is only half the battle. If you can't step back and crisply describe your findings in terms of business impact, you quickly lose the room, lower credibility, and break trust with business leaders.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Evidence: The Backbone of Your Story Your story's credibility stems from its findings: both data evidence and the story context. Data Evidence: Collect and analyse data pertinent to your objective. This data acts as the backbone of your story, supporting your narrative and revealing valuable trends, patterns, and insights. It's the facts and figures that make your story believable and persuasive, reinforcing your arguments and enhancing your story's validity. Story Context: Context adds depth to your data, making it meaningful and relevant. Explain why your data matters, its relation to broader organisational objectives, and its direct impact on your audience. This context helps your audience comprehend the data's significance, allowing them to connect the dots between raw data and its implications. Visualization: Bringing Your Data to Life Visualising your data helps to clarify and accentuate your key messages. Rather than presenting raw data or lists, craft clear and engaging visual representations of your data. This could involve charts, infographics, or diagrams, which enable your audience to quickly grasp the information and easily identify the patterns or trends you're emphasising. Narrative: The Art of Engaging Your Audience Narrative is the act of weaving together data and insights into a compelling story that resonates with the audience and inspires action. By using an engaging narrative, relatable examples and analogies, and emotional appeal, HR professionals can effectively communicate the human impact of organisational decisions and drive meaningful change. “To infuse more storytelling into People Analytics, understand the business and people context, use narrative techniques and visualisations to present data engagingly, and go beyond data by exploring the human factors driving it. Enhancing storytelling in this field can significantly boost its impact on business outcomes.” — Tony Truong, Vice President of People Strategy and Operations, Roku Engaging Narrative: To captivate your audience, weave your data and insights into a compelling narrative. Ensure your story flows logically, featuring a beginning, middle, and end, each part reinforcing the key message you wish to convey. Relatable Examples and Analogies: Examples and analogies act as bridges between complex data and familiar concepts. By relating your data to real-life scenarios or recognisable concepts, you make it more accessible and understandable for your audience, making your story more relatable and engaging. Emotional Appeal: The magic of storytelling lies in its ability to evoke emotions. Incorporate elements that resonate with your audience on an emotional level. This could involve personal anecdotes, inspiring stories, or connections between the data and the organisation's values and goals. “People Analytics insights have an easier path to landing as a compelling story if quantitative findings are combined with qualitative findings. Pulling anecdotes from HR and non-HR leaders, managers, and employees in your business lines is a validating and powerful storytelling device.” — Ian O’Keefe, Head of Talent Analytics and Data Science, Amazon Interactivity: A Living, Breathing Story Data stories are not static monologues but dynamic dialogues. Build your stories in a way that allows you to be prepared for follow-up questions and additional requests. Consider building your data stories in platforms where you can treat them as living documents, flexible and adaptive, fostering interactivity and ongoing engagement. This approach will enrich your narrative, keeping it relevant and resonant over time. Action: The Impetus for Change The goal of any data-informed story is to inspire action. Conclude your story with a clear call to action, outlining what steps you want your audience to take based on the insights presented. This crucial step ensures your story doesn’t merely inform but also drives engagement, leading to tangible change. Ready to learn more? Download our eBook Why Data-Informed Storytelling Is the Future of HR to explore additional examples and learn how One Model can help your organization tell compelling, data-driven stories.
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Featured
7 min read
Aug 08, 2024
Richard Rosenow, One Model’s VP of People Analytics Strategy, spoke at People Analytics World in London this year on how the People Analytics role has evolved. Watch the video or read our "Cliff’s Notes" below. Richard Defines People Analytics People Analytics is a multifaceted field that encompasses many things. So it’s helpful to break it down into three key components: the Community, the Act, and Function. Community: The community is the heart of People Analytics. It centers People Analytics as a movement of people who want to make the world of work better with data. The Act: The use of data to support workforce decisions. Everyone in the business participates in the act of People Analytics, including managers, leaders, and HR professionals. This practice has been around informally since the 1940s, highlighting its longstanding importance. Function: The formalization of People Analytics as a business unit has been around for about 15 years, with pioneers like Jeremy Shapiro and Tom David Port leading the way. This function continues to evolve, adapting to the changing needs of the business world and supporting data-informed workforce decision-making. What is People Analytics? Learn more. Overview of the People Analytics Function Richard's experiences in the field of People Analytics have given him an inside look. One unique aspect of People Analytics he points out is the absence of a centralized governing body. This lack of a formal structure allows for continuous growth and evolution. It’s important to give ourselves and each other grace as we navigate and develop this dynamic field. Perhaps for the same reason, People Analytics faces several common challenges, including lack of budget, data acquisition and quality issues, resistance to change, and disconnection within the company. Understanding and addressing these challenges is crucial for the continued growth and success of People Analytics. One critical aspect of overcoming these challenges is understanding and connecting within what we call the People Data Supply Chain. By improving visibility and connectivity across different levels with HR, we can address many of the problems that arise. People Data Supply Chain One of the first tasks of People Analytics is finding usable data, which means reaching upstream. The quality of the data inevitably leads to a focus on technology. As the People Analytics leader moves into technology, a lack of standards in org structure are often revealed. And ultimately, at the very top, we see inconsistencies in strategy. If we don't decide what we're going to do and what we're going to do well, how does the operation know what to build? How do we set up our tech? How do we get our data out? And how do we do analytics? Besides the problem of unactionable data, inefficient and disconnected functions and disrupted data flow within the supply chain creates friction and politics. Connecting the Functions As the first one in People Analytics to point out this infrastructure, its limitations, and its opportunities, Richard stresses that the sequence within the People Data Supply Chain is crucial. Often, there is good visibility above but poor visibility below, creating disconnects. Understanding this sequence and ensuring smooth transitions between levels can significantly reduce problems. Integrating these functions can lead to smoother operations and more effective people analytics practices. And a well-established data supply chain is especially imperative before implementing generative AI in HR. Emerging Role: Workforce Systems Leader The variety of job titles in People Analytics is staggering; Richard has, in fact, identified over 2,600 unique titles. But In the midst of all this change, he has seen a new position emerging that combines people analytics with tech, ops, and strategy. Currently called by many names, this workforce systems leader reduces politics and provides a viable alternative to reporting directly to the CHRO. This new role offers an excellent career path for People Analytics leaders. It allows them to leverage their unique insights and experiences to drive meaningful change within their organizations. Moving Forward As we continue to learn and grow in the field of People Analytics, Richard reminds us that it’s crucial to be kind to ourselves and each other. Sharing insights and experiences within our community will drive our collective progress. Considering the People Data Supply Chain is essential for effective people analytics practices, we’ll be releasing an in-depth exploration of the topic soon. If this resonates with you, let’s continue this important conversation. Together, we can shape the future of People Analytics and drive meaningful change in our organizations. Speaking of the future, Richard Rosenow covers these timely topics in greater depth in the webinar below. Take a listen!
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Featured
6 min read
Jul 30, 2024
To say that HR is undergoing significant transformation is quite an understatement. But in the shadow of AI/ML, people analytics, and other massive splashes, two familiar foundations are shifting: HR team structures and what HR teams are focusing on. Here's a flyover of what's at stake. 1. Evolving HR Team Structures HR team structures are evolving to bridge traditional functions with analytics, technology, and strategic planning. It's important to know what's changing and how your business can adapt: Impact of Layoffs - Layoffs, especially in tech, force HR teams to rethink their strategies. Some companies downsize, while others use this time to attract top talent, leading to more diverse and adaptable teams. Recommended Approach: Use layoffs as an opportunity to reassess and restructure your HR team to align with new team focuses (below). Focus on bringing in diverse skills and expertise to create a more resilient and adaptable team. Optimal Team Size - There’s a growing belief that HR teams can be more effective with a smaller, well-defined team size. Bigger isn't always better; the right team size can enhance efficiency. Recommended Approach: Evaluate your organization’s specific needs to determine the optimal team size. Prioritize quality over quantity to build a lean, efficient team. Platform Approach - Modern HR platforms are reshaping team structures by automating routine tasks and streamlining workflows. This shift allows HR teams to focus more on strategic insights and less on manual processes. Recommended Approach: Invest in comprehensive HR technology platforms that offer automation and integration capabilities. This can free up your team to focus on strategic tasks and improve overall efficiency. New Emerging Roles - At the same time that some roles are becoming redundant or obsolete, new ones are forming to oversee or bridge gaps in new processes. We're seeing people analytics leaders morph into entirely new roles that span across HR functions. This cross functional people analytics position goes by many names, but we're calling it Workforce Systems Leader. Recommended Approach: Stay adaptable, proactive, and informed. Embrace emerging roles like the Workforce Systems Leader to optimize your HR processes and keep your organization at the forefront of industry trends and advancements. Joining a people analytics community can be very helpful in the midst of ongoing evolution. Stay tuned as we address the implications, functions, and ongoing shifts of roles in this industry. 2. Shifting HR Team Focuses As team structures change, so do their priorities. HR must now be focusing on three key areas: data infrastructure, productivity analytics, and skills and workforce planning. Data Infrastructure - A strong data foundation is crucial for advanced analytics and AI. Efficient data management helps HR teams create actionable insights that drive business forward. Recommended Approach: Invest in advanced data management tools and provide training for HR staff to ensure high-quality data and effective use of analytics. Productivity Analytics - The shift to remote and hybrid work has made productivity analytics essential. HR needs to measure productivity accurately and understand what influences it, especially in new work environments. Recommended Approach: Implement productivity tracking software and regularly analyze the data to refine remote work policies and improve employee performance. Skills and Workforce Planning - Integrating skills data into workforce planning is becoming vital. HR must understand the impact of specific skills on workforce dynamics and align this knowledge with company goals. Recommended Approach: Conduct a skills inventory and use advanced workforce planning tools to align skill development initiatives with the company’s strategic objectives. It's not necessarily easy, but by embracing these and other changes we've identified this year, HR departments can improve their effectiveness, foster collaboration, and drive significant business outcomes. Ready to get ahead of these shifts and redefine the impact of HR in your organization? Download this new resource today to take a deeper dive into all 6 of this year’s top emerging trends for people analytics.
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5 min read
Jul 22, 2024
Greenhouse is a robust applicant tracking system, but with the flood of data that gets generated, information about your candidate pipeline, recruiter efficiency, and conversion rate can get lost. There are foundational graphs and reports available out of the box. But many organizations need more highly-advanced analytics to drive talent lifecycle insights. That’s why One Model created Advanced Analytics for Greenhouse, an innovative offering that empowers users to unlock the full potential of their Greenhouse data. What is Advanced Analytics for Greenhouse by One Model? We've created this analytics service specifically for Greenhouse customers to help you get the most out of your investment. From automated data extraction, to insight-rich visualizations, to robust querying and reporting, everything in the service was designed by Greenhouse experts for Greenhouse users. We provide a Quick Start deployment, broad user support library, and a complete self-administration toolkit with a low cost of ownership. Advanced Analytics for Greenhouse helps you answer critical questions, including: What is the average application count per job? What is our gender diversity in applicants and offers? How long does it take to make an offer? What is the forecast for opened and filled jobs? How many open jobs do recruiters have? Why choose One Model’s Advanced Analytics for Greenhouse? Greenhouse helps people-first companies hire for what’s next by powering all aspects of attracting, hiring, and onboarding top talent. And One Model offers an end-to-end, flexible recruiting analytics platform that extends Greenhouse reporting beyond the basics. By combining the power of both Greenhouse and One Model, here are 7 incredible benefits users can expect from Advanced Analytics for Greenhouse: 1. Efficient Deployment Using our flexible people analytics platform and extensive knowledge of Greenhouse software, we’ve designed a Quick Start deployment process to get you up and running quickly. 2. Automated Updates and Managed Data Pipelines Say goodbye to worrying about data updates. One Model provides automatic data refreshes to ensure users have latest information on their recruiting outcomes. Get notifications when your updates are processed successfully. 3. Ensured Security Data security is paramount. With Advanced Analytics for Greenhouse, administrators can rest assured that their data is safe as it moves through our secure data platform, backed by robust security protocols and ISO Certification. 4. Full Administrative Control Your Advanced Analytics for Greenhouse administrators get access to an easy-to-use and secure interface to control user set up, define user capabilities, and manage data access rights by department or metric. 5. Data-Driven Storytelling Our analytics solution is feature-rich but not complicated. It allows users to go beyond reporting to develop and deliver visualizations which answer questions that matter. They can design, create, and share information with their stakeholders with ease. 6. Hidden Patterns Revealed One Model's exploratory data toolkit enables users to delve deep into data, uncovering valuable patterns and trends across time that drive recruitment success. You can answer complex questions through drag and drop functionality without coding. 7. Competitive Edge Gained It’s not easy to find and hire the right talent, but with One Model’s Advanced Analytics for Greenhouse you can gain deeper recruiting and hiring, giving your company a competitive edge in a challenging labor market. Unlock New Horizons of Success If you want to revolutionize your talent acquisition analytics and gain powerful insights, Advanced Analytics for Greenhouse is the answer. It's an opportunity to take your recruiting game to the next level, ensuring your HR and people analytics teams have the data-driven tools they need to succeed. Want to learn more? Watch our demo and explore this page to learn more about Advanced Analytics for Greenhouse. Or fill out the form below to request more information.
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Featured
4 min read
Jul 11, 2024
Have you been tasked with proving the ROI of your people analytics program? Start here. Traditional return on investment (ROI) calculations often fall short in capturing the full value of people analytics. While efficiencies and cost savings are important, they represent a narrow view of people analytics' potential and value. If you've been asked to defend your people analytics business impact, you must start with a broad approach before diving into the weeds. These 3 mindsets are just the beginning, but they're critical. 1. Understand the Limitations of Traditional ROI Metrics Traditional ROI calculations typically focus on two components: estimated savings through system efficiencies and reductions in attrition, or faster time-to-fill job postings. While these metrics are useful, they can be misleading. Establishing a direct cause-and-effect relationship is tricky. For example attributing savings from reduced attrition doesn't tell the whole story, especially in a volatile job market. Additionally, measuring people analytics effectiveness may require factoring in the cost of implementing advanced technologies. So it's important to reinforce that the value of people analytics infiltrates the entire workforce experience and efficiency, which should not be measured strictly in financial terms. 2. Embrace a Holistic Perspective, But With a Laser Focus The mission of people analytics is to foster continuous improvement in talent decisions, leading to better organizational outcomes. So people analytics should be evaluated based on its ability to drive better talent decisions across the organization. This broader perspective encompasses not only financial outcomes, but also benefits various stakeholders, including employees, customers, and the community. By focusing on the overall impact on organizational effectiveness and stakeholder satisfaction, people analytics can be seen as a critical driver of long-term success. This approach encourages investments that enhance the quality of talent decisions and support the organization's strategic goals. 3. Introduce a Simplified Value Model A more practical and effective approach to measuring people analytics value is through a 3-pronged framework: Utilization: Tracks how often people analytics content is used. Leaders regularly engaging with people analytics deliverables, such as dashboards and reports, indicates that members of your team want and are finding value in workforce data. User Level: Assigns high value to senior leaders. If a CEO frequently uses a workforce dashboard, it's likely delivering valuable insights that inform decision making. Tracking engagement levels across different user groups can highlight which tools are most effective and where improvements are needed. Deliverable Level: Evaluates the potential impact of the people analytics content by measure outcomes and decisions influenced by these deliverables. For example, a report that leads to a successful strategic initiative demonstrates high value. By focusing on key users and high-impact deliverables, this model ensures people analytics teams align and prioritize their efforts to meet organization needs. We're Here to Help Of course, these are just the beginning steps in the complex task of assessing the effectiveness of your people analytics program. If you're ready to dive into the specific metrics and tools that will help you make a solid case for people analytics based on data, we're here to help. Download our comprehensive guide, Measuring the Value of People Analytics. You'll discover the various lenses you need to look through when calculating people analytics ROI in general, as well as specific formulas for key metrics. Plus, you'll see how we calculate the value of a small people analytics portfolio based on the value-utilization framework. Get the Equations and Key Metrics You Need:
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Featured
5 min read
Jul 11, 2024
People data is the lifeblood that fuels insights and drives strategic decisions. Yet, for many leaders, extracting meaningful data from complex systems like Workday can be a daunting task. One Model's Workday Connector is designed to turn this challenge into an opportunity, providing a powerful solution that stands out in the crowded market. Here's why it’s a game-changer for technical people analytics leaders. The One Model Advantage: Beyond Brute Force At its core, our Workday API Connector is built on a deep understanding of the intricacies of Workday. Unlike competitors who might rely on inefficient methods—such as pulling data for every employee every day—One Model has developed a sophisticated approach that is both clever and efficient. Intelligent Data Retrieval With a brute force approach querying a year's worth of data for a single employee requires 365 requests to the Workday API. For a 1,000 employee company this means to get a full year's data will require 365,000 API requests. Workday’s API returns data in large, complex XML files and API requests can take seconds to receive a response. For the 1,000 employee company, even if the API responds to every request in 1 second, it will take over 4 days to pull all the data from the Workday API. This brute force method does not scale and is not practical, especially for larger enterprises. Our solution? We focus on significant data change points, intelligently identifying the moments when meaningful changes occur in an employee's record. This approach not only reduces the volume of data processed but also ensures that we capture the most critical updates. The Self-Healing Data Model: Scalability and Accuracy One Model’s unique self-healing data model is a standout feature that ensures accuracy and consistency in your analytics. Here's how it works: Intelligent Identification: By leveraging our deep understanding of the nuances of data locations and changes, our connector identifies and extracts only the necessary data points. This minimizes the load on Workday’s API and speeds up the data retrieval process. Error Detection and Correction: Our system automatically detects discrepancies and back-dated changes, correcting them without manual intervention. This self-healing capability ensures that your data remains up to date and accurate, even if historical changes are made. Dynamic Processing: The connector dynamically adapts to changes in the Workday API, ensuring continuous, reliable data extraction without interruption. Comprehensive Data Support: From Raw Workday Data to Analytical Models One Model goes beyond mere data extraction. We transform raw data into analytical models, providing actionable insights rather than just raw numbers. Our approach integrates custom fields and user-defined reports, ensuring that even the unique aspects of your data are captured and analyzed. Integration with Custom Reports For those unique data points that aren't covered by standard API calls, One Model supports the integration of custom reports. Customers can create custom reports in Workday, which our connector then pulls and integrates into the overall data model. This flexibility means that no piece of data is left behind, giving you a comprehensive view of your workforce. Unmatched Support and Stability Our Workday Connector isn't just a tool; it's a platform-based service. Through the platform, we offer continuous monitoring, maintenance, and support to ensure your data pipeline remains robust and reliable. Beyond the platform, our team is on hand to address any issues, making sure that your focus remains on deriving insights, not on troubleshooting data pipelines. Handling Workday Data Updates with Ease Workday’s frequent updates can pose challenges, but One Model’s connector is designed to handle these seamlessly. By using versioned API endpoints and dynamic data processing, we ensure that changes in Workday’s data model do not disrupt your analytics operations. Why Choose One Model? In a market where many solutions promise easy data extraction but fall short on delivering comprehensive, scalable, and accurate data models, One Model’s Workday Connector stands out. Here’s why: Scalability: Efficient data retrieval methods that scale with your organization. Accuracy: Self-healing models that ensure data integrity. Flexibility: Integration of custom reports and fields. Support: Continuous maintenance and monitoring from a dedicated team. We have many customers and current prospects that have come to us to solve their challenges in accessing, obtaining, and maintaining a historic data load from Workday. With our Workday Connector, you get more than a Workday data export – you get it in a form that drives meaningful, actionable insights. Unlock the full potential of your people data with One Model. Connect with us today or download our Workday People Analytics guide to learn more about our connection to Workday and how it can transform your analytics capabilities.
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Jul 02, 2024
Effective workforce listening is critical for HR professionals. Listening at scale involves gathering and analyzing data from various channels to understand the workforce better. As a key listening tool, employee surveys provide valuable insights into employee sentiment, behaviors, and overall satisfaction. However, transforming survey data into actionable outcomes can be challenging. This is where One Model’s new Qualtrics API connector comes into play. One Model’s Qualtrics API integration simplifies survey data extraction and analysis by streamlining the consolidation and mapping of survey information with other HR data for comprehensive analysis and strategic planning. Simplify Data Acquisition with Unified Data Models One of the primary benefits of the new Qualtrics API connector is its ability to simplify data acquisition through a unified data model. Traditionally, organizations have struggled with the laborious task of mapping varying survey questions and consolidating data for meaningful analysis. The new API reduces the number of manual steps and potential for errors. Integrate Survey Results with Key HR Metrics The Qualtrics API integration enables users to integrate survey results with key HR metrics. By doing so, it facilitates advanced analytics and strategic planning across different departments and time periods. This integration provides a holistic view of employee engagement and performance, allowing organizations to understand the impact of HR initiatives on retention and performance over time. Enhance Employee Engagement and Performance The new API connector plays a crucial role in boosting employee experience by providing deeper insights into employee sentiment and behaviors. By integrating survey metrics with HR metrics, organizations can develop targeted engagement strategies, enhance the effectiveness of HR initiatives, and ultimately strengthen company culture. This comprehensive analysis helps in identifying areas for improvement, driving stronger performance, and increasing employee retention. Key Benefits Save time: Extract and prepare data in fewer steps. Create a better culture: Support personalized employee experiences and strategic planning by using detailed insights from integrated data. Go beyond surface-level data: Gain deeper insights into engagement and performance with machine learning and statistical analysis. For a deeper understanding of the integrated framework for workforce listening, explore One Model’s comprehensive approach that includes conversations, surveys, and systems data in their blog post here.
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6 min read
Jul 01, 2024
How does Human Resources help a business succeed? Like it or not, success in today’s organizations hinges on creating exceptional employee experiences. That means HR holds the key to achieving this critical objective. In this Hacking HR podcast episode, Enrique Rubio and Richard Rosenow, our VP of People Analytics Strategy, explored how HR data can transform workplaces into more human-centered environments. Yet, many HR professionals grapple with data anxiety for a variety of reasons. Listen in on their conversation or enjoy our “Cliff’s Notes” to learn how HR and people analytics professionals can overcome these challenges and embrace a data-driven approach to focusing on people. Why Building a Human-Centered Workplace Requires Data Enrique: We’ve been investing in conversations about empathy, kindness, compassion, feedback, mental health, wellness—all things that create a human-centered workplace. How can we implement these values in the workplace using data? How do we measure that it’s working well? Richard: Data has transformed HR’s ability to listen and engage in meaningful conversations at scale. Historically, HR professionals excelled at listening, but data now allows us to listen to larger populations effectively. For instance, in a company of four thousand employees, it’s impossible for leadership to talk to everyone personally. Data helps us understand and address the needs of employees by identifying patterns and insights that we might miss otherwise. It’s about listening at scale and making informed decisions based on those insights. Overcoming Data Anxiety Enrique: There’s a sentiment among HR professionals who feel they joined the field to work with people, not to dive into data, math, and technology. How do you address these concerns? Richard: The good news is that the technical burden on HR is decreasing. With advancements like ChatGPT, HR professionals don’t need to become data engineers. These technologies handle the heavy lifting, allowing HR to focus on strategic and consultative roles. Learning basic data literacy and understanding how to use data effectively is crucial, but the need to learn complex technical skills like SQL is diminishing. Today, the goal of successful human resource management is to leverage technology to enhance HR’s core strengths in understanding and supporting people. Real-Life Impact of Data in HR Enrique: Do you have any examples where data truly delivered value in creating a human-centered workplace? Perhaps looking into absenteeism versus engagement, or something similar? Richard: One memorable example is from my time as an HRBP for a large retail population experiencing high attrition. We collaborated with a professor researching job embeddedness, a measure of how well employees fit into their roles and communities. By running surveys before and after implementing a targeted program, we were able to decrease attrition significantly. This experience highlighted the power of using data to design effective HR programs and measure their impact, reinforcing the importance of HR success metrics. Surprising First Steps Enrique: It can be challenging to know where to start with integrating analytics into HR practices. What would be your first steps? Richard: Focus on connection and confidence. Start by making connections between HR metrics and business outcomes. Understand how HR activities impact operational results and find ways to measure these connections. Additionally, build confidence in your data. Reliable data allows HR to make informed decisions and advocate for necessary changes. At OneModel, we help HR leaders build unified data models, providing the confidence needed to understand and drive business success. Identifying HR Success Metrics Enrique: One common issue is investing time and resources into HR projects without setting up indicators of success. How can HR professionals ensure they have the right indicators? Richard: It’s crucial to set up indicators of success early on. Engage with analytics teams from other departments, if needed, to establish these indicators. While measuring complex human aspects like well-being can be challenging, finding proxy indicators and triangulating data can provide meaningful insights. For example, asking employees if they have a best friend at work can be a good proxy for workplace happiness, which can be linked to engagement and productivity. How One Model Helps Successful human resource management involves combining data insights with a deep understanding of human behavior, allowing HR professionals to develop programs that enhance employee satisfaction and business performance. One Model makes this possible by enabling listening at scale and efficiently providing deep data insights never before available. We enable HR teams to turn data into meaningful “stories” that drive action and growth. Want to focus on people not data? Learn how to tell better data stories with One Model.
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6 min read
Jun 18, 2024
David Green, a powerhouse in the people analytics world and a wonderful friend, is celebrating a major milestone, and the whole people analytics community is here for it. Congratulations, David Green, for 10 years of inspiring people analytics professionals with your Data Driven HR Monthly Newsletter Why Not Listening to David Green Should Carry a Health Warning Richard Stein, Chief Growth Officer at Amazing Workplace, recently observed on LinkedIn: “There is a reason why David Green is #1 and not listening to him should carry a health warning!” This statement captures David's impact on the market. David Green is not merely a renowned expert in people analytics, data-driven HR, and the future of work; he stands as one of the most influential figures in the HR community today. Throughout his career and in his latest role as Executive Director at Insight222, David has helped thousands of practitioners find our space of work and supported hundreds of global member organizations in creating value and enhancing employee experiences through the development and application of people analytics. David’s influence spans numerous platforms, reaching hundreds of thousands across his channels: David Green on LinkedIn (go follow him now!) MyHRFuture blog Digital HR Leaders Podcast Digital HR Leaders YouTube Channel Data-Driven HR Monthly Newsletter Excellence in People Analytics, a book co-authored with Jonathan Ferrar A Decade of Insights This month, David celebrates a significant milestone: 10 years of producing his popular newsletter, a roll-up of at first annual, then quarterly, and, as of late, monthly takeaways and links to the best articles and content coming out across people analytics. The Data Driven HR Monthly newsletter has grown into a media empire of sorts and a critical resource for the HR and people analytics community. Each issue delves into the latest trends in people analytics, digital HR, and the future of work, providing a curated selection of noteworthy articles, research findings, and practical advice from industry leaders. His inaugural roll-up on LinkedIn, "The 20 best HR Analytics articles of 2014", is still a must-read. It continues to hold true as a Who's Who of leaders changing the world of people analytics today, and many of the articles highlighted there are relevant a decade later (for better or for worse!). The newsletter's consistent engagement highlights how readers from across HR and beyond find value in his insights to stay informed and drive organizational transformation. It stands out for its comprehensive coverage and its role in fostering a well-informed and forward-thinking HR community. Noteworthy Mentions Green has been recognized with several notable awards and accolades in the field of people analytics and HR. Some of his key recognitions include: The Top 10 HR Influencers of 2024 (HR Cap, January 2024), ‘The 100 most influential people in HR’ (The HR Weekly, January 2021), and for the third year in succession, the ‘Top 100 HR tech influencers’ (HR Executive, May 2021). Additionally, we included Ferrar and Green’s book Excellence in People Analytics in our One Model Virtual Library (our recommended reading list) and were honored to be mentioned in it. To the Future David Green's contributions to the field of people analytics are lasting and foundational. David has shaped the way organizations and leaders across our field harness data to enhance human resources and overall workplace efficiency. He has made the world of work a better place through his efforts. As the HR world continues to evolve, we look forward to many more years of his valuable insights.
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6 min read
Jun 03, 2024
Anyone who analyzes data knows there's always a need to drill into reports to answer the questions that pop up. One Model is the only people analytics platform that allows you to drill into literally anything and everything, as if your siloed enterprise data sources were a single source of truth. Working with Metrics, Dimensions, and Time Explore is a powerful tool designed to help you perform powerful ad-hoc analysis on your One Model storyboards, reports, and visualizations. Here's a quick look at how the Explore tool works. Select Your Metrics Metrics are quantifiable measures used to understand the results or outcomes that you observe in your business. The Explore tool presents the entire list of metrics available to you based on your organization's metrics library and the access permission associated with your user profile and your group/team membership. You can add and remove metrics by dragging and dropping them from your metrics library to your metrics selection fields. Your metrics library contains all of the direct and derived values that are used to tell the stories hidden within your data. To learn more about how metrics are established in your One Model instance, check out this article or this video. (You may need to login with your One Model account to view Help Center content.) Pick Your Dimensions Dimensions are attributes or categories by which data can be grouped. Dimensions organize data into meaningful sections for comparing. For example, in a turnover report, dimensions could include rank, business unit, performance rating, and so on. To sub-group your data even further, you might want to add pivoted dimensions, which help you compare groups by more than one attribute. One Model's Explore tool allows you to drag and drop any number of dimensions into your report to see an analytical picture with more detail. Mind Your Time Model Time modeling is perhaps the trickiest and most important activity that happens on the One Model platform. Since time is a constant, your data analysis depends on the most comprehensive coverage of observable and measurable events for analyzing data over different periods. In theory, time subdivides infinitely. But in practice, most analysts and decision makers prefer to view time within a standard set of available lenses such as days, months, quarters, and years. But since months, quarters, and years can have different numbers of days within them, it is critical to getting time right to understand your business in the most accurate way possible. It's important for these cumulative measures to "add up" or "sum to the right number" when aggregated (or drilled through) at scale. It's equally important for data about events to be captured at various time intervals. For example, a group of employees who are currently high-performing rock stars may inform a decision today about high performers. But in reality, many of those rock stars may have been groupies in the past. One Model has no peer when it comes to the most effective application of time series analysis. Here's why. I created the Sankey diagram below with fake data to show a point. Observe how none of the more than 4000 high performers at the end of 2021 remained high performers at the end of 2023. So any analysis conducted in 2024 that uses the pool of 2023's high performers to infer multi-year trends would be an incomplete and possibly flawed analysis of the company's high performers. Most other approaches don't account for the question of "how it looked" in the past. Explore Explore's Unrivaled Speed to Insight Your organization needs the most accurate and current information to make the most informed talent decisions. The Explore tool is one of many keys to telling the stories within your data. Approachable & Intuitive One Model's Explore tool features a professional-class user interface designed to cater to both casual and highly technical users. This balanced design ensures that casual users can easily navigate and utilize the tool without feeling overwhelmed, while technical users have access to advanced functionalities and customization options. The interface’s adaptability fosters a productive environment for all users, enabling them to swiftly uncover insights and make data-driven decisions. Consistent Metrics Definitions Paired with Flexible Dimensional Pivots The Explore tool ensures the consistent application of organization-wide metrics definitions and offers the flexible application of dimensions, enabling users to tailor analyses to their specific needs. By presenting a cohesive and accurate picture of organizational data, the Explore tool enables faster and more reliable insights, accelerating the overall time-to-insight. Better, Faster Insights to More Decision-Makers Around Your Organization One Model's Explore tool excels in its ability to deploy sound insights to any team or decision maker within an enterprise. By seamlessly integrating with various data sources and offering robust reporting features, the tool ensures that actionable insights are readily accessible to all relevant stakeholders. No other people analytics platform drives more data-driven decision-making better than One Model, thanks to tools like Explore, which empower organizations to make informed decisions quickly and efficiently. Essential Questions to Ask When Selecting an AI-Powered HR Tool Learn the right questions to ask to make the right decisions as you explore incorporating AI in HR.
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7 min read
May 31, 2024
RedThread Research has identified 7 skills verification methods that range from simple to more complex. In Part 1 of this 2-part job skills assessment series, we dive into the 4 simplest and most common job skills assessments. In Part 2, we examine 3 complex forms of skills verification that lean heavily on benchmarks and data. RedThread members may access the full report authored by Heather Gilmartin and Dani Johnson. As the prevalence of skills-based recruiting grows, HR leaders are beginning to grapple with how to verify skills in order to ensure their data is accurate. They’re discovering that evaluating job skills is more complex than merely defining roles and hoping to find perfect matches. Decision-makers must weigh a variety of factors to determine the most suitable verification approach for their needs. You’re likely using some of these tactics to authenticate skills, but which are right for each role? And when should you level up to new ones? 1. Self-Assessment If you’re looking for simple ways to verify skills, having employees and applicants affirm their own expertise is the second most common approach, according to RedThread. This is most typically seen in job applications, employee resumes, and interviews. But just because it’s popular doesn’t mean it’s effective. While widely used, self-assessments can be unreliable. Discrepancies can occur for several reasons, including poor self-awareness, overconfidence, unintentional "self-presentation" bias, or, more seriously, candidate fraud. Many studies support the notion that people are notoriously inaccurate in subjective evaluation compared to objective measurements. Additionally, RedThread notes that this approach lacks specificity of the level of skills and doesn’t contribute to the company’s skills data set. That’s not to say there’s no place for worker self-reviews. As long as leaders recognize the limitations and risk, self-assessments can be a good, low-cost first step in identifying top talent early on. Giving potential employees an opportunity to showcase their abilities and skills contributes to a better hiring experience. 2. Performance Feedback / Informal Observation In this verification type, an observer validates skills through an informal set of standards using various modes of feedback and reviews. According to RedThread’s report, 37% of surveyed organizations use performance feedback in their skills verification processes - the single most-used method by a wide margin. This is possibly because even before adopting a skills-based recruiting strategy, performance feedback was already being used. These evaluations offer valuable insights into an employee's understanding and reveal any knowledge gaps by reflecting their overall performance over time or within a specific project. This approach contrasts with formal assessments, which isolate feedback to a single, often stressful event or test. One significant downside to note in this type of job skills assessment is that the observer’s feedback can be subjective and influenced by personal biases. 3. Formal Observation The key difference between formal and informal observation is that formal observation employs a specific framework to assess employee skills. A formal, structured set of standards empowers managers to develop the ability to hold difficult conversations. It enables the clear identification of areas of improvement, and it provides a foundation for coaching and knowledge transfer that helps improve performance levels. Even beyond actual performance and skills, observation can provide insight into so-called “soft skills,” such as how they handle pressure, adapt to new challenges, and interact with colleagues. It’s important to invest in the time and training needed to carry out effective, unbiased observation. Observers should factor in the possibility that employee apprehension may result in inconsistent results. Additionally, observation might not capture all aspects of an employee’s capabilities. 4. Formal Assessment Think tests, simulations, and sandboxes. RedThread reports that 53% of respondents who use formal job skills assessments do so because of compliance and regulatory requirements for certain roles, including necessary certifications or credentials. Formal assessments can be very valuable. They increase objectivity, help clarify the role for applicants (who may be defining the skill differently than you do), provide leaders with data, and save time for recruiters. However, they don’t always align with the role or tell you what you need to know. Paying attention to assessment quality is critical for the best outcomes in skills verification. Upskilling Your Career Skills Assessment Approach In this first installment of our exploration into skills verification approaches, the basic methods we’ve discussed serve as a foundational step. It's important to recognise, however, that these initial methods, while effective up to a certain point, might not suffice for roles requiring deeper or more specialized skill verification. And as the skills trend continues to evolve, leaders will increasingly desire more confidence, accuracy, or granularity in their skills data. In Part 2 of this series, we explore 3 more rigorous and comprehensive approaches to meeting the evolving demands of talent acquisition and employee upskilling programs. One Model: Skills-Based Recruiting Depends on Data One Model provides a people analytics platform that enhances skills-based recruiting by leveraging data-driven insights to identify skill gaps and predict future talent needs. We help organizations make more informed hiring decisions and better align their recruitment strategies with their business objectives. Learn how to build a people data platform that will allow you to do better skills-based hiring.
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5 min read
May 31, 2024
In Part 1 of our series on job skills assessments, we explored 4 simple ways to verify skills as identified by RedThread Research. RedThread members may access the full report authored by Heather Gilmartin and Dani Johnson. In Part 2, we delve into 3 sophisticated techniques that leverage both internal and external data to ensure a more accurate job skills assessment approach. As the landscape of skills-based recruiting expands, it becomes evident that some roles and contexts demand more nuanced and data-intensive verification methods than others. 1. Comparison to External Benchmarks When verifying skills, it’s crucial to measure them against established external standards. Yet, according to RedThread, only 11% of organizations do so. Benchmarking helps companies understand how their candidates' skills stack up against industry standards. In addition to providing a clear perspective on talent level relative to the broader market, it helps the organization future-proof their talent strategy and competitive edge. However, relying solely on external benchmarks may overlook unique aspects of a company’s culture or specific job roles that require customized skill sets. This approach also assumes that industry standards are up-to-date and sufficiently granular for an organization’s needs, which may not always be the case in fast-changing industries. Effective benchmarking relies on advanced skills intelligence tools, thus requiring an investment in technology or access to benchmarking data. As with other verification methods, benchmarks are most effective when used in conjunction with internal assessments. These platforms can integrate with existing HR systems to provide deeper insights and real-time data that help refine benchmarking efforts against industry standards. 2. Inference from HR Data Skills prediction based on HR data involves analyzing information from HR technology systems to infer employee skills. AI models predict employees’ skills based on a range of data sources. It’s quick, effective, and doesn’t require much employee involvement, RedThread explains. They report that 13% of employers currently make use of this career skills assessment method. This method uses historical data, such as past job performances, training records, and employee interactions, to predict skill levels and identify potential gaps. As it continues to evolve, the accuracy of skill predictions generally increases with the number of data points processed by AI. While powerful, this approach can be limited by the quality and completeness of the data collected. Biases in historical data can also lead to skewed predictions, making it essential to continuously update and review data inputs to ensure accuracy and fairness. HR data on industry skills is typically purchased through Human Resource Information (HRIS), Learning Management (LMS), Talent Marketplaces, Applicant Tracking (ATS), and Performance Management systems. Such systems enhance the accuracy of skills predictions by utilizing machine learning models which improve as they process more diverse and comprehensive data sets. 3. Inference from Work Data Using work system data to measure skills involves analyzing real-time data from work processes and outputs. By evaluating the quality, efficiency, and creativity of the work produced, organizations can gain a precise understanding of an employee's practical skills. This method requires sophisticated data analysis tools and expertise. It is also more complex than using HR data because it demands advanced technical integrations and substantial cross-functional collaboration to identify relevant metrics for specific skills. However, RedThread concludes that this is the only skills verification method that offers real-time insights into daily work and enables decisions at scale, based on performance data. This is where One Model shines, by seamlessly integrating with multiple data sources across the organization, enabling a more holistic and real-time assessment of employee skills based on actual work outputs. How One Model Partnerships Elevate Job Skills Assessment with Advanced Data-Driven Approaches Lightcast is a leading expert in the labor market. They collect and process a wide array of data, including job postings, resumes, and work history profiles. This data is aligned to job titles and skills classifications every two weeks. By merging Lightcast's extensive knowledge of the external labor market with One Model's ability to unlock people data, organizations can gain business insights relative to industry-wide talent trends. A One Model partnership empowers HR teams to: Enhance the consistency of data in reporting by adding standardized titles to current roles Analyze talent headcount, career paths, retention with accuracy Better align skills with job roles to enhance skills knowledge and plan for the future Ready to spend your time sharing insights, not integrating your people data? Learn how One Model integrations can help you see the whole picture. Or get a peek under the hood at how One Model could specifically benefit your organization. Request a demo.
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4 min read
May 28, 2024
The buzz around Artificial Intelligence (AI) in the workplace is growing louder by the day. As organizations worldwide attempt to harness this revolutionary technology, particularly in the realm of Human Resources (HR), a fundamental question arises: Is our workforce data truly ready for AI and Machine Learning (AI/ML)? The Reality of Data Readiness for AI and ML In our modern business environment, HR teams are making use of workforce data for a variety of purposes. Traditionally, these teams had focused on extracting data for reporting in the form of monthly extracts or daily snapshots. This approach, while useful for traditional needs, falls short of the data needs for AI and ML. That’s because data preparation for AI isn’t just about collecting and storing data to review later; it's about curating data in the right way to effectively train sophisticated models. AI tools today are highly complex and capable of predicting patterns with remarkable accuracy. However, vast amounts of high-quality, curated data are required to effectively train those models. The quality and relevance of the data are critical for the fine-tuning needed for specific tasks or domains like our use cases in HR. The Need for a Paradigm Shift From this perspective, most HR datasets and HR data stores that we had previously prepared are not ready for AI and ML (whether it's generative AI or "traditional" predictive AI). Without appropriately prepared training data, the algorithms we hope to launch will fall short in their learning. Potential benefits of AI in HR—from recruitment optimization to workforce alignment with business goals—could remain untapped or, worse, lead to unintended consequences if models are trained on poor or incorrect data. Preparing your HR team for this new phase of work isn’t just about adopting new technologies; it's a paradigm shift in how we think about and handle data. This is even more pivotal in the areas of MLOps and LLM operations when we try to deploy these models at scale in a repeatable fashion. We’re going to start to hear more about these terms and the operational needs of machine learning in the near term future and it’s HR’s responsibility to stay on top of the nuances in this space. The First Step: Preparing and Unlocking Your Data Data extraction is one of the most essential parts of preparing for AI and ML. We address the foundational importance of this step, robust data preparation and management, in our blogpost 5 Tips for Getting Data Extraction Right. It explores in greater detail these 5 action steps: Prioritize and align extracted data with the needs of the business Be thoughtful about what you extract Build the business case to pull more Automate your extractions Extract for data science, not just reporting The paradigm shift and these tips can help HR teams more effectively and efficiently adopt AI practices that will drive business value and insights. Why One Model Stands Out in People Analytics AI The final key in preparing for AI and ML is having the right technology in place to build a fine-tuned model that meets your company’s unique needs. One of the main reasons I joined the One Model team stems from their foresight and commitment in this area. Due to that investment, we're now the only people analytics vendor with a machine learning platform that runs on a data model tailored to your firm, not just last-minute AI features. This distinction is vital. And "One Model" isn’t merely about preparing data for AI models; it’s an end-to-end platform encompassing data management, storytelling, model creation, evaluation, deployment, and crucially, audit-ready and transparent tools. Our platform empowers HR teams to manage and deploy customized ML models and MLOps effectively, beyond the traditional scope of data engineering teams. The dialogue around AI, ML, and MLOps in HR is already in full swing. Staying informed and engaged in this conversation is crucial. If you wish to delve deeper or discuss strategies and insights in this space, I, along with the One Model team, am more than willing to engage. We're keen to hear how your team is navigating the intricate landscape of MLOps in HR. Essential Questions to Ask When Selecting an AI-Powered HR Tool Learn the right questions to ask to make the right decisions as you explore incorporating AI in HR.
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5 min read
May 20, 2024
For employees, recognizing a bad company culture isn’t difficult. Their feelings of being overwhelmed, frustrated, unvalued, and unsupported serve as clear indicators. When employees experience these emotions, it’s a red flag that something is amiss within the organization. Leaders may see the downstream effect of a negative culture in the workplace in various ways. They might see low productivity, high employee turnover rates, and a general low return on investment (ROI) for the organization. Our VP of Sales and Solutions Architect Leader Phil Schrader discussed this topic with our friends at Culture Curated. Partners Season Chapman and Yuli Lopez shared several common ways they see leaders contributing to toxic workplace cultures. #1 You’re Fostering an Environment of Disconnection Yuli pinpointed siloes as a significant issue that impacts culture. “When there is poor work culture, you see it reflected in not enough connectivity between departments or peers. Siloes impact the way work is getting done.” The lack of connectivity not only affects internal operations but also has a tangible impact on customer satisfaction and organizations’ bottom line: A Towers Watson study found that strong internal communication strategies can lead to a 47% higher return to shareholders compared to the least communicatively effective firms. According to Forbes, siloes are often a trickle-down effect of conflicted leadership.The #1 key to solving this problem that plagues most organizations and creates a toxic workplace culture? Transparent communication. #2 - You Haven’t Defined Your Organization’s Core Identity Season notes that as an organization evolves, defining its core identity is crucial. It boils down, she says, to “being honest about what you want, what you need, and what competencies and behaviors you need your employees to display.” For example, while growth and innovation may have once defined a company, there may come a point when consistency and predictability become essential. If leaders fail to define this new season for their teams, results can be disjointed and a poor work culture results. With no clear sense of their organization’s purpose and identity, employees can struggle to connect their individual roles to the broader mission. This disconnect hampers motivation and engagement, ultimately affecting overall organizational performance. Conversely, a well-defined core identity is the compass that guides an organization toward success. It aligns teams, fuels innovation, and ensures a cohesive, purpose-driven workforce. #3 - You're Eroding Trust and Teamwork Every organization goes through seasons where employees are “in the trenches,” so to speak, when the work is challenging and collaboration is a must. Season shares that in healthy organizations, employees jump in and work together. Believing in and removing obstacles for each other has a catalyzing effect on the team and the results. However, where teams exhibit unhealthy competition, distrust, disengagement, or failure to communicate, a toxic work culture is born. Leaders can unintentionally foster these negative conditions by withholding information, showing favoritism, being disorganized, and failing to recognize and support their teams. On the other hand, when leaders model the collaborative, encouraging spirit they want to see in employees, they positively shape team dynamics, building trust and nurturing motivation. How One Model Helps Create Healthy Organizational Culture People Analytics is the answer to many culture challenges. The One Model People Analytics platform empowers HR leaders to effectively use their workforce data to understand and manage virtually every aspect of the employee experience. From Data to Decisions: What Is People Analytics?
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28 min read
May 15, 2024
Welcome to the One Model resource page for books about People Analytics. This is meant to be a living document, so if we missed one of your favorites, please don't hesitate to reach out to Richard Rosenow with a recommendation. He's always looking to add to the library. August People Analytics Book Pick: How to Measure Human Resource Management "This book, by Jac Fitz-enz and Barbara Davison, is a cornerstone of our field. It was groundbreaking when it was first published in 1984 and remains incredibly relevant today. Fitz-enz's insights into quantifying and evaluating HR practices were years ahead of their time, laying the groundwork for the sophisticated analytics we use now." - Richard Rosenow Past Book Club Books: June People Analytics Book Pick: Creepy Analytics by Dr. Salvatore V. Falletta "This book is already on my bedside table, and I cannot wait to dive in. As we continue to explore the power of people analytics, I find it essential to keep an eye on ethical HR practices." - Richard Rosenow Meet the Author Webinar - Register to hear the recording! Join the conversation on LinkedIn. People Analytics Essentials - Reading Starter Kit When it comes to books about people analytics, we are finally at a point where we have the luxury of having too many choices. The virtual library is well stocked and all of these books come highly recommended and vetted by our experts, but if you had to start somewhere, we recommend the following 7 books to kick things off. Work Rules - Best “painting the dream” “Work Rules” by Laszlo Bock is the first book I recommend to folk looking to learn more about People Analytics. Written by Google's former SVP of People Operations, this book offers a behind-the-scenes look at Google's unique approach to attracting and retaining top talent. Through compelling anecdotes and evidence-based insights, Bock presents actionable strategies for building a dynamic, innovative, and people-centric organization. Work Rules has held the top spot on my book recommendation list for a long time as it’s the right blend of real-life case study and inspiration. It’s not too technical, but you leave feeling excited to learn more about the space. Whether you're in HR and new to this people analytics space or looking for ideas about how to infuse people analytics into your existing practices, "Work Rules!" is a great introduction in how it paints the picture of what people analytics can look like at scale within an organization. Moneyball - Best at “building excitement” "Moneyball" by Michael Lewis (the movie is great, but the book is better!) serves as an unexpected guide for people analytics leaders. While it tells the story of the Oakland Athletics baseball team's innovative use of statistics, the takeaways from the book reach far beyond baseball. The application of data analytics to assess baseball players and make strategic decisions on the field makes for an easy transition to talking about how people analytics can assist business decisions. For those new to people analytics or HR, "Moneyball" offers relatable examples of how data-driven decisions can lead to surprising and effective outcomes. Its engaging narrative can even serve as a conversation starter with business leaders outside the typical HR function, demonstrating how unconventional thinking about talent practices, paired with data analysis, can lead to success. "Moneyball" is not only a gripping story but a playbook for those seeking to introduce people analytics into their organization. Excellence in People Analytics - Best at “Overall introduction” "Excellence in People Analytics" by David Green and Jonathan Ferrar is the classic resource now within the people analytics field. Their book is specifically tailored for those in the people analytics field or those seeking to embark on this fascinating journey. The authors, both renowned experts in the field, offer a comprehensive guide to understanding, implementing, and excelling in people analytics within an organization. With a blend of theoretical frameworks and practical case studies, the book provides a holistic view of how people analytics can drive better decision-making and foster organizational success. Ideal for HR professionals, analytics leaders, and business executives, "Excellence in People Analytics" will help set the stage for people analytics and inspire you to leverage data in innovative ways to enhance people processes. Sensemaking - Best at “reminding you that humans still matter” "Sensemaking" by Christian Madsbjerg is a thought-provoking exploration of the human context in the era of data and analytics. Madsbjerg argues for a balanced approach that marries data with a deeper understanding of human behavior, culture, and emotion. I go back to read Sensemaking every couple of years to remind myself to focus on the people in people analytics. Knowing what data can do for an organization is important, but it’s just as important in a people analytics role to understand the limitations of data. For those entering the field of people analytics or looking to expand their HR perspective, "Sensemaking" provides a unique standpoint, emphasizing that not everything can be reduced to numbers. It encourages readers to blend analytical thinking with empathy, intuition, and cultural awareness. This approach can lead to more nuanced and effective decisions in people management. "Sensemaking" is a must-read for those who wish to infuse their analytical work with human insights and achieve a more sophisticated and holistic understanding of the people they serve. Cartoon Guide to Statistics - Best at “Stats without fear” The "Cartoon Guide to Statistics" is a breath of fresh air for anyone who has ever felt bewildered by statistics. Whether you're a people analytics leader or an HR professional looking to dip your toes into the world of data, this lighthearted and engaging guide speaks plain English and turns complex statistical concepts into digestible and even enjoyable lessons. As someone who came to statistics later in life, this book was a blessing and I can’t recommend it enough. Through witty cartoons and crystal-clear explanations, the book proves that relearning statistics as an adult doesn't have to be a daunting task. In fact, it can even be funny! A breakthrough resource for those who may struggle with traditional statistical texts, the "Cartoon Guide to Statistics" offers a welcoming entry point to the crucial world of data analysis. The Fundamentals of People Analytics: With Applications in R - Best at “mastering the stats” While leading people analytics teams at Experian, Mastercard, Robinhood, and Roku, Craig has somehow also found time to teach, give back to the people analytics community, and write a full statistics textbook, end to end, with people analytics at the core. The Fundamentals of People Analytics: With Applications in R is what happens when a true practitioner sees a problem, no great statistical resources with HR folk in mind, and applies himself to it fully. The result is a masterwork guide to statistics for the people analytics professional. If you are looking to learn statistics for HR or build your confidence in the applications in HR, look no further than this book. Talent Intelligence - Best at “Talent intelligence” "Talent Intelligence" by Toby Culshaw explores the field of talent intelligence, an area adjacent to, but one that is starting to appear to be distinct from, people analytics. Overviewing the world of both internal and external talent markets, Culshaw's insights provide a deep understanding of how to strategically approach talent acquisition and talent management through data-informed practices. This book is an ideal recommendation for those involved in recruiting or those in people analytics seeking to expand their perspective. Whether you're an HR leader or a professional looking to understand the broader landscape of data-informed HR, "Talent Intelligence" offers a comprehensive guide to leveraging data to make informed talent decisions. It's an eye-opener for anyone wanting to deepen their understanding of the ever-evolving landscape of talent in today's business world. The field of People Analytics In this section are books that cover the full field of People Analytics. These are excellent overviews that share a range of topics from what this field is, to how to go about it, to case studies in the space. A great place to start for folk looking to learn more about how analytics is used to better understand the workforce. Excellence in People Analytics - Jonathan Ferrar and David Green The Power of People - Nigel Geunole, Jonathan Ferrar, and Sheri Feinzig People Analytics for Dummies - Mike West Introduction to People Analytics - Nadheem Khan and David Milnor People Analytics: How Social Sensing Technology Will Transform Business and What It Tells Us About the Future of Work - Ben Waber Work Rules - Laszlo Bock HR analytics: The What, Why, and How - Tracey Smith Predictive Analytics for Human Resources - Jac Fitz-enz and John Mattox II Strategic Analytics: Advancing Strategy Execution and Organizational Effectiveness - Alec Levenson Data-Driven HR: How to Use Analytics and Metrics to Drive Performance - Bernard Marr Human Capital Analytics - Gene Pease, Bryce Byerly, and Jac Fitz-enz The Practical Guide to HR Analytics: Using Data to Inform, Transform, and Empower HR Decisions - Shonna Waters The Basic Principles of People Analytics: Learn how to use HR data to drive better outcomes for your business and employees - Erik van Vulpen The New HR Analytics: Predicting the Economic Value of Your Company's Human Capital Investments - Jac Fitz Enz Specialized People Analytics Similar to the first section, this section is for books that cover a broad overview of the space, but for a more narrow vertical within the space. These are books that still touch people analytics, but specialize is sub-topics such as DEI, L&D, Workforce Planning and Talent Intelligence. Great for learners who want to go deep on a given topic or transition into people analytics from their prior field. Talent Intelligence - Toby Culshaw Inclusalytics: How Diversity, Equity, and Inclusion Leaders Use Data to Drive Their Work - Victoria Mattingly, PhD, Sertrice Grice, and Allison Goldstein Agile Workforce Planning: How to Align People with Organizational Strategy for Improved Performance - Adam Gibson Strategic Workforce Planning: Developing Optimized Talent Strategies for Future Growth - Ross Sparkman Next Generation Performance Management: The Triumph of Science Over Myth and Superstition - Alan Colquitt Learning Analytics: Measurement Innovations to Support Employee Development - John Mattox II, Mark Van Buren, and Jean Martin Adaptive Space: How GM and Other Companies are Positively Disrupting Themselves and Transforming into Agile Organizations - Michael Arena Positioned: Strategic Workforce Planning That Gets the Right Person in the Right Job - Dan Ward and Rob Tripp People Analytics focused Analytics, Data Science, and Statistics With the maturity of the people analytics space, we've seen a rise in textbooks covering the fundamentals of HR analytics from an anaytics technical perspective or statistical overview of the space. If you are looking to brush up on your technical knowledge or just starting down your journey with statistics and looking for an HR analytics textbook, you're in the right place. The Fundamentals of People Analytics: With Applications in R - Craig Starbuck Handbook of Regression Modeling in People Analytics: With R - Keith McNulty Handbook of Graphs and Networks in People Analytics: With R - Keith McNulty Introducing HR Analytics with Machine Learning: Empowering Practitioners, Psychologists, and Organizations - Austin Hagerty and Christopher Rossett Doing HR Analytics - A Practitioner's Handbook With R Examples - Lyndon Sundmark Storytelling with Data - Cole Nussbaumer Predictive HR Analytics: Mastering the HR Metric - Dr. Martin Edwards and Kristen Edwards General Analytics, Data Science, and Statistics The benefit of people analytics being a younger discipline in the analytics field is that we have many other disciplines that have gone down this path ahead of us. We can learn from analytics and statistics books across many disciplines and bring that knowledge back to people analytics. Here is a sample of books that come up frequently when speaking to people analytics leaders about their favorites from outside the field. Competing on Analytics: The New Science of Winning; With a New Introduction - Thomas Davenport and Jeanne Harris Weapons of Math Destruction - Cathy O’Neil The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World - Pedro Domingos Naked Statistics: Stripping the Dread from the Data - Charles Wheelan The Signal and the Noise: Why So Many Predictions Fail--but Some Don't - Nate Silver Cartoon Guide to Statistics - Larry Gonick Statistics in Plain English - Timothy Urdan Understanding Humans At the end of the day, we are people analytics, not just analytics, and with that comes a real need to understand our subject area - people! These are some of the goto books in the space and some of my favorites when it comes to engaging with the social sciences. They are hand picked for being engaging, thoughtful and educational reads. Moneyball - Michael Lewis Sensemaking: The Power of the Humanities in the Age of the Algorithm - Christian Madsbjerg Humanizing Human Capital: Invest in Your People for Optimal Business Returns - Stela Lupashor and Solange Charas Irresistible: The Seven Secrets of the World's Most Enduring, Employee-Focused Organizations - Josh Bersin The Model Thinker: What You Need to Know to Make Data Work for You - Scott Page The Undoing Project: A Friendship That Changed Our Minds - Michael Lewis Thinking, Fast and Slow - Daniel Kahneman Superforecasting - Phillip Tetlock, Dan Gardner Evidence-Based Management: How to Use Evidence to Make Better Organizational Decisions - Eric Barends and Denise Rousseau Humans at Work - The Art and Practice of Creating the Hybrid Workplace - Anna Tavis and Stela Lupashor Misbehaving - Richard Thaler It’s Not Complicated - Rick Nason Want to learn more from an expert about the space? Let's schedule time to chat. Measuring HR We won't get far with people analytics if we don't have well defined inputs, outcomes, or measures within HR. Understanding how HR is measured is critical to a people analytics team success. There is a full second library worth of HR measurement books out there and this section just scratches the surface. The HR Scorecard: Linking People, Strategy, and Performance - Dave Ulrich, Mark Huselid, Brian Becker Human Resource Management: People, Data, and Analytics - Talya Bauer, Berrin Erdogan, David Coughlin, and Donald Truxillo Investing in People: Financial Impact of Human Resource Initiatives - Wayne Cascio and John Boudreau Victory Through Organization: Why the War for Talent is Failing Your Company and What You Can Do About it - Dave Ulrich, David Kryscynski, Wayne Brockbank, and Mike Ulrich Investing in People: Financial Impact of Human Resource Initiatives - John Boudreau, Wayne Cascio, and Alexis Fink The ROI of Human Capital - Jac Fitz Enz Positioned - Dan Ward, Rob Tripp HR Technology Last but not least, the HR technology space has its own set of fantastic resources. We won't get very far in people analytics without technology to produce data, so understanding this space is critical. Here are some standout HR technology books from the past few years. Introduction to HR Technologies: Understand How to Use Technology to Improve Performance and Processes - Stacey Harris Artificial Intelligence for HR: Use AI to Support and Develop a Successful Workforce - Ben Eubanks I, Human - Tomas Chamorro-Premuzic Talent Tectonics: Navigating Global Workforce Shifts, Building Resilient Organizations and Reimagining the Employee Experience - Steve Hunt Finished this list? Check out One Model's whitepapers and ebooks. Also, did we miss your favorite? Recommendations are welcome! Send Richard Rosenow your recommendations and we'll add to the list. Learn how to up your game with One Model's people analytics software.
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May 13, 2024
In the rapidly evolving landscape of Human Resources (HR), where technology and automation are reshaping the way businesses operate, the role of HR tech influencers has taken on paramount importance. As organizations navigate this dynamic environment, insights from trusted HR influencers have become indispensable in making informed decisions, adopting innovative tools, and embracing data-driven strategies. A testament to this influence is the recognition of Richard Rosenow with the "2024 Top HR Tech Influencer" award. The Role of HR Influencers in Technology Adoption HR departments across the globe have been in the process of transitioning for nearly 2 decades. From a subjective people operation to one of the most important analytical business assets. At the forefront of this transformation are the HR influencers whose insights and expertise have been guiding HR leaders through the emerging technology jungle and helping expand the roles inside their teams. in their HR strategies. The annual list of "Top 100 HR Tech Influencers," curated by Human Resource Executive and the HR Technology Conference & Exposition, serves as a testament to the pivotal role these influencers play in shaping the HR technology landscape. This list, now in its sixth year, comprises a diverse array of professionals, including analysts, consultants, and practitioners, who collectively represent the vanguard of HR technology thought leadership. Their contributions extend beyond their respective fields, encompassing thought-provoking perspectives, innovative solutions, and a deep understanding of the synergy between technology and HR. How do you become an HR Influencer? Becoming an HR influencer entails a journey of expertise, innovation, and consistent value delivery. Richard Rosenow's path to becoming an HR influencer exemplifies this process. With a background rooted in HR and technology, Richard leveraged his real-world experience building and leading people analytics teams to create insightful content and share actionable strategies across various platforms. Through engaging articles, speaking engagements, and thought leadership on LinkedIn, he demonstrated a deep understanding of HR technology's evolving landscape. Richard's dedication to staying updated on industry trends, sharing real-world solutions, and fostering meaningful connections established him as a trusted voice. His deep sense of caring and creating goodwill has also made him a friend to many. By consistently adding value, addressing pain points, and offering innovative perspectives, he garnered a dedicated following. One Model is honored to have Richard on our team. As a previous customer and evangelist who exemplifies our values, we could not be happier to have him on our team. More about the 2024 HR Tech Influencer Award The year 2024 marks a pivotal juncture in the realm of HR, with automation and generative artificial intelligence redefining traditional workplace dynamics. These advancements have underscored the need for HR operations to be optimized for efficiency, agility, and adaptability. HR departments are increasingly turning to technology to streamline processes, make informed decisions, and enhance overall organizational performance. The selection process for the "Top 100 HR Tech Influencers" list is rigorous and thorough, spearheaded by the editorial team at Human Resource Executive in collaboration with the HR Technology Conference organizers. The primary objective is to identify individuals who possess the transformative power to reshape how technology is leveraged within the HR industry. Rebecca McKenna, senior vice president of the HR portfolio at ETC, emphasizes the significance of this year's cohort, especially given the rapid advancements witnessed in HR technology. These influencers stand as beacons of reliable guidance, offering organizations across the globe profound insights and dependable advice. Interested in talking to Richard and the One Model team? Let us know! In conclusion, in a world where technology and HR are intricately intertwined, HR influencers have emerged as essential conduits of knowledge and innovation. Richard Rosenow's upcoming recognition underscores the significance of their contributions, reminding us that the path to HR excellence is paved by those who illuminate the way forward.
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May 08, 2024
Transitioning to a data-driven HR system can be daunting. So our VP of Sales and Solutions Architect Leader, Phil Schrader, met up with Yuli Lopez, Partner and Principal at Culture Curated, to discuss best practices for HR leaders embarking on or looking for guidance on that journey. Yuli describes her own mindset to this transition in the video below. Read on for a few additional tips for anyone going through or leading this level of change and upheaval. Embrace a Growth Mindset Adapting to digital systems is a learning curve for everyone involved. It’s crucial to approach this transition with an open mind, ready to embrace new methodologies and technologies. Yuli was two steps ahead in this regard. “[As an HRVP], it was exciting to be able to have information that I had been chasing,” she said. For teams or individuals feeling nervous about or resistant to using data in HR, she encourages new users to “jump in and put time on your calendar for just exploring.” She’s right. Venturing beyond familiar or unexpected information can unveil insights you weren’t even aware you needed. This exploration can help users begin to understand how data stories are constructed from visibility into the details impacting employees. Of course, a willingness to experiment works best in a culture where mistakes are seen as opportunities for growth rather than failures. A growth mindset not only enhances individual capabilities but collectively elevates the organization. Practice Adaptability The transition to using data in HR is less a straight line and more a series of learning opportunities, commonly thought of as obstacles. In this journey, embracing change and having the ability to pivot your HR mindset is paramount. Leaders who quickly adapt to the unexpected and use every challenge as a stepping stone towards innovation will cultivate a flexible environment. Open dialogue will be the norm, ensuring that every team member feels they have a voice in this transformative process. Adaptability is undergirded by two key characteristics (Source) that leaders must both personify and incentivize employees to develop: Emotional Resilience: The inner strength required to navigate through challenges and preserve mental and emotional health during times of change. Personal Responsibility: The commitment to proactively manage how we react to change, ensuring we are in control of our own development and progress. In addition to technical training, coaching on the dynamics of change and change management can be useful. Prioritize Collaboration Digital transitions benefit significantly from diverse perspectives and expertise, not to mention a strong, collaborative team. Engaging team members in the planning and implementation phases ensures that the digital solutions adopted are user-friendly and genuinely address the needs of the organization. Yuli notes that it’s important to partner with other departments. “There may be other aspects you’re not thinking about. If you go to them with a hypothesis, together you may be able to draw unexpected insights. This collaborative approach not only facilitates smoother adoption but also strengthens the sense of ownership among staff, fostering a supportive environment for change. Create a Strong Visionary Perspective Vision casters are like seasoned captains navigating through uncharted waters. They have a keen eye on the distant horizon, focusing on the incredible benefits that lie ahead. For a data transition, that could be delivering impactful insights across your organization and easily translating workforce data into cost allocations. These visionaries don't just keep these exciting perspectives to themselves; they share them, painting a vivid picture of the future and recruiting buy-in for an efficient process. As both cheerleaders and coaches rolled into one, these leaders are in the trenches, reminding everyone why the upheaval of transition is worth it. They champion and model patience and persistence, highlighting what every step closer to using data in HR means for the team, the organization and clients. How One Model Helps These mindsets are fundamental for HR leaders guiding their departments through the digital transition, but the technology of choice plays an enormous role in the outcomes of the journey. One Model provides the people analytics solution technology that orchestrates everything decision makers need to be able to quickly make brilliant workforce decisions.
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6 min read
May 06, 2024
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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Apr 09, 2024
We asked our friends at Culture Curated why organizations should have a strong focus on human resource compliance. That led to a more foundational question: What is an organization’s culture? Culture: Ping Pong Tables or Compliance in HR? In the quest to boost workplace culture–and thus performance, our initial instinct might be to think of adding fun elements, like a ping pong table in the break room. However, the journey to improving corporate culture delves much deeper than surface-level entertainment. It begins with the bedrock of strong human resource compliance. In fact, “Any good culture is going to be built on the foundation of strong compliance,” says Season Chapman, Partner & Principal Consultant of Culture Curated. “It’s about how we must treat people.” But compliance isn't just about adhering to HR compliance laws or procuring a human resources compliance solution. It's about establishing a framework within which people are treated fairly and decisions are made responsibly. This foundation of compliance in HR is essential, not just for its own sake, but as the ground floor upon which the rest of the company culture is built. Laying Your Culture’s Foundation: Accountability and Belonging Moving beyond the notion that culture is merely about having fun, culture is–at its core–about accountability, achieving results, and fostering trust among team members. But how do we shift the conversation towards these deeper aspects of culture? The answer lies in starting with human resources compliance as the base layer. Drawing from psychological principles, humans seek a sense of belonging and connection. They want to feel aligned with the company's mission and vision. The secret to that goal starts with a focus on building meaningful relationships with employees and fostering a sense of belonging and support. In today's workplace, the concept of psychological safety is paramount for cultivating a culture where employees feel confident in sharing ideas. This safe space is critical for a vibrant, innovative workplace culture. Starting the Journey Towards a Balanced Culture So, how does an organization embark on this journey towards a culture that balances fun, compliance, and psychological safety? According to Yuliana Lopez, Partner & Principal Consultant of Culture Curated, “The starting point is an organizational assessment.” She explains that such assessments gauge the current state of compliance and how employees feel about their work environment and relationships with peers. This comprehensive evaluation can identify areas for improvement and set the stage for developing a culture that not only meets legal requirements but also fulfills and inspires its workforce. Are You Ready for the Coming Wave of AI Regulation for Human Resources? How One Model Helps With Compliance Foundations One Model assists with compliance by providing an integrated analytics platform designed to manage and analyze workforce data according to legal standards and best practices that includes: Offering advanced analytics and reporting capabilities that enable compliance with regulatory requirements. Prioritizing robust data security and privacy measures to protect sensitive information and comply with data protection regulations. Featuring role-based access controls so that only authorized personnel have access to sensitive data, in order to maintain continuous compliance with labor laws, and occupational safety and other standards. Providing customizable dashboards to monitor key compliance indicators, from wage and hour laws to benefits regulations and beyond. While the allure of quick fixes like a ping pong table may seem like an easy way to boost morale, the real work in improving culture goes much deeper. By establishing a strong foundation of compliance with human resource compliance solutions like One Model, organizations can lay the groundwork for a positive culture. This foundation enables leaders to enhance performance, foster genuine connections, and support the well-being of every employee. Wondering about compliance in the world of AI and Machine Learning? We’ve got you covered. 1. Understand how ethics are changing in a world with AI. Read more. 2. Be prepared with regulations coming to HR. Join the Regulations and Standards Masterclass today. Learning about AI regulations and standards for HR has never been easier with an enlightening video series from experts across the space sharing the key concepts you need to know.
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7 min read
Apr 02, 2024
Reporting specialists and data analysts are often required to predict the future for stakeholder groups. They do this through a variety of models, including forecasting and annualization. Although both methodologies aim to predict future values, their applications and the mathematical logic behind them vary significantly, catering to different business needs. What is Annualization and Its Significance? Annualization is a mainstay for finance and accounting but there are situations where it may be useful in HR contexts. It can be used to estimate year-end values for turnover rates, total new hires, and job openings filled, based on current data. Annualization works well when: There is little volatility in the metric across time periods There is little seasonality in the metric The metric is not likely to trend upward or downward during the course of the year Simplify Annualization with One Model One Model streamlines the computation of annualized metrics. By selecting the "Year to Date" option and "Annualize" in "Time Functions," the system will only consider the current year's data, offering a clear example of annualization at work. The Case for Forecasting Forecasting provides several benefits over annualization. While annualization typically only considers data points from the current year, forecasting can: Utilize data from a much wider time frame and range of data points Factor in seasonal fluctuations and trends Provide a more nuanced view of potential future states with confidence intervals, which is especially valuable for HR metrics that exhibit variability (e.g., number of hires, number of terminations, and termination rates). Simplify Forecasting With One Model One Model simplifies forecasting with its Embedded Insights feature. Just create a time-series line graph for your metric and use the feature to extend your forecast to the year's end. Increasing the number of data points, by adjusting the time metric from monthly to weekly or daily, for instance, can enhance forecast accuracy by capturing shorter-term cycles that may be present in the data. Including data from at least 30 data points will improve the accuracy of your forecasts and if annual seasonality is present, including data covering two or more years will also improve accuracy. You can adjust forecast parameters to align the final forecast period with the year-end. After running the forecast, simply click on the last data point in the visualization to see the forecasted value and its confidence interval. For more complex situations where the current year data pattern is expected to shift relative to last year’s pattern, One AI can be used to create a predictive model that incorporates additional internal and external features to improve accuracy. Making the Choice: Annualizing or Forecasting? Annualization and forecasting each have their strengths and weaknesses. Deciding between them depends on your data and your stakeholders’ needs. Sometimes a rough approximation is good enough; other times, a precise estimate or a range of values (e.g., a confidence interval) will be required. Annualization Forecasting Only considers data from the current year Can leverage data from multiple years Only needs a single month of data to start the estimation process One Model will need at least 4 data points to produce a forecast, but forecast accuracy suffers with so few data points unless the metric progresses in a very linear fashion Does not adjust for seasonality or trend Accounts for trends and seasonality Very simple approach requiring little input regarding computations and easy to understand More sophisticated approach that may prompt questions from end-users (luckily One Model provide embedded information describing the forecast) Estimates made early in the year are likely to be inaccurate Estimates made early in the year are likely more accurate than Annualization, especially when data from the prior year are utilized Will always underestimate or overestimate if a trend is present Can produce more accurate results even when trend is present Alternatives and Strategic Adjustments Alternatives like the 12-month rolling average provide another strategy for estimating year-end values, accommodating changes anticipated over the year. For specific metrics, like annual turnover, manually adjust the year-end prediction using expert analysis or the expected effects of internal actions. Depending on the metric being forecasted, it may also be reasonable to manually adjust the year-end value based on general projections for the year. For instance, to predict next year's annual turnover, start with the current end-of-year rate and refine it using projections from external experts or by considering the expected effects of internal measures aimed at reducing turnover. One Model Simplifies Forecasting and Annualization You might encounter scenarios where estimating year-end values for a metric is necessary. Although predicting these values with absolute precision is challenging, One Model can generate reasonable estimates, bearing in mind that sudden changes mid-year could significantly affect forecast accuracy. In practice, forecasting, particularly with One Model's Embedded Insights, tends to be more effective than annualization, especially at the start of the year. However, the accuracy of forecasting is impacted by decisions related to data inclusion and model parameters. Forecasting may also require a bit more effort to maintain, albeit minimal. Fortunately, One Model simplifies the use of both annualization and forecasting. In fact, using both methods to create estimates can be practical. When the results are close, opting for the annualized figure might be preferable for its simplicity. If results differ, the underlying data should be evaluated and the method that best aligns with the data’s characteristics should be used. One Model has you covered regardless of the situation you face and the approach you prefer or choose.
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8 min read
Mar 29, 2024
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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6 min read
Mar 28, 2024
In a timely conversation on DEI data, Phil Shrader of One Model and Season Chapman and Yuli Lopez of Culture Curated shed light on the importance of diversity and inclusion data analytics. While strides have been made in leveraging people analytics to propel the DEI movement forward, they reveal a stark reality: The journey towards achieving comprehensive diversity data standards is far from over. What’s Missing and What’s Present in Your DEI Data? As we delve deeper into the complexities of gathering DEI data, it becomes evident that significant gaps in what is collected hinder progress toward truly inclusive environments. Critical areas needing attention and improvement include: Performance reviews and gender bias Season Chapman highlighted a concerning statistic: In a significant study, 66% of women received negative personality-related feedback in performance reviews, compared to less than 1% of men. (Source) This discrepancy not only exposes a gender bias but underscores the need for a more nuanced approach to evaluating performance and collecting performance data. By systematically analyzing both the written and verbal components of reviews, organizations could begin to identify biases entrenched in their evaluation processes. Ageism and Strength-Based Diversity The often overlooked dimension of age bias, dubbed by the American Psychological Association as 'the last socially acceptable prejudice,’ highlights a gap in DEI initiatives’ predominant focus on racial and gender bias. Season also highlighted the tendency to emphasize weaknesses rather than strengths in organizational cultures. Incorporating strength-based analytics into DEI strategies could revolutionize how talents are matched with roles, fostering a more inclusive and productive workplace environment. Do you track and measure these 4 diversity metrics? Awareness of DEI Data Bias Types The above examples and many others highlight the significant potential for bias in data and data collection. Bias can exist within current data due to a variety of factors, including but not limited to: Historical bias can exist when past data, such as male-dominated hiring patterns, favors men over women for certain roles. Representation bias can occur when data used to train an algorithm may over- or underrepresent some groups. An example of this is found with facial recognition tools that produce higher error rates for certain groups. Measurement bias can happen when data that is collected disproportionately values behaviors or achievements that are more accessible to a particular group. Algorithmic bias can result when algorithms use their own predictions to make future decisions, which can replicate and even amplify existing biases in the dataset. It’s important to note that there’s no such thing as completely bias-free data. (Source) But we must seek to mitigate bias in our analytics by choosing effective technology, increasing our awareness of how it occurs, and applying safeguards. 3 Key Considerations in Advancing DEI Through Analytics Exploring the landscape of diversity data reveals three pivotal areas essential for effective DEI strategies: Accurately interpreting and applying DEI data: To achieve this, organizations can use advanced analytics and visualization tools that enable stakeholders to see beyond the surface-level numbers. This enables them to identify underlying patterns and insights that drive targeted, effective DEI interventions. Ensuring data collection methods capture the full diversity of an organization: This involves developing and implementing data collection strategies that are inclusive of all identities and experiences, thus mitigating biases that could skew the understanding of the organization's diversity landscape. Addressing privacy, confidentiality, and bias in data and algorithms: Organizations should establish multidisciplinary ethics committees that regularly review data collection, analysis practices, and algorithmic decisions for biases. This oversight ensures continuous alignment with ethical standards and promotes fairness and equity in all AI-driven DEI decisions. How One Model Supports DEI Initiatives Modern enterprises must do more than just track hiring metrics; they need to deeply analyze diversity data to drive genuine improvements. Leveraging people analytics software like One Model enables organizations to reduce bias and harness insights for crafting policies that foster long-lasting diversity and inclusion. Our clients use One Model's powerful analytics to visualize and monitor their DEI journey, establishing robust strategies that not only report but actively shape a more inclusive workplace. Ready to project your diversity in 5 years? One Model can calculate that for you.
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4 min read
Mar 12, 2024
The world of people analytics is at a crossroads. On one side, the potential for data-driven decision-making in HR is incredible, offering insights that can transform organizational dynamics and employee engagement. On the other, a stark reality exists: a significant gap in the talent pool, especially when it comes to finding talent ready to tackle the data engineering side of people analytics. This gap isn't just a minor inconvenience; it's a major roadblock for HR departments aiming to leverage the full power of data analytics. Let's unpack why this is a critical issue and how companies like One Model are addressing it. Talent Challenges in Building In-House Solutions Developing an effective people analytics platform is no small feat. It requires an end-to-end team with a diverse set of skills, from data engineering and data science to HR expertise and software development. But finding individuals who possess these skills is a daunting task that often requires extensive time and resources to source, recruit, and onboard. Once onboarded, the innovation gap can become quickly apparent. Data engineers and data scientists thrive on solving novel complex problems, but we’ve seen the maintenance and iterative improvement of in-house HR technology can lead to disengagement and high turnover for this group. Especially given the rare blend of skills these professionals possess and high market demand. Moreover, every hour spent by your HR or IT team on developing, troubleshooting, and maintaining an in-house analytics solution is an hour not spent on value creation or strategic initiatives. As organizations grow and change, so too do their analytics needs. Building a solution that can scale and adapt with these changes without significant additional investment is a formidable if not impossible challenge, often straining resources further. Platforms are not a one-and-done investment. The Strategic Advantage of Vendor Partnerships Partnering with a people analytics platform vendor like One Model brings a wealth of experience and a team of experts who are continuously engaged in the development and refinement of the platform. This immediate access to expertise translates into scaled reporting and sophisticated analytics capabilities that are ready to use on day one. By starting with a vendor, organizations can keep internal resources focused on strategic priorities, leveraging the headstart provided by the vendor rather than getting bogged down in technicalities. Vendors operate at scale, serving multiple clients with the same infrastructure. This allows them to offer powerful analytics capabilities at a fraction of the cost it would take to develop similar functionalities in-house. Additionally, vendors are motivated by competition and client’s needs to continuously innovate and improve their offerings, ensuring organizations benefit from these innovations without additional investment. One Model: A Case Study in Vendor Excellence When it comes to overcoming the talent challenges of building and maintaining a sophisticated people analytics platform, One Model stands out. Not only do we offer incredible careers for data engineers, working on challenging and impactful projects across the analytics space, but we also maintain an incredible retention rate for that talent. Our approach to dedicated support means that the data engineer who implements your solution often stays on to support your subscription, offering deep familiarity with your organization's specific needs and challenges. Our leadership, including our CEO who comes from a data engineering background, ensures that our solutions are not only technically advanced but also perfectly tailored to the real-world needs of people analytics. This level of expertise and commitment positions One Model as a partner who understands the intricacies of people analytics from a data engineering perspective — making us an attractive, cost-effective, and strategically sound alternative for organizations looking to leverage the full power of people analytics without the challenges of staffing. Conclusion While building a people analytics solution in-house from the ground up may seem appealing, the practical challenges and talent implications often make partnering with a vendor the safer choice. One Model offers a history of success, expert support, and an innovative platform that continues to evolve to meet your organization's needs, ensuring you remain at the forefront of HR technology revolution. Choose One Model, where data engineering talent meets HR innovation, and let us help you leverage insights to attract, retain, and develop top talent effectively. Learn how One Model can help you.
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5 min read
Feb 20, 2024
In today's dynamic job market, the transition to skills-based hiring is gaining momentum. This approach focuses on evaluating candidates based on specific skills rather than traditional factors like education and work history. However, as HR professionals, it's essential to recognize that skills-based recruitment can only reach its full potential when built upon a solid job title taxonomy. The Missing Link: Job Taxonomy A job taxonomy or job architecture is like the foundation of a house – essential for stability and structure. It's a framework that classifies jobs based on a variety of factors and needs. Think of it as a common language that allows everyone in your organization to clearly understand the definition of roles and their place in the bigger picture.. This is an important start because if we don’t have agreed-upon language to talk about the hierarchies of roles, even your most basic reporting fails. Without a clean job taxonomy, you could easily find yourself struggling to report on something as basic as "engineering talent." In the past, teams might have managed without a perfect job title taxonomy, but those days are long gone. With the growing complexity of the workforce, an increase in HR technologies, and the need for firm foundations for people analytics, a well-structured job taxonomy is now essential. Addressing the Pitfalls in Skills-Based Approaches Unfortunately, there seems to be a growing misconception that skills-based hiring methods somehow eliminate the need for clean taxonomy and data architecture. This oversight is akin to skipping your vegetables – it might seem tempting, but it's not sustainable. This fallacy is based in part on the limitations of current skills-based hiring itself and the need for more case studies in practice. A good starting point includes: Recognizing the Path It can be helpful to see skills-based hiring not as the perfect, new, fully-formed solution for workforce management, but as a step in the evolution from education-based and job-title-based approaches. Both of those prior methods were shortcuts that never got granular enough to really capture human capability. While a skills-based approach is an improvement, it’s still simply a shortcut to understanding human capabilities. Right now, it loosely conveys "we're going to be more careful" in assessing candidates against the actual requirements of the job. The question is whether organizations have the data architecture to support it. And are they getting the buy-in from other business functions to capture the true value of becoming skills-based? Improving Our Shared Language Skills-based hiring isn't just about evaluating skills for individual positions; it's about identifying critical skills that drive business growth and introducing language that clarifies the space. For example, expected skills should align with job profiles, while assessed skills reflect individuals within your company. And it doesn't seem like anyone is even talking about 'potential skills' yet. These distinctions are crucial for clarity. Balancing Granularity and Hierarchy Just as we would say "workspace" instead of listing every item on our desk, skills-based hiring requires a balance between granularity and hierarchy. While detail is necessary for technological advancements, we still need the broader terms for everyday conversations about work. For example, it would be helpful to list all the stuff on and around our desk to an organizational consultant who was helping us tidy up, but "workspace" is sufficient for most conversations. The same thing applies to skills. In some cases, saying "People Analytics" skills is more practical than listing specific roles like data analysis, storytelling, data engineering, consulting, or research. But in others, it could cause confusion to try to have a discussion at that level. We need that granularity of an individual skill to enable tech advances (e.g. talent marketplaces, job matching, talent assessments). But we still need the hierarchy and rollups of the skills into roles and job families to continue our day to day conversations about workforces. Both are required to make skill conversations meaningful. Think of Job Taxonomy as a Verb It can’t be overstated that job taxonomy isn't a one-and-done task; it's a living entity that evolves with your organization and pays out dividends over time. It should perhaps be thought of as an ongoing verb, not a one-time noun. And a clean taxonomy’s pivotal role in various HR functions – from workforce planning and compensation analysis to talent acquisition and learning and development – highlights even further how important it is. Unfortunately, its initial price tag can appear high enough that some teams have trouble forecasting the benefit. Job title taxonomy is tied into so many projects, though, that it's a must-have as soon as you can get it. Without solid taxonomy, integrating skills in particular into the recruitment process becomes a daunting or impossible task. For now, starting with static expected skills for current jobs, updated quarterly or even annually, would be a massive first step from a profile-based view of the world and unlock a lot of new opportunities. It's good to start small in this space. The Starting Point: Standardized Job Taxonomy In the meantime, perhaps we can somehow translate the fervor around skills-based hiring into conversations about meaningful data architecture and data engineering funding. While not as glamorous, data standardization is an indispensable foundation for the success of skills-based recruitment, the glue that holds it together. One Model helps by helping you set up a true people data platform that is customizable and transparent. Learn how to build a people data platform that will allow you to do better skills-based hiring.
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9 min read
Feb 07, 2024
In organizational management, span of control plays a key role in defining how streamlined and agile a company can be. Understanding the Span of Your Manager-to-Employee Relationships At its core, span of control refers to how many people a manager or supervisor directly oversees. The optimal number depends on a variety of factors including job type and job level, and most organizations set targets using rules of thumb and experience. The span of control metric helps determine if the organization is structured appropriately, with too large a span of control leading to ineffective management and manager burnout, and too small a span of control leading to inefficiency. To calculate your average span of control, divide the total number of direct reports by the total number of supervisors. For instance, if there are 100 direct reports to 10 supervisors, the average span of control is 10. Exploring the 2 Types of Span of Control In the context of organizational structure, span of control is classified as either wide or narrow. Each type presents unique advantages and challenges, so it is not a one-size-fits-all proposition. The choice between a wide and narrow span of control depends on various factors, including: The nature of the organization's work and its structural preferences Industry norms Complexity of tasks Managerial capacity Job level Both wide and narrow spans have their place, even across departments and job levels within an organization. The key is to find a balance that maximizes efficiency, promotes effective management, and aligns with the organization's overall goals. Wide Span of Control In a wide span of control, a single manager supervises many subordinates. This structure is often seen in companies with flat organizational structures, with fewer layers between the top and bottom levels and a shorter chain of command. Wide structures are also more common at lower levels in organizations. Features: Low supervision overhead costs Prompt response from employees Improved coordination Suitable for repetitive or low-skill tasks Advantages: Encourages delegation of authority Facilitates better manager development Ensures clear policies Promotes autonomy among subordinates Fewer levels in the managerial structure Cost-effective Suitable for larger firms and repetitive tasks Well-trained subordinates Disadvantages: Risk of supervisors being overburdened Potential loss of control for superiors Need for highly qualified managing employees Hindered decision-making Increased workload for managers Unclear duties for team members Confusion among subordinates Management challenges in large teams Reduce manager-employee interactions Narrow Span of Control Conversely, a narrow span of control is characterized by a manager overseeing a smaller number of subordinates. This approach is prevalent at the top or middle management levels, especially when tasks are complex and require more support from superiors. Features: Ideal for new managers to gain supervisory experience Beneficial for managing remote or diverse teams Necessary for jobs requiring frequent manager-employee interactions Useful in new operations and for employee training Advantages: Easier communication and management in small teams High specialization and labor division Better opportunities for staff advancement Direct supervision by managers over each subordinate Effective communication between subordinates and managers More layers in the management structure for easier control Improved management control and effective supervision Disadvantages: The potential of stifling of employees' creativity due to excessive manager control Slower decision-making in extended hierarchies Limited cross-functional problem-solving Higher costs due to more managerial positions Delays in information transmission and decision-making The Challenge of Manual Span Management Effective span management is a balancing act, nearly impossible to achieve without technology. Strong span management requires examining spans vertically, horizontally, and over time; this creates a complex situation that is not easily or effectively handled without well-orchestrated data. Span Management Impacts A high manager-to-employee ratio might lead to insufficient attention to each team member, potentially affecting employee development and performance. Conversely, a low ratio could indicate inefficiencies and a bloated organizational structure that erodes profitability. Span Management in Different Industries Span management requires a tailored approach, as the ideal ratio varies by industry and job function. In labor-intensive industries, a higher ratio is often more manageable, whereas in knowledge-based sectors, a lower ratio might be preferable to ensure quality supervision and mentorship. Seasonal Staffing Certain industries or departments may experience fluctuations in workload at different times of the year, necessitating a flexible approach to span management. During peak seasons, managers may need to handle more direct reports or delegate responsibilities more effectively, while in slower periods, they may focus on training and development. A dynamic strategy can maintain efficiency without compromising the quality of supervision or employee growth. The Role of HR and Analytics in Span of Control Human Resources plays a critical role in monitoring and adjusting the span of control. HR can track this metric in real-time by using analytics tools to help maintain an optimal balance. People analytics software like One Model offers capabilities to analyze and adjust management span of control across various levels and departments, ensuring organizational efficiency and employee satisfaction. Data-Driven Span of Control Analysis Span of control analyses help organizations identify optimal structures and make precise staffing decisions in response to changes over time. Using people analytics tools, HR can dissect span of control across different dimensions such as department, geography, and manager level. Analysts should examine span of control: Both vertically and horizontally, and over time Relative to gross and net revenue Relative to employee-related outcomes such as engagement and retention It is not practical or effective to evaluate and manage span of control manually; this is an area where robust data can be used to drive effective decision making and optimize outcomes. However, to kickstart this analysis, even basic data from a core HCM or HRIS system can be enlightening. Metrics like span of control and organizational layers are akin to stepping on a scale — they provide immediate feedback on the state of your organizational structure. Within this discussion, key metrics such as span of control trends and visualization of layers and organizational units are invaluable. One crucial metric, for instance, is the number of managers with only one or two direct reports. This simple statistic can reveal much about the nature of your management structure. These insights are essential for keeping talent management processes aligned with business reality. If your current team or technology cannot readily provide these views, it may be time to reconsider your approach and tools. It took our team under 5 minutes to find the ratio between managers and non-managers. How long will it take your team to answer Question #38 on the People Analytics Challenge? Setting Targets for Span of Control Setting the right targets for span of control involves considering various factors, including industry norms, organizational structure, and management levels. A higher ratio may be effective for frontline or production roles, while senior management might require a lower ratio to strategize and lead effectively. Organizations often set their span of control targets based on industry benchmarks, aiming for a median that balances efficiency and managerial attention. Variations in span of control targets can be set for different organizational units, such as contact centers, corporate offices, and field operations. But the best organizations strive to surpass industry norms and link span of control metrics with outcomes of interest such as efficiency, profitability, employee engagement, and voluntary turnover. By doing so, they can optimize span of control to drive desired outcomes. Mastering Span of Control with One Model Understanding and effectively managing the span of control is crucial for any organization seeking to optimize its structure for maximum efficiency and employee development. With One Model, organizations can gain the insights needed to make informed decisions about their management structures, ensuring they are well-equipped to adapt to changing market demands and internal growth dynamics. One Model also supports next-level span-of-control analytics by allowing organizations to link span-of-control with operational metrics, moving the organization from descriptive analytics into the realm of optimization. After all, blindly following industry benchmarks won't ensure optimization within the organization. One Model is equipped to support optimization through the modeling core HRIS data, employee engagement data, employee performance data, and operational data related to production, safety, and financial outcomes. If you aren’t using a tool to measure and track span of control, you’re missing out. If you aren’t linking span of control to business metrics that matter, you’re really missing out.
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6 min read
Feb 02, 2024
In the rapidly evolving field of People Analytics, a pressing roadblock has come to the forefront: the need for remote eligibility in senior roles. This isn't just a passing trend; it's a strategic imperative shaped by market realities and the nature of People Analytics itself. Let's dive into why every organization looking to lead in People Analytics should consider making their senior roles, if not all of their People Analytics roles, remote eligible. 1. The Scarcity of Senior People Analytics Leaders The first point to consider is the tight talent market of senior People Analytics leaders. In cities big and small, from New York to San Francisco, the pool of top-tier professionals in this niche field is still small. My experience in talent intelligence and location strategy shows that expecting to find a world-class leader in your immediate vicinity is wishful thinking. Opening up the Search for Remote Talent With a remote search, organizations can open their roles to a wider, more diverse range of candidates. This approach isn't just about filling a position; it's about finding the matched people analytics leader for your organization who can bring the right perspectives and drive innovative strategies in People Analytics. 2. Talent Density and the Geographical Challenge People Analytics, a relatively nascent and specialized field, overall lacks the talent density seen in more established areas of HR like recruiting or compensation. This reality requires a more tailored approach to building and leading teams, usually involving multiple sites and sometimes sites in multiple countries. Increasingly, People Analytics teams are distributed, with components in multiple locations or even outsourced, which essentially establishes the team as a remote team. “If one person on the team is remote, the team needs to act like a remote team” - Darren Murph (Remote Work Expert) The Case for Remote Leadership In such a scenario, anchoring a leader to a single location is counterproductive. A leader's effectiveness in People Analytics hinges on their ability to manage and integrate their team. Remote work facilitates this by allowing the People Analytics leader to lead by example, demonstrating what it means to be remote at the company. 3. People Analytics Teams: Pioneers of Remote Work Research A critical aspect often overlooked is that People Analytics leaders are not only avid followers of the academic work in this area but also that they are likely to be the pioneers of remote work research. Over the past five years, these senior leaders and their teams have studied and understood the nuances of remote, hybrid, and in-person work models. The Informed Choice of PA Leaders People Analytics leaders are making informed personal choices based on their research and understanding of work models. They're increasingly opting to stay put or seek remote roles, knowing full well the impact and potential of remote work arrangements. This trend isn't just about personal preference; it's about leading by example and embracing what they've learned through their research. 4. The Wide Reach of People Analytics People Analytics is not confined to a single department, function, or stakeholder; it spans across the entire organization (even outside of HR). Senior leaders in this field need to interact with various stakeholders across different departments and locations. Remote Work: A Practical Necessity Given this broad scope, the traditional model of a leader bound to a single office location becomes impractical. Whether it's through phone, video calls, or email, much of the People Analytics leader's role already functions in a remote capacity as they interact with a variety of stakeholders globally on a daily basis. Formalizing this as a remote role eligible role acknowledges the existing operational reality. 5. The Relocation Resistance Among PA Leaders In my interactions with job seekers and executive candidates that we’ve spoken to as part of the One Model People Analytics roles page project, a clear trend emerges: top talent is increasingly reluctant to relocate. They are turning down roles that require them to move or just not engaging with those recruiters. This isn't just a preference; it's a decisive factor in job selection. The Untapped Talent Pool There is a significant talent pool waiting for remote opportunities. Organizations not offering remote options for positions like PA Leader, PA Director, or VP of People Analytics are missing out on this talent. This isn't about accommodating personal preferences; it's about accessing the best in the field. Join the conversation on Linkedin. Conversation with feedback from PA Leaders Summary: A Call to Action for the HR Community The evidence is clear: the future of successful People Analytics builds lies in remote eligibility for hiring. While there are arguments for in-person roles, maybe for junior staff (largely unproven), the need for remote eligibility in senior positions is undeniable. As an HR community, we must recognize and adapt to this reality to connect the best talent to the right teams. Embracing Remote Work It's time to rethink how we approach senior roles in People Analytics. By embracing remote work, we can tap into a broader talent pool, foster innovative leadership, and align with the forward-thinking nature of People Analytics. Post your Senior People Analytics roles as remote opportunities! People Analytics Roles. Employers: Need a secure people analytics platform that ensures you can have a remote workforce? Reach out for a demo of One Model.
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17 min read
Jan 19, 2024
The landscape of People Analytics and HR Technology is rapidly evolving and staying on top of the latest trends and insights is crucial for professionals in this field. To understand where other experts are turning for their insights and inspiration in 2024, we surveyed people analytics practitioners. Our aim? To discover which conferences are on their radar - the ones they plan to attend and those they aspire to make it to someday. Let's dive into the results of this survey, revealing what's hot on the conference circuit this year! Who's in the Spotlight? Role of Respondents We had a diverse group of practitioners from a number of disciplines, but a vast majority were working on or for people analytics teams and projects (100+ of the responses). A handful of HR tech and HR Ops leaders who did not have People Analytics teams also replied. We had a small number of vendors/consultants and academics take the survey as well. While these groups were not the primary focus of our analysis, their presence reflects the conferences' role in business development, networking, and the diverse perspectives in the field today. Seniority Breakdown Director+: A third of the respondents held positions at the director level or higher. This substantial representation emphasizes the strategic importance these conferences hold for senior decision-makers. Manager / Sr. Manager: Approximately a quarter of respondents were in managerial roles (either Manager or Senior Manager). It's worth noting that in many developing people analytics teams, these roles might be the highest-ranking members as the function evolves. Individual Contributor: The remaining respondents (~40%) identified themselves as Individual Contributors, constituting the most represented group. This not only underscores the active interest and participation of operational-level professionals in people analytics conferences but also accurately reflects the structure of seniority within People Analytics teams. Naturally, due to organizational design, there will be more Individual Contributor representation than Director/Manager. Company Size Breakdown 20,000+ Employees: This is the largest segment, comprising about half of the respondents. It suggests that major corporations view these conferences as crucial for their people analytics strategies and initiatives. These are also likely the teams with available budgets for professional development. 5,000-20,000 Employees / 1,000-5,000 Employees: Roughly 20% of respondents belong to each of these ranges. This underscores the importance of these conferences for large organizations that have established people analytics functions but aren't as big as the largest corporations. Less than 1,000 Employees (a. <1,000): The smallest segment unsurprisingly comes from smaller organizations. We’ve heard that in these organizations, employees often have multiple roles or teams that they balance, making it difficult to find time to attend these conferences. Top Conferences on the Radar Drumroll please, here are the top planned conferences! Planned Conferences Four conferences were identified by more than 20% of practitioner respondents as events they would attend. Several other conferences fell within the 10-15% range. However, for simplicity, we've only listed conferences that garnered 20% interest or higher below. There are indeed many excellent conferences (more listed below), but these are the standouts this year: SIOP Chicago Roughly 1/3 of those who replied to the survey planned to attend SIOP! This stood out by far from the other conferences. For some in the people analytics space, this may come as a surprise, but for those who have attended SIOP in the past, this makes a lot of sense. SIOP attendees are loyal. SIOP is the annual gathering of the Society for IO Psychology professionals (but open to anyone interested) and sees upwards of 5,000 IO Psychologists descend on a new city each year for four days of intensive conference activities. With around 10 concurrent sessions every hour, there is content for everyone, leading to a healthy dose of FOMO. Specifically, SIOP provides a fantastic experience for people analytics leaders and practitioners. I had the opportunity to attend SIOP in 2023, and it was memorable for its rigorous debates, insightful discussions, and excellent networking opportunities. The sessions I attended on AI ethics, employee listening, recruiting analytics, and assessments were some of the best in-person content I've experienced. Additionally, the impromptu conversations in the hallways with new and old friends were incredibly valuable. If you’d like to learn more about SIOP and how a People Analytics team may benefit, please read my review here: A People Analytics Journey to SIOP! I am thrilled to attend again in 2024 and have the privilege of presenting a Machine Learning Operations masterclass with Rob Stilson and Derek Mracek (more details to follow). If you're planning to attend, please let me know! Local PA Meetups Chosen by a third of respondents and in a close second to SIOP, local meetups interest is still going strong (and it feels like it’s rising). NYC and Bay Area still lead the pack as the earliest meetups and strongest communities, but we’ve seen dozens of meetups spring up (in the US at least) over the past few years (including Pittsburgh here in my backyard!). As part of participating in this survey, I'll be connecting people with others in their local community to initiate more meetups. So, stay tuned for updates. People analytics can often feel isolating for small teams. Therefore, I urge everyone reading this to take note of your local meetup and try to attend if possible! We've also included a comprehensive list of known local meetups at the end of this blog post (jump to end of blog) Wharton People Analytics With approximately a quarter of respondents, Wharton stands out as one of the few conferences in the US solely dedicated to people analytics and not affiliated with a vendor. Now in its 11th year in 2024, the Wharton conference is academically rich and rigorous. Although I haven't personally attended before, I'm looking forward to participating in March this year! HR Technology Conference and Exposition (Las Vegas) Rounding out the top four, a quarter of respondents indicated that HR Tech in Vegas is the place to be, underscoring both how significant technology choices are to People Analytics teams, but also the density of talent that makes its way to Vegas for HR Tech. With nearly 10k attendees, the vendor floor is a spectacle and an exciting way to see the showcase of technology supporting the people analytics and broader HR space. If you’re a PA leader who also oversees or interacts with Tech, it’s a must-attend event each year. Wish List Dreams We also asked respondents which conferences were on the practitioners' wish lists. Four conferences stood out that People Analytics practitioners wish they could attend: Wharton People Analytics A repeat from the list above, about half of the respondents wish they could attend the Wharton People Analytics conference. As mentioned, Wharton has been a staple in the community for well over a decade now. It’s unclear why more folk can’t make it to Wharton PAC, but I’ll make sure to take rigorous notes later this year and will report back on insights and takeaways. Make sure to subscribe to our newsletter to hear more throughout the year! People Analytics World - London (Tucana) Well over a third of respondents wish they could attend People Analytics World London. PAW London is a dedicated gathering of people analytics practitioners put together by Tucana. It’s a staple in the field and always draws mature people analytics teams and world-class speakers. Tucana has also recently branched out to supporting workforce planning and a number of other events globally and is a leading provider supporting the PA community. Definitely one to try to attend if you can! Insight222 Global Executive Retreat Nearly a quarter of respondents wish they could attend the Insight222 Executive Retreats, but compared to many others on these lists, these executive retreats are invite only. Insight222 is the premier membership organization for people analytics teams and from what I hear, these events are meticulously planned, organized, and executed. Bravo to the Insight222 teams for curating these experiences and if you ever change your mind about speakers from outside vendors coming to speak… you know where to find me. Gartner ReimagineHR Rounding out the top 4 is Gartner ReimagineHR. Gartner ReimagineHR is a premier conference for HR leadership with a specific focus on CHROs and CHRO directs. The quality of conversation is high and the maturity of teams is elevated. I missed this one in 2023, but after hearing reports from folk who attended, it’s not one I’ll miss again. Looking forward to attending this one in 2024 too. Those are the most popular events, but many world-class events were not mentioned. We've compiled our list below and appreciate all who submitted people analytics conferences. If you found this helpful, please let us know. If it proves beneficial, we'll compile a similar list again next year. One Model + Lightcast + Worklytics = The Talent Intelligence & People Analytics Summit And we here at One Model have got some of our own events coming together in 2024! The main one to highlight is a roadshow we’re putting together with our friends at Lightcast and Worklytics. The Talent Intelligence & People Analytics Summit is traveling to a few select cities in the US across 2024, starting with Austin, Texas on February 7th! It’s not too late to register. Finally, I hope to see you out there in 2024! Make sure to follow me and the One Model page to stay connected to us out in the field! 2024 events Follow One Model on LinkedIn and check out our events page. Transform US | 11-13 March 2024 | Las Vegas Wharton People Analytics | 14-15 March 2024 | Philadelphia SIOP Annual Conference | 17-20 April 2024 | Chicago People Analytics World - London | 24-25 April 2024 | London Unleash America (Las Vegas) | 7-9 May 2024 | Las Vegas 9th Annual People Analytics Summit (Toronto) | 14-15 May 2024 | Toronto TALREOS | 16-17 May 2024 | Chicago Irresistable 2024 (Bersin) | 20-23 May 2024 | Los Angeles Oracle Ascend | 17-20 June 2024 | Las Vegas SHRM Annual Conference & Expo | 23-26 June 2024 | Chicago People Analytics Exchange (IQPC) | 25-27 June 2024 | Minneapolis HR Analytics Summit (London) | 4 September 2024 | London Workday Rising | 16-19 September 2024 | Las Vegas HR Technology Conference and Exposition (US) | 24-27 September 2024 | Las Vegas Unleash World (Paris) | 16-17 October 2024 | Paris SuccessConnect (SAP) | 28–30 October, 2024 | Lisbon Gartner Reimagine (Orlando) | 28-30 October 2024 | Orlando HR Analytics and AI Summit (Berlin) | 24-26 November 2024 | Berlin Is the wait too long? You don't need to wait till the next event to talk to One Model (although we're excited to see you in person). Connect with us today. 2024 Meetups And now time for the meetups! These meetups happen frequently throughout the year, so the best wya to be involved and stay involved is to connect with their local site / meetup / LinkedIn group. Where we can, we’ve included some details about how to connect and when there was not a site yet available, we’ve added in local organizers. Brisbane (AU): March 27th at 8pm AEST (Link to event) New York: https://lnkd.in/gbfu_Mjc (Jeremy Shapiro / Stela Lupushor) Bay Area: https://lnkd.in/gnrgRBnH (Annika Schultz / Mariah Norell) Chicago: https://lnkd.in/ghgc3EDb - (Chris Broderick) Philadelphia: https://lnkd.in/g-bWmX5y - (Fiona Jamison, Ph.D.) Pittsburgh: https://lnkd.in/eCdP7KFC (Ken Clar / Richard Rosenow) Minneapolis: https://lnkd.in/eS2aUH3W (Stephanie Murphy, Ph.D. / Mark H. Hanson) Seattle: Bennet Voorhees / Marcus Baker / Philip Arkcoll Denver: Kelsie L. Colley, M.S. ABD / Zach Williams / Gabriela Mauch Boston: Hallie Bregman, PhD / Noel Perez, PMP Dallas: Jordan Hartley, MS-HRM / Cole Napper Austin: Ethan Burris / Roxanne Laczo, PhD Houston: Amy Frost Stevenson, PhD / Jugnu Sharma, SHRM-CP Atlanta: Sue Lam Nashville: Dan George Orlando: James Gallman / Danielle Rumble, MBA Omaha: Justin Arends Salt Lake City: Willis Jensen Toronto: Danielle Bushen / Konstantin Tskhay, PhD Washington DC: Rewina Bedemariam Portland: Rosanna Van Horn
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Featured
12 min read
Dec 19, 2023
Understanding the subtleties of organizational promotion dynamics offers a window into career advancement opportunities or lack thereof and can uncover both desirable and undesirable organizational outcomes. Key among these insights is the internal promotion rate. This metric goes beyond mere numbers to reveal the depth of employee development, engagement levels, and the effectiveness of human resources strategies in fostering high performers and nurturing talent for higher-level job roles. Analyzing Organizational Health Through Promotion Metrics The internal promotion rate, calculated as the percentage of employees promoted in a given period, can offer human resources professionals a clear view of their employee development opportunities. Strong promotion rates tell us that development opportunities are available and being acted upon, and we can use these rates to identify areas of celebration and opportunity with the organization. Promotion activity is linked with retention as employees who receive promotions are encouraged to stay through increased pay and responsibility, and it shows other employees that growth, development, and advancement opportunities are provided by the organization. Real-time promotion data becomes a strategic asset in understanding how promotions influence employee morale and retention, and allows HR leadership to craft promotion protocols and goals. Here are several analyses where promotion rates could be used: Career Path Ratio - Used to gauge the success of internal grooming of managers, performance management process controls or compensation cost management. Cross-Function Mobility - This metric identifies skills development of high-potential employees through diversity of experience. Promotion Speed Ratio - Once you have the promotion rate, you can divide total tenure to understand how fast employees are being promoted. Upward Mobility - You can divide promotions by internal movements to understand your upward mobility paths compared to all. Performance to Promotion ratio - This will help you understand how performance ratings correspond to promotions. Gender and Ethnic Diversity Staffing Breakdown - Examining gender and diversity breakdowns with hierarchical levels and promotion rates can help identify and address any "glass ceilings" that may be present, offering a more comprehensive understanding of gender and diversity dynamics within the workforce. Turnover Breakdown - Assessing turnover rates in tandem with gender and ethnic diversity breakdowns and promotion rates is crucial for gaining a holistic perspective on the organization's gender- and diversity-related challenges and opportunities, contributing to a more nuanced analysis of workforce dynamics. Promotions into higher level positions, achieved through performance management cycles, non-competitive moves, or competitive moves, reflect strong individual performance and readiness for greater responsibility. They serve as recognition and rewards for outstanding performance within an organization. While a high promotion rate suggests robust performance and managerial strength, it could also result from flawed performance management or succession planning processes. Conversely, a low rate may indicate a lack of qualified internal talent or organizational constraints that make it difficult for internal talent to move upward. Low rates could also reflect a bias towards “buying” external talent. Analyzing promotion rate requires considering an employer's performance management policies for accurate interpretation. Ultimately, the internal promotion rate is a multifaceted indicator, reflecting how effectively an organization nurtures its talent and commits to long-term development while upholding diversity and inclusion principles. How to Calculate Promotion Rate The internal promotion rate is a straightforward yet revealing metric. First, you need the right data to get the right answers. Locate both your core workforce and mobility data. Formula: Promotions / Average Headcount * 100 For example, suppose your organization has 500 employees on average for the period and 50 employees were promoted within a year. The internal promotion rate would be: 50 / 500 x 100 = 10% However, this calculation only provides a surface-level understanding. Organizations must explore how promotion rates vary across different demographics and departments to gain deeper insights. How to Track Promotions Across Diverse Demographics Understanding how to track promotions effectively can provide crucial insights beyond mere statistical data; it highlights the diversity and inclusivity of an organization's workplace practices. By examining promotion rates across various workforce segments, including diversity groups, job roles, business units, age groups, and tenure groups, organizations can better understand their approach to career advancement and how it impacts different demographic groups. Diversity Groups and Career Advancement When analyzing promotion rates among diverse groups, it becomes possible to spot biases or disparities. This is crucial for ensuring that career advancement opportunities are equitable and accessible to all employees, regardless of their background. Today's businesses must monitor hiring metrics and analyze diversity reporting effectively to make meaningful changes. People analytics software can remove bias and give companies data to support workplace diversity and inclusion programs and policies. One Model customers like Colgate use our people analytics software to build powerful visuals to track and communicate their progress, increase workplace inclusion at every step, and build an enduring diversity-rich strategy. Job Roles, Business Units, and Growth Opportunities The potential for career growth and progression within an organization can vary significantly across job roles and business units. For example, a department experiencing rapid expansion may witness higher promotion rates as roles broaden and the demand for new leadership surges. This dynamic highlights the importance of strategic HR functions like succession planning in shaping career pathways and organizational resilience. Succession planning is more than just a process; it's a strategic effort to identify critical positions and groom potential successors for these key roles. When a vital role becomes vacant, organizations with robust succession planning can promptly fill the gap with a capable and prepared individual, enhancing operational continuity. This proactive approach ensures organizational readiness for future changes and signals to employees a clear trajectory for growth and advancement within the company. Effective succession planning intertwines seamlessly with people analytics. Clarifying objectives and progress is essential to shape the future workforce strategically. This clarity is best achieved through metrics that track and measure the effectiveness of succession strategies. Metrics and analytics can provide insights into the readiness of potential successors, the distribution of talent across the organization, and the impact of training and development initiatives aimed at preparing employees for future roles. Another crucial aspect of succession management is its ability to boost employee motivation. Employees are more engaged and motivated when they perceive opportunities for growth and advancement within their current organization. Seeing a well-defined path to potentially step into key roles enhances their commitment and drives their performance, aligning their personal growth aspirations with the organization's strategic goals. It took my team 5 min 16 sec to pull these charts together. How quickly can you complete the People Analytics Challenge? Age, Tenure, and Upskilling Younger employees or those with shorter tenures tend to have steeper promotion trajectories than their more experienced counterparts. Early career persons will need to be promoted more frequently as they begin to master their discipline. The time between promotions tends to increase as higher job levels are achieved. The time it takes to go from Specialist to Manager is typically much shorter than going from VP to SVP. PwC’s Global Workforce Hopes and Fears Survey found the biggest priorities for younger workers are training, development, flexibility, autonomy, and transparency on social issues. One key distinction among Generation Z workers (ages 18 to 24) is that they are more vocal in their demands than older generations. Specifically, they are more than twice as likely to ask for a promotion in the next year (38% of Gen Z and 37% of Millennials, compared to 16% of Baby Boomers). High-potential employees, especially, are keen to embrace new challenges, learn, and grow. Providing robust professional development and training opportunities boosts employee confidence and enhances their value to the employer. This investment leads to higher performance, satisfaction, and productivity, positively impacting the internal promotion rate. Organizational Culture and Structure The shared values, beliefs, and behaviors within an organization significantly influence employee experiences and, by extension, their promotion prospects. A positive workplace culture correlates with higher sales, profits, and productivity, while a negative culture can drive high turnover. HR professionals can assess employee performance against the organization's culture and values to understand how workplace culture affects employees. Changes in organizational structure have been shown to impact employee performance directly. HR can be pivotal in shaping workplace culture to enhance promotion rates. This involves fostering open communication, transparency, and respect across the organization and ensuring all employees feel included. Company values should be reinforced during onboarding and through ongoing training and leadership programs. Limitations of Promotion Rate Metric This metric falls short of providing a comprehensive evaluation of promotions in relation to other internal movements, such as transfers. It does not delve into the nature of promotions, whether they transpire within the routine course of performance reviews or if they involve transitions into higher-level positions within different organizational units. Furthermore, it lacks specificity regarding the hierarchical level at which these promotions occur. Importantly, it does not shed light on the consequential aspects of promotions, such as changes in compensation or increased responsibilities that often accompany these advancements in an individual's career trajectory. In order to obtain a more nuanced understanding of the dynamics at play, a more comprehensive assessment that encompasses these facets would be necessary. Streamlining Promotion Tracking with One Model How promotions are tracked within an organization is just as crucial as the data itself. An effective tracking system not only gathers data but also segments it meaningfully. This is where One Model plays a pivotal role, offering an advanced yet user-friendly suite of tools that effortlessly disaggregate data across various essential metrics. Simplifying Complex Data with One Model — One Model’s platform is designed to ease tracking and analyzing promotion data complexities. With its sophisticated, intuitive interface, HR professionals can quickly segment and analyze data across departments, gender, diversity, and cohort groups. This comprehensive approach enables organizations to gain deeper insights into their promotion dynamics and make more informed decisions. Departmental Breakdown Made Easy — Understanding promotions within specific departments is critical to identifying growth opportunities and potential areas for improvement. One Model allows for a detailed departmental breakdown, clarifying which areas excel in employee advancement and which might need a more focused approach. Gender and Diversity Analysis for Inclusivity — A crucial aspect of modern HR is fostering an inclusive workplace culture. One Model simplifies the process of conducting thorough gender and diversity analyses. By providing clear insights into promotion rates across different demographic groups, One Model helps businesses ensure that all employees, regardless of gender or background, have equal growth opportunities. Cohort Group Analysis for Targeted Development — Cohort group analysis, such as examining promotion rates among new hires or high-potential employees, is vital for shaping effective talent development strategies. One Model’s tools enable HR professionals to perform nuanced analyses of various cohort groups, helping to tailor development programs and career progression pathways that align with individual and organizational goals. The OPM Promotion Calculator and GS Promotion Rate While discussing promotion rates, tools like the OPM promotion calculator, particularly relevant in government jobs utilizing the General Schedule (GS) system, are worth noting. Although indirectly related, such calculators can offer benchmarks for understanding promotion norms in broader industries. The Significance of a Comprehensive Analysis More than merely calculating the average promotion rate is required. A comprehensive analysis, considering all the segments above, is imperative. It ensures a fair and inclusive work environment and helps align workforce development with organizational goals. Understanding and effectively managing internal promotion rates is a multifaceted process. Organizations must go beyond merely calculating these rates; they should deeply analyze variations across demographics and departments. Tools like One Model facilitate this process, providing a seamless solution for tracking and analyzing promotion data, ensuring that every aspect of workforce development is aligned with broader business objectives. Ready to take your analysis to the next level? Request a demo and watch me breakdown promotion rate live.
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Featured
10 min read
Dec 11, 2023
Different teams, different vibes! As the holiday season approaches, the pressure to find the perfect gifts for coworkers can sometimes be overwhelming. Fear not, because we've curated a list of trendy and thoughtful presents tailored to various job categories. Whether you're shopping for a product manager, UX designer, HR professional, analyst, programmer, or tech support guru, we've got you covered. Product Manager Presents: At One Model, we have amazing Product Managers. I know they are always looking for ways to stay organized and love interacting with their peers. Therefore, I think these might be great presents for these coworkers. Desk Calendar - Sometimes old fashion project management gets the ideas flowing. Moleskin Notebook - A product manager will never hesitate to take down a note in style. Monitor Memo Board - When you have a lot of input to juggle, sometimes you want your notes close at hand. Virtual Team Building Experience - Foster team spirit with a virtual team-building work activity. Since we have a virtual team, we tried this service. UX Designer Delights: To thank your designers for all their hard work, why not get them something to stimulate their creativity? Book - Fuel their artistic soul with a beautifully crafted book of design inspiration. Universal Principles of UX and The Design Everyday Things are highly revered options. Digital Drawing Tablet - Boost their creativity with a state-of-the-art digital drawing tablet. Design Thinking Card Game - Spark creativity with a game that challenges designers to think outside the box. HR Heroes' Treats: HR is the bread and butter of any company, so we thought these little bits and pieces would be perfect: Human Resources Shirt - Growing companies are doing a lot of recruiting and an HR team member can appreciate this shirt. Online Course Subscription - Invest in their professional development with an online course subscription. Don't have much to spend? Send them to the HR Regulation Masterclass. Check out our blog dedicated to funny HR gift ideas. Analyst Appreciation: Funny Mug - Analysts and data scientists like to stay sharp. You can't go wrong with a fun mug. Premium Coffee/Tea Sampler - Speaking of stay sharp, that mug needs some fuel. Check out this world-traveled coffee sampler and this gourmet tea collection. Data Visualization Art - Telling the story visually is an art, and books like this are definitely inspirational. Cool earplugs - To identify key insights sometimes you need to block out the world. Sudoku puzzle book - Dare I say that Sudoku is one of the most fun ways to play with numbers. If you want to get more than a book, check out this sudoku game. Programmer Picks: If you are a programmer, you'll know programmers can never have enough coffee or goofy socks. This is why our gift list is filled with these things, along with some other goodies. Coding Socks - Keeping your feet warm in style. Check these fun ones out. Computer Programmer Coffee Mug - Like analysts, programmers also like to keep sharp lest they miss a semi-colon. Here is a fun mug. Floppy Disk Coasters - Don't let that coffee mug leave a ring on their desk. Check out these retro tech coasters. Coding-Themed Hoodie - Keep them cozy and stylish with a hoodie featuring clever coding graphics. Coding Puzzle Box - Challenge their coding skills with a puzzle box, like the rubics cube. Laptop Cooling Pad - Keep their tech cool and stylish with this cooling pad for their laptop. Tech Support Treasures: If you're the one employee who feels like they call their tech support person a little too much, why not get them something sweet from our list below: Funny tech support shirt - Here is a great idea for the holidays. Humorous coffee mug - Give them a reason to smile in the morning with a fun gift. Coffee Mug Warmer - Don't let their coffee get cold while trying to figure out your issue. In the department of thoughtful gestures, small gifts for coworkers can make a big impact. These carefully curated presents for coworkers not only show your appreciation but also reflect the unique interests and roles within your team. So, dive into the joy of gifting, and spread smiles across the office. Whether giving for the holidays, birthdays, anniversaries, or just because, you can't go wrong with one of these gifts. In closing, I want to leave you with a riddle: What is a single gift for the entire office that creates a measurable positive impact for every team? Answer: One Model One Model democratizes data to every people leader improving retention, employee experience, and internal promotions. Ask your CHRO or People Leader to check out One Model. Ask One Model to Reach Out to Your People Leader
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Featured
10 min read
Dec 06, 2023
Succession planning is a strategic HR function. Its purpose is to map out key positions in the organization and identify potential successors who are (or will be) ready to step into those key positions when they become vacant. Organizations with effective succession planning programs are more resilient. When a critical role is vacated, they already know who can step up and fill the role. Succession management also boosts employee motivation because they can see a path forward within the organization. Strategic HR activities like this go hand in hand with People Analytics. In order to effectively plan for the future, you need clarity around what you want to accomplish and whether you are improving.. Metrics help you create that clarity. How many of our plans have successors? How ready are they? What’s our bench strength? Are our successors representative of the wider talent pool? So let’s dig in and talk about that union of strategy and analytics. How do you measure your succession plan readiness, and what are the key metrics for succession planning and leadership development? Measuring Succession Planning First, here is an "oldie but a goldie" video walking through the succession planning process. Second, here are the key elements of measuring succession planning. Scope: What are the critical roles that require identified successors. Ideally, your program covers all non-entry level roles, but time is scarce so prioritize. Coverage: Given the scope above, do you have plans set up for all critical roles? Readiness: Have you evaluated each successor’s readiness for each plan they are in? Remember that one person might be a successor for multiple positions, and they might be more ready for some roles than others. Readiness can be categorized in high-level groupings. For example, “Ready Now”, “Ready in < 1 Year”, and “Ready in > 1 Year”. Bench Strength: Given completeness and individual readiness, how strong is your bench? Can you fill all critical roles? Is it still strong if you net out the successors, i.e. account for people who are selected in multiple plans. Diversity: Does your plan make full use of the available talent in the organization? Have historical tendencies caused you to overlook strong successors because they have different backgrounds and experiences from the incumbents? Will your leadership ranks become more or less diverse when your plans move into action? It took me 44 minutes and 56 seconds to pull together the metrics above to answer Question #25 from the People Analytics Challenge. Let me show you the full Succession Dashboard. Connect with us today! Key Metrics (with Definitions) Here are the key metrics you can use to address the strategic questions above. Percent of Leaders with a "Ready Now" Successor Bottom line. What does your successor coverage look like right now? Count up the number of leaders who have a successor that is ready now. Divide that count by the total number of roles in your succession planning program (see Scope above). For example, if you have 10 positions that you’ve identified as needing a successor and you have a ready now successor for 7 of those roles, then your percentage of leaders with a ready now successor is 70%. Now flip that number around and say to yourself, “Ok if one of our really key people left today, there’s a 30% chance that we’d have no one ready to take over that position.” Don’t let that be you. Use the detailed data from this calculation to create an operational list of the positions without a successor. Then work the list! Gross and Net Bench Strength The first metric tells you how ready you are to move on from one key person. Gross and Net Bench strength give you a sense of how resilient your organization would be in the face of multiple changes. Technical note: These calculations will assume that your program has set out to have 3 successors identified for each key role. Gross Bench Strength: Total successors divided by total successors needed, ignoring whether the successors are used in multiple plans. Net Bench Strength: Total successors divided by total successors needed, only counting each successor once. i.e. taking into account whether the successors are used in multiple plans. So let’s look at these calculations together. Let’s say you have 10 key roles and you have determined that you should have 3 successors for each. That means your total successors needed is 30. Now go through your plans and add up all the listed successors. Perhaps you have 26. That means you have 26 successors out of the 30 you need making a gross bench strength of 87%. Awesome. Ok. Now let’s get more nuanced. Let’s deduplicate the list of successors. Maybe there are 2 high potentials in that pool who are listed on all 10 plans. Extreme example but useful for our illustration. That means that there are really only 8 unique successors. That makes your net bench strength 8 / 30 or 27%. This difference between a gross bench strength of 87% and a net bench strength of 27% tells you that you have good immediate coverage but low resiliency. You can effectively respond to 1 or 2 people leaving, but beyond that, your bench will be depleted. Incumbent vs. Successor Diversity % Generally speaking, today’s organizations are looking to take full advantage of their available talent by ensuring that traditionally underrepresented groups are considered for advancement. A simple way to check on this progress is to compare the representation numbers of your incumbents to the representation of your successors. Let’s suppose the current pool of employees in key roles is 10% diverse while your pool of successors is 20% diverse. This is a signal that your succession planning process will contribute to greater diversity in your key positions in the future. Remember to align your successor diversity metrics with the key groupings defined by your organization’s DE&I program. These could include gender, ethnicity, or other employee attributes. Promotion Rate and Time on Bench If you make progress on the metrics above, then you’ll be leading your organization into a more resilient future. Good job! But remember, resilience is great for the organization, up to a point. Remember that the high potential employees in your plans have their own career goals. If they feel stuck on the bench, they’re likely to find their next role outside the company. If you are so resilient that you could back up all your key leaders for the next 25 years, then you are fooling yourself. Those high potential employees listed on your plans will be long gone by then. So keep an eye on the promotion rate of your internal candidates over time. (Number of promotions / average headcount). They’ll be making their own estimates as well. Alternatively, you might calculate the time on bench for your successors. When one of your successors leaves the company, check to see if they were on the bench too long. Or just ask them in your exit interview. Pay particular attention to the time on bench for your diverse successors. It’s not enough to say, “Look at how diverse our bench is!” if those candidates are continuously passed up for the next big job. Using Successor Metrics to Support People Strategies The metrics above are just a starting point. The key to strategic HR and people analytics is a willingness to ask important questions and use data to answer those questions. Ideally your succession planning process fits into a larger talent management vision that is supported by a wide range of interconnected datasets and measures. For example, you may be ready to fill key roles with external candidates. Your time to fill for similar positions will help you know if that’s a reasonable backup strategy. Alternatively, your employee pulse survey data and turnover by attrition analyses may indicate that you are having a hard time retaining diverse employees. Perhaps this will link back to the time on bench calculations discussed above. You are unlikely to find meaningful answers in a single data source, so invest in building the right underlying data architecture to connect data from succession plans, core HR, recruiting, engagement, compensation, and other workforce data. At the same time, keep the strategic focus in mind so that you’re not just doing analytics for analytics sake. Come back to the important questions like, “If we lost someone in a key role today, what’s the percent chance we’d be totally flat footed with no idea how to replace them?”
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Featured
26 min read
Dec 04, 2023
Are you looking for a way to bring joy to an HR professional close to your heart? The right gift can crack a smile on their face and make them feel special. That could even mean including a gift for their future "little people analytics superstar". Since the holiday season is coming, we know you're likely having a busy period. If you're doing a bit of gift-giving for the holidays and struggling to find out what someone on your team might like, we have you covered. So, whether you have one person to get gifts for or multiple, we know you're bound to be scratching your head. Therefore, to help you out, One Model has compiled a shortlist of thoughtful and funny HR gifts to get you started. Shop by HR Gift Categories Office Accessories Games & Activities Pet Lovers More Fun Than Serious Reading Kids' Gifts for their Mini-Me People Analytics Kids Books Business Toys Games for Future Inclusion Leaders Looking for a gift for the entire company - Check us out. Office Accessories Permission Slips , Office Citations, or Witty Women Can't Be Nice Notepads Tea and Coffee Mugs Recruiters Swear, HR can't fix crazy, Did you Document It?, Data Analyst, Relax I have a spreadsheet Tumblers and Happy Hour glasses Because I used my HR Voice Glass, There's no crying in HR, Analyst- I'm not arguing, HR hows your day wine glass, I'm a data analyst Decision Makers Logo Paperweights and Wheels or the classic Magic 8 Ball Other fun desk accessories Desk signs like "When You Excel..", custom job title plague, or Don't Make Me Use My HR Voice Candles like "Smells like this could've been an email" Or even funny HR pens and notebooks Games & Activities Charty Party - Get you're chart on! #CultureTags For the cook Recipes from around the world For the pet lover in HR It's not that they like their furry friends more than humans, it's just that they are easier to deal with. For those into Learning and Development > Dog puzzle toy For those who bring their dog to the office > Dog suit and Laptop Chew toy For those that express themselves through pets > Cat Moody or Dog Moody desk cards Coloring Books - There are so many options Snarky and Humorous for Adults #HRLife Coloring Book Data Analyst Color Book Fun Reads Save the serious books for birthday presents. Am I Overthinking This (A Great Pairing with the Decision Makers Above) You Can't Make This Sh*t Up!: Tales From the HR Crypt Surrounded by Idiots Per My Last Email... Want to couple a funny book with something more stimulating? Check out the book recommendations from Richard Rosenow's People Analytics Library. Also, check out our people analytics children's books. Gifts For Those Future People Analytics Superstars People Analytics Kids Books A is for Analytics The Great Graph Contest Machine Learning for Kids: A Project-Based Introduction to Artificial Intelligence I Am Human: A Book of Empathy Bayesian Probability for Babies HR Business Toys for Kids Play Office Worker with Complete Pretend Workstation Build an Office Doll House Office Accessories & Spaces Office Dolls Fun Games for Future DE&I Leaders Around The World Trivia Friends & Neighbors Helping Game Stone Soup Board Game Hopefully, this gives you some ideas and helps your holiday shopping season go a little bit smoother. Everyone loves a laugh during the holidays whether it be HR gag gifts or other cheeky gifts for HR professionals. One Model is committed to helping every individual at an organization be successful by empowering them with the people insights they need to make the best decisions for that organization. Want to get the entire company a gift? Check out what One Model can offer:
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Featured
18 min read
Nov 15, 2023
Human resources (HR) departments play a crucial role in shaping a company's success by managing its most valuable asset — its workforce. But traditional HR practices often rely on gut feelings, intuitions, and subjective observations, which can result in bias and poor decision-making. People Analytics, also known as HR analytics, offers a data-driven approach to understanding and optimising the workforce's performance, productivity, and engagement. What Is People Analytics? People Analytics is essentially the process of collecting, analyzing, and interpreting workforce data to gain insights into HR practices' effectiveness and improve decision-making. What does People Analytics involve? It involves using various data sources, such as employee surveys, performance metrics, turnover rates, and other HR-related data, to measure and analyze different HR aspects. HR professionals can leverage this data to identify patterns, trends, and relationships that are otherwise invisible, enabling them to make informed decisions that positively impact the workforce and the organisation's bottom line. While the terms "analytics," "reports," and "business intelligence" are sometimes used interchangeably, they are not synonymous. Analytics involves the systematic analysis of data to uncover meaningful patterns and insights, whereas reports refer to structured presentations of data in a summarised format. On the other hand, business intelligence encompasses a broader scope, including the collection, analysis, and interpretation of data to support strategic decision-making. So, what is People Analytics? The People Analytics definition goes beyond general analytics, reports, and business intelligence by focusing specifically on the analysis of HR-related data and the extraction of insights pertaining to the workforce. Unlike generic analytics, People Analytics centers around human-centric data, such as employee demographics, performance metrics, and engagement surveys. It delves deep into the behavioral aspects of work, uncovering correlations and patterns that provide valuable insights into talent management, employee engagement, and workforce planning. With People Analytics, you can gain a more holistic understanding of your workforce and make data-driven decisions tailored to HR needs. What Does People Analytics Involve? Data Collection What does People Analytics involve? People Analytics involves the seamless collection of relevant HR-related data from a multitude of sources, including HR systems, employee surveys, performance evaluations, and other pertinent data repositories. Other data sources include employee benefits, employee turnover rates, and workforce demographics. The goal is to collect as much data as possible from various sources for a more comprehensive and accurate view of the workforce. Data Cleansing The data collected from various sources often contain errors, inconsistencies, and missing data that can lead to flawed insights and ill-informed decisions if not addressed. Once the data is collected, People Analytics software will clean, validate, and transform it into a format suitable for analysis. This process can also include consolidating data from multiple sources, standardizing data formats, and filling in missing data. Data Analysis In this step, data is analyzed using statistical methods, machine learning algorithms, and data visualization tools. This enables HR professionals to identify patterns, trends, and relationships that are critical to understanding the organization's workforce and answer important HR-related questions, such as: What factors contribute to employee turnover? What skills and attributes are required for high-performing teams? What training and development programs are most effective in improving employee performance and productivity? Through this analysis, HR professionals can identify potential issues and opportunities, allowing them to take proactive measures to address them. They can also explore different scenarios and test hypotheses to make more informed decisions about the workforce. Data Visualization Data visualization tools are crucial in this process, allowing HR professionals to communicate insights to stakeholders effectively. These tools can take many forms, from simple charts and graphs to complex dashboards that display a wide range of data. HR professionals can use visualization tools to identify patterns and trends in the data, spot anomalies, and explore correlations between different variables. They can also use them to compare data across various departments, locations, or time periods, tell compelling stories, and generate persuasive reports. Effective data visualization can be an essential factor in the success of People Analytics initiatives because it makes data digestible — enabling stakeholders to grasp complex concepts and insights quickly and easily. Get the Ultimate Download Whether you're new to People Analytics or ready to enhance your existing program, this eBook covers everything you need to know about establishing a strong foundation for a successful People Analytics function that leads to smarter HR strategy and meaningful change across your organization. Four Key People Analytics Trends 1. Revolutionizing the Role and Function of HR People Analytics is reshaping the HR function from being primarily administrative and operational to becoming a strategic partner in driving business outcomes. By leveraging advanced analytics tools and techniques, HR teams can extract valuable insights from data, enabling them to make data-driven decisions that align with organizational goals. This transformation empowers HR professionals to shift their focus from transactional tasks to strategic initiatives, such as talent acquisition, retention, and development. 2. Transforming HR Business Interactions People Analytics provides HR teams with the ability to deliver data-backed insights to business leaders, fostering more meaningful and impactful conversations. HR professionals can then effectively communicate the impact of HR initiatives on key business metrics, such as revenue, productivity, and profitability. This transformation strengthens the partnership between HR and other business functions, positioning HR as a valuable contributor to overall business strategy and success. 3. Elevating the HR-Employee Connection People Analytics is also driving a transformation in the HR-employee relationship. By analyzing employee data, organizations can gain deeper insights into employee sentiments, preferences, and needs. This data-driven approach enables HR teams to personalize employee experiences, tailor development programs, and create a more inclusive and engaging work environment. The result is a stronger bond between HR and employees, as HR professionals can better understand and meet the individual needs of employees, leading to higher levels of engagement and satisfaction. 4. Enhancing the Quality of People Analytics With the advancements in AI and machine learning, HR teams can unlock more sophisticated and accurate insights from complex data sets. Predictive analytics models can forecast future workforce trends, identify potential attrition risks, and even recommend personalized learning and development opportunities for employees. These enhanced insights enable HR professionals to be more proactive and strategic in their decision-making, optimising talent management strategies and improving overall organisational performance. Embracing these four trends empowers HR teams to become strategic partners in driving organisational success, creating a more data-driven, agile, and employee-centric HR function. Choosing the Right People Analytics Tool: 3 Key Metrics When selecting the right People Analytics tool for your organization, it's essential to consider three key metrics to make an informed decision. These metrics will enable you to evaluate the effectiveness, usability, and compatibility of the tools with your HR objectives. 1. Data Integration Capabilities Ensure the tool can seamlessly integrate with your existing HR systems, such as your HRIS, performance management software, and learning management system. The ability to aggregate data from various sources is vital to obtain a comprehensive view of your workforce and maximize the insights derived from the analytics tool. Look for a tool that offers flexible and efficient data integration capabilities to support your data-driven decision-making processes. 2. Analytical Capabilities Evaluate the tool’s capabilities in data analysis, statistical modeling, and predictive analytics. Consider the range of analytics techniques and algorithms it offers, as well as its ability to generate actionable insights. Robust analytics capabilities enable you to uncover patterns, trends, and correlations within your HR data, facilitating strategic workforce planning, talent management, and employee engagement initiatives. Look for a tool that aligns with your specific analytical requirements and provides advanced analytics features to address your organization's unique challenges. 3. User-Friendliness and Accessibility Consider the user interface, ease of use, and the availability of user-friendly dashboards and visualization features. The tool should empower you to navigate and extract meaningful insights from the data effortlessly. Accessibility is equally important. Ensure the tool is accessible across devices and provides secure data access to authorized users. Look for a tool that prioritizes user experience and provides intuitive interfaces to maximize adoption and utilization across your HR teams. One Comprehensive Solution for Data Integration One Model offers a comprehensive People Analytics platform that integrates data from multiple sources, making it easier for HR professionals to gain insights into their workforce. The highly customizable platform allows organizations to tailor their HR data needs to their specific requirements. One Model's platform can also save organizations significant amounts of money. For example, a leading provider of HR outsourcing solutions used One Model's platform to expand its People Analytics capabilities, resulting in an 800% savings over the cost of an internal build. Why Is People Analytics Important? Why should you invest in People Analytics? Why is People Analytics important? At the core, HR analytics means driving better, faster talent decisions at all levels of the organization. You need to invest resources in HR data to drive and accelerate this mission.The value of People Analytics should be judged by the quality of talent decisions that are being made across the organization and the ROI of those decisions on the business. With the right People Analytics tool, users can quantify and measure the ROI of People Analytics on an organization, including cost savings, employee retention, new hires, and more. Below are several core benefits of People Analytics: Improved HR Practices: People Analytics tools enable HR professionals to make informed decisions based on data rather than subjective observations or intuition. HR analytics means more effective HR practices that are aligned with the organization's goals and objectives. Better Workforce Management: By analyzing workforce data, HR professionals can identify skills gaps, training needs, and performance issues, allowing them to take corrective actions to improve workforce management. Increased Employee Engagement: HR analytics can help identify factors contributing to employee engagement, such as job satisfaction, work-life balance, and career growth opportunities. By addressing these factors, organizations can improve employee engagement and reduce turnover. Higher Return on Investment: By optimizing HR practices and improving workforce management, HR analytics can help organizations achieve a higher return on investment (ROI) and improve their bottom line. The Role of AI in HR According to Bersin’s research, a mere 2% of HR organizations actively utilize People Analytics. This presents a significant advantage for innovative businesses looking to tap into this field and leverage its potential. People Analytics profoundly impacts how HR functions by transforming recruitment, performance measurement, compensation planning, growth mapping, learning, and retention management. In fact, studies by Deloitte indicate that People Analytics is rapidly becoming the new currency of HR, providing benefits such as increasing job offer acceptance rates, reducing HR help tickets, and optimizing compensation. As the new currency, People Analytics brings a wealth of benefits to HR professionals, enabling them to enhance key aspects of their work. HR analytics is evolving from a one-time initiative to becoming a real-time, easily adaptable tactic that offers immense benefits for HR as processes scale with business needs. HR analytics means HR teams can make data-driven decisions that result in more successful recruitment outcomes, streamlined HR processes, and better alignment of compensation practices with employee performance and market trends. This shift towards people analytics as the new currency signifies its increasing importance and its pivotal role in shaping the future of HR practices. At the core of People Analytics is artificial intelligence (AI). AI allows HR professionals to analyze vast amounts of data quickly and accurately. AI-powered HR analytics can even inform candidate screening, performance evaluation, and workforce planning, freeing HR professionals' time to focus on higher-value activities. AI can also provide predictive insights, allowing them to anticipate workforce trends and take proactive measures to address them. How People Analytics Has Evolved People Analytics has evolved significantly over the past few years, thanks to advances in technology and data science. Although managing humans may be the most complex aspect of work, other humans have been the primary means of interpreting and managing them thus far. But this is gradually changing, with computers beginning to provide more nuanced and targeted support for managing humans. People Analytics is now becoming an expected way to enhance HR teams' decision-making, with more and more teams relying on this function daily. Initially, HR analytics primarily focused on HR reporting and compliance, such as tracking headcount, turnover, and diversity metrics. But as technology and data science advanced, it’s now more sophisticated, enabling HR professionals to gain deeper insights into the workforce's performance, productivity, and engagement. As a result, organizations that embrace HR analytics are gaining a competitive edge by making data-driven decisions that positively impact their bottom line. As more HR leaders become aware of the advantages of People Analytics and these teams learn to integrate it into their function, they will recognize its benefits and embrace it as an essential part of their work. The Stages of People Analytics Maturity To truly understand the question, “What is People Analytics?” you also need to know that People Analytics is a journey, and organizations can be at different stages of maturity. The spectrum of People Analytics maturity consists of four stages: Descriptive Analytics: Organizations at this stage use basic HR metrics to describe what has happened in the past, such as headcount, turnover, and time to fill vacancies. Diagnostic Analytics: At this stage, organizations use data to diagnose the reasons behind HR-related issues, such as high turnover or low productivity. Diagnostic analytics involves identifying patterns and relationships in data to understand the root causes of problems. Predictive Analytics: Organizations at this stage use data and AI to predict future HR trends and outcomes, such as workforce demand and supply, turnover, and performance. Predictive analytics enables organizations to take proactive measures to address potential issues before they occur. Prescriptive Analytics: Organizations at this stage use data and AI to prescribe specific actions to improve HR outcomes. Prescriptive analytics involves recommending specific HR interventions to achieve specific goals and objectives, such as training and development programs or employee engagement initiatives. Leveraging the Latest People Analytics Solutions The latest People Analytics solutions enable organizations to delve deeper into the behavioral aspects of work, better understand the cause-effect relationship between various human and non-human aspects at work, and make data-driven decisions. There are three key points to make the most of a People Analytics solution: Identify and quantify the relevant data to be analyzed. Stay updated on the latest industry trends. Create clear end goals when implementing these solutions. Additionally, HR professionals must continually update and upskill their knowledge and capabilities to ensure that the organisation can optimise the latest people analytics offers and effectively leverage the latest trends for a more productive and satisfied workforce. Why One Model Beats The Competition One Model stands out as the best-in-class People Analytics solution on the market. With its comprehensive platform, organizations gain access to a robust suite of tools and features designed to streamline data collection, analysis, and visualization. The customizable nature of One Model empowers HR teams to tailor their analytics needs to fit their unique requirements, enabling them to extract actionable insights that drive strategic decision-making. Unlock the full potential of your HR analytics capabilities with One Model. Book a demo today to discover how One Model can revolutionize your People Analytics journey, helping you uncover valuable workforce insights and propel your organization towards greater success. Request Your Time to Meet with Us.
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Featured
5 min read
Nov 02, 2023
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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Featured
11 min read
Oct 24, 2023
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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Featured
6 min read
Oct 19, 2023
When examining the workforce dynamics of an organization, it's common to fixate on revenue-generating roles. After all, these positions are directly responsible for bringing in profits. However, focusing solely on revenue-centric roles leaves out a significant chunk of the workforce: the non-revenue employees. The Role of Non-Revenue Employees While non-revenue employees might not directly contribute to the financial bottom line, their contributions are foundational to the organization's success. They constitute the vast business “machinery” that powers the organization, supports revenue-generating roles, and ensures smooth business operations. In fact, they can represent more of your workforce. These include roles in HR, IT, administration, and many other indirect revenue employees who maintain the infrastructure of a business. Non-revenue units keep the operations of a business running. Imagine a product-based company without a logistics team to ensure timely deliveries or a multinational enterprise without HR personnel to manage its vast workforce. The value of non-revenue-producing departments becomes clear when you consider the chaos that would ensue in their absence. Non-revenue employees often introduce efficiency, stability, and scalability into an organization. They identify bottlenecks, streamline processes, and ensure that the revenue-generating departments can operate at peak productivity. Indirect revenue employees may not directly contribute to sales, but they directly influence revenue by performing at a high level of customer satisfaction, meeting or exceeding CSAT goals, reducing churn and creating referenceable champion customers. It took me 10 minutes and 15 seconds to create this breakout. Want to see me do it live? Fill out the form, and let’s connect our teams. The Value of Non-Revenue Units in People Analytics While non-revenue-generating (NRG) roles may not directly influence the new sales revenue stream, they are foundational to an organization's long-term success. Here's why: Holistic Workforce Analysis: An organization only gets a skewed view of its workforce by concentrating on revenue-producing roles. People analytics should consider every layer and department to ensure a balanced strategy for talent acquisition, retention, and development. Reducing Churn in Non-Revenue Departments: Turnover in non-revenue producing departments can be just as detrimental as in sales or business development. For instance, frequent changes in the support and client services roles leads to a loss of inherent knowledge, long ramp up times and loss of confidence with customers reflecting in low CSAT scores, while turnover in HR can impact talent management strategies. Organizations can reduce churn, stabilize operations, and indirectly boost revenue by applying people analytics to these non-revenue units. Identifying Opportunities for Upgrading Skills: As businesses evolve, the roles of non-revenue employees change. People analytics can help identify the need for new skills or training in these non-revenue units, find employees with the skills already and utilize those people, ensuring they continue to support the company effectively and saving money in the long term (training and recruitment costs). The dilemma often faced revolves around headcount — is it worth investing in these indirect revenue employees? The perceived short-term pain of increasing payroll for NRG employees often becomes a deterrent. As leaders, it's tempting to don many hats, especially with constrained budgets. But in doing so, are leaders truly optimizing their own roles? An organization's head, tasked with vision, direction, and often direct revenue-generation through donations, can get tangled in the intricacies of non-revenue units, thereby diluting their effectiveness. The Opportunity Cost with Non-Revenue Departments Convincing a board to hire for NRG roles, especially in medium or smaller organizations, is not straightforward. How you frame the argument is key. One approach is the opportunity cost perspective. By calculating an executive director's (ED) hourly pay and then juxtaposing that against time spent on non-revenue-producing department tasks, organizations can discern the real costs. For instance, if an ED earning $70,000 annually spends 10 hours weekly on tasks better suited for an NRG role, that's an annual cost of $17,498. If reallocating those 10 hours could generate more than this amount, it’s a stronger case for hiring specialized staff. While it's not always as black and white, this method provides tangible metrics, bridging the gap between HR and finance in understanding the worth of non-revenue employees. Ultimately, the emphasis should be on the organization's health and growth. While NRG roles might not bring in direct revenue, their contribution allows revenue-generating sectors to flourish. The Future of Non-Revenue Employees in Business Strategy The line between revenue-generating roles and non-revenue employees is blurring. As businesses increasingly adopt interdisciplinary strategies, the contributions of non-revenue units become more intertwined with revenue outcomes. For example, an effective marketing campaign (often considered a cost center) can significantly boost sales, making it an indirect revenue employee function. The bottom line? While the spotlight often shines brightest on revenue-generating roles, the silent machinery of non-revenue employees is what keeps a business thriving. It's time we acknowledge the importance of non-revenue producing departments and give them the attention they deserve in our people analytics endeavors. Want to see if your people analytics team can answer the top questions asked of HR as fast as us? Download the people analytics challenge!
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Featured
6 min read
Oct 17, 2023
The One Model team just returned from Las Vegas after an exciting HR Tech 2023. We launched some new products, evangelized the basics of connecting data from around the organization, and partied with our new partner Lightcast. Watch our team recap and see some fun images from our time at the show or scroll down to read our takeaways. Great Minds in HR Tech Shared a Ton of Insights HR Tech 2023 offered something for everyone, across many roles. It was great to see so many HR analytics and people analytics evangelists and enthusiasts. It's an inclusive community that offered incredible diversity of thought and experience. My colleague Richard Rosenow noted that an evolution of the field was actively taking place at HR Tech, specifically toward model governance. I personally told dozens of visitors to our booth that sooner or later, regulators and auditors are going to be asking questions about how a decision was made. Leaders in that room should be prepared to show every aspect of their data-driven decision process in a trustworthy and explainable way. Our HR Tech Conversations from the Expo Hall Check out our entire event interview playlist on Youtube. We Felt the Energy of HR Tech An event in Vegas always has a high level of energy, but our feeling was that organizations are buzzing about the opportunity to build the very best workforce through productivity and well-being. Most people know that people analytics is the path to every workforce story. About 70% of the folks we spoke with said their team is interested in People Analytics and were actively looking for solutions that provide great insights. Do the Basics Right There was so much talk about skills and generative AI, but many companies haven't finished with the basics of enterprise data orchestration. Many companies still struggle organize all of their people data. The cool stuff is difficult or impossible without a data foundation of well-connected enterprise systems. Lots of Hype Around Generative AI It seemed as though most software vendors were discussing their own generative AI technologies. I felt some enthusiasm from would-be technology buyers, but most are rightfully concerned about transparency and accountability as AI regulations become more likely. Most vendors have a very common large language model implemented, but we've seen analysis that shows generative AI isn't a reliable interpreter of quantitative data. In that study, only 70% of the answers that the AI generated were correct. So Many Opportunities for Fun One Model really allows our customers to get out of the late-night data crunching and come out and have a good time. Several companies were so excited about the prospect of having a scalable people analytics solution that they even joined us and our partners at a special VIP event. So, if late-night data crunching is your current reality, it's time to explore the capabilities of One Model. Continue your People Analytics journey with One Model. Schedule a demo!
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Featured
10 min read
Sep 12, 2023
John Sumser, one of the most insightful industry analysts in HR, recently wrote an article providing guidance on the selection of machine learning/AI tools. That article is found HERE, and can serve as a rubric for reviewing AI and predictive analysis tools for use in your people analytics practice or HR operations. Much of our work day is filled with conversations regarding the One Model tool and how it fits into an organization's People Analytics initiative. This is often the first practical exposure a customer contact has using Artificial Intelligence (AI), so a significant amount of time is invested in explaining AI and the dangers of misusing it. Good Questions to Ask About Artificial Intelligence Solutions - And Our Answers! Our product, One AI, delivers a suite of easy-to-use predictive pipelines and data extensions, allowing organizations to build, understand, and predict workforce behaviors. Artificial Intelligence in its simplest form is about automating a decision process. We class our predictive modeling engine as AI because it is built to automate the decisions usually made by a human data scientist in building and testing predictive models. In essence, we’ve built our own automated machine learning toolkit that rapidly discovers, builds, and tests many hundreds of potential data features, predictive models, and parameter tuning to ultimately select the best fit for the business objective at hand. Unlike other predictive applications in the market, One AI provides full transparency and configurability, which implicitly encompasses peer review. Every predictive output is not only peered reviewable within a given moment of time but also for all time. This post will follow a Q&A style as we comment on each of John’s 12 critical questions to ask an artificial intelligence company. 1) Tell me about the data used to train the algorithms and models. Ideally, all data available to One Model is used for feeding the machine learning engine - the more the better. You cannot overload One AI because it is going to wade through everything you throw at it and decide which data points are relevant, and how much history it should use, and then select, clean, and position that data as part of its process. This means we should feed every single system we have available into the engine from the HRIS, ATS, Survey, Payroll, Absence, Talent Management - everything and the kitchen sink as long as we’re ethically okay with its potential use. This is not a one size fits all algorithm; each model is unique to the customer, their data set, and their target problem. The content of training data can also be user-defined. Users define what type of data is brought into the modeling process, choosing which variables, filters, or cuts will be offered. At any time if users want to specify how individual fields will be treated, they have the ability to do so with the same types of levers as you would have in creating your own model externally. 2) How long will it take for the system to be trained? The scope of data and the machine learning pipeline determine training time. The capacity to create models is intrinsically available in One AI and training can take anywhere from 5 minutes to 20+ hours. For example, we automatically schedule re-training a turnover prediction model for a 15k employee-customer in the space of 45 minutes. 3) Can we make changes to our historical data? Yes, data can be set to be held static or use fresh data every time the model is trained. One AI acts as a data science orchestration toolkit that automates the data refresh, training, build and ongoing maintenance of the model. Models are typically scheduled to potentially refresh on a regular basis e.g. monthly. With every run extensive reports are created, time-stamped, and logged so users can always return to summary reports of what the data looked like, the decisions made, and the performance of the model at any given time. 4) What happens when you turn it off? How much notice will we receive if you turn it off? One AI models and pipelines are completely persisted. They can be turned on and off with no loss of data or logic. We are a data science orchestration toolset for building and managing predictive models at scale. Is AI being offered in a solution for your HR Team? Download our latest whitepaper to get the questions you should ask in the next sales pitch when someone is trying to sell you technology with AI. 5) Do we own what the machine learned from us? How do we take those data with us? Yes, customers own the results from their predictive models, and those results are easily downloaded. Results and models are based upon your organizations data. One Model customers only see their own results, and these results are not combined with other data for any purpose. All the decisions that the machine made to select a model are shown and could be used to recreate the model externally as well. 6) What is the total cost of ownership? Predictive modeling, along with all features of our One AI product, are inclusive within the One Model suite subscription fee. 7) How do we tell when the models and algorithms are “drifting”? Each predictive model is generated and its results are fully transparent. Once a One AI run is finished, two reports are generated for review: Results Summary – This report details the model selected and its performance. Exploratory Data Analysis – This report details the state of the data that the model was trained on so users can determine if the present-state data has changed drastically. Models are typically scheduled to be re-trained every month with any new data received. The new models can be compared to the previous model using the output reports generated. It is expected that models will degrade over time and they should be replaced regularly with better performing models incorporating recent data. This is a huge burden on a human team, hence the need for data science orchestration automating the manual process and taking data science delivery to scale. 8) What sort of training comes with the service? One Model’s customers are trained on all aspects of our People Analytics tool. Training is offered for non-Data Scientists to be able to interpret the Results Summary and Exploratory Data Analysis reports so they can feel comfortable deploying models. A named One Model Customer Service Manager is available to aid and provide guidance if needed. 9) What do we do when circumstances change? One AI is built with change in mind. If the data changes in a way that breaks the model or the model drifts enough that a retrain is necessary, users can restart the automated machine learning pipelines to bring in new data and create a new pipeline. The new model can be compared to the previous model. One AI also allows work to occur on a draft version of a model while the active model is being run in production. 10) How do we monitor system performance? The Results Summary and Exploratory Data Analysis charts provide extensive model performance and diagnostic data. Actual real-world results can be used to assess the performance of the model by overlaying predictions with outcomes within the One Model application. This is also typically how results are distributed to users through the main analytics visualization toolsets. When comparing actual results against predictions, One Model cautions users to be aware of underlying data changes or company behaviors skewing results. For example, an attrition model may identify risk due to an employee being under-trained. If that employee is then trained and chooses to remain with the organization, then the model may have been correct but because the training data changed results can’t really be compared. In the case of this employee their risk score today would be lower than their risk score from several months ago prior to training. The action to provide additional training may indeed have been a response from the organization to address the attrition risk, and actions like these that are specifically made to address risk must also be captured to inform the model if mitigation actions have taken place. The Results Summary and Exploratory Data Analysis reports typically build enough trust in cross-validation that system performance questions are not an issue. 11) What are your views on product liability? One AI provides tooling to create models along with the reports for model explanation and interpretation of results. All models and results are based exclusively on a customer’s own data. The customer must review the model’s results and choose to deploy and how they use those results within the organization. We provide transparency into our modeling and explanations to provide confidence and knowledge of what the machine is doing and not just trusting a black box algorithm is working (or not). This is different from other vendors who may deliver inflexible canned models that were trained on data other than the customers or are inflexible to use a unique customer data set relevant to the problem. I would be skeptical of any algorithm that cannot be explained or its performance tracked over time. 12) Get an inventory of every process in your system that uses machine intelligence. Each One Model customer decides how specific models will be run for them, and how to apply One AI. These predictive models typically include attrition risk, time to fill, promotability, and headcount forecast. Customers own every model and the result generated within their One Model tool. One AI empowers our customers to combine the appropriate science with a strong awareness of their business needs. Our most productive One AI users utilize the tool by asking it critical business questions, understanding all relative data ethics, and providing appropriate guidance to their organization. If you would like to learn more about One AI, and how it can address your specific people analytics needs, schedule some time with a team member below.
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Featured
6 min read
Sep 01, 2023
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
Sep 01, 2023
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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Featured
10 min read
Aug 17, 2023
The One Model difference that really sets us apart is our ability to extract all your messy data and clean it into a standardized data catalog. Let's dive deeper. One Model delivers people analytics infrastructure. We accelerate every phase of your analytics roadmap. The later phases of that roadmap are pretty fun and exciting. Machine learning. Data Augmentation. Etc. Believe me, you’re going to hear a ton about that from us this year. But not today. Today we’re going to back up for a minute and pay homage to an absolutely wonderful thing about One Model: We will help you clean up your data mess. Messy Data? Don't distress. Josh Bersin used this phrasing in his talk at the People Analytics and the Future of Work conference. From my notes at PAFOW on Feb 2, 2018: You know there are huge opportunities to act like a business person in people analytics. In the talk right before Josh’s, Jonathan Ferrar reminded us that you get $13.01 back for every dollar you spend on analytics. But you have to get your house in order first. And that’s going to be hard. Our product engineering team at One Model has spent their careers figuring out how to pull data from HR systems and organizing it all into effective data models that are ready for analytics. If your team prefers, your company can spend years and massive budgets figuring all this out... Or, you can take advantage of One Model. When you sign up with One Model: 1) We take on responsibility for helping you extract all the data from your HR systems and related tools. 2) We connect and refine all that data into a standard data catalog that produces answers your team will actually trust. Learn what happened to Synk when they finally had trust. Big data cleansing starts with extracting the data from all your HR and related tools. We will extract all the data you want from all the systems you want through integrations and custom reports. It’s part of the deal. And it’s a big deal! For some perspective, check out this Workday resource document and figure out how you’ll extract your workers’ FTE allocation from it. Or if Oracle is your thing, you can go to our HRIS comparison blog and read about how much fun our founder, Chris, had figuring out how to get a suitable analytics data set out of Fusion. In fact, my coworker Josh is pulling some Oracle data as we speak and let me tell you, I’m pretty happy to be working on this post instead. Luckily for you, you don’t need to reinvent this wheel! Call us up. We’ll happily talk through the particulars of your systems and the relevant work we’ve already done. The documentation for these systems (for the most part) is out there, so it’s not that this is a bunch of classified top-secret stuff. We simply have a lot of accumulated experience getting data out of HR systems and have built proprietary processes to ensure you get the most data from your tools. In many cases, like Workday, for example, we can activate the custom integration we’ve already built and have your core data set populated in One Model. If you go down that road on your own, it’ll take you 2 - 3 days just to arrange the internal meeting to talk about how to make a plan to get all this data extracted. We spent over 10,000 development hours working on our Workday extraction process alone. And once you do get the data out, there’s still a mountain of work ahead of you. Which brings us to... The next step is refining your extracted data into a standardized data catalog. How do you define and govern the standard ways you are going to analyze your people data? Let’s take a simple example, like termination rate. The numerator part of this is actually pretty straightforward. You count up the number of terminations. Beyond that, you will want to map termination codes into voluntary and involuntary, exclude (or include) contractors, etc. Let’s just assume all this goes fine. Now what about the bottom part? You had, say 10 terminations in the given period of time, so your termination rate is... relative to what headcount? The starting headcount for that period? The ending headcount? The average headcount? How about the daily average headcount? Go with this for two reasons. 1) It’s the most accurate. You won’t unintentionally under or overstate termination rate, giving you a more accurate basis of comparison over time and the ability to correctly pro-rate values across departments. See here for details. And 2) If you are thinking of doing this in-house, it’ll be fun to tell your team that they need to work out how to deliver daily average headcounts for all the different dimensions and cuts to meet your cleaning data requirements. If you really want to, you can fight the daily average headcount battle and many others internally. But we haven’t even gotten to time modeling yet, which is so much fun it may get its own upcoming One Model Difference post. Or the unspeakable joy you will find managing organizational structure changes, see #10. On the other hand, One Model comes complete with a standard metrics catalog of over 590 metrics, along with the data processing logic and system integrations necessary to collect that data and calculate those metrics. You can create, tweak, and define your metrics any way you want to. But you do not have to start from scratch. If you think about it. This One Model difference makes all the difference. Ultimately, you simply have to clean up your messy data. We recognize that. We’ve been through it before. And we make it part of the deal. Our customers choose One Model because we're raising the standard and setting the pace for people analytics. If you are spending time gathering and maintaining data, then the yardstick for what good people analytics is going to accelerate away from you. If you want to catch up, book a demo below and we can talk. Tell us you want to meet. About One Model: One Model helps thriving companies make consistently great talent decisions at all levels of the organization. Large and rapidly-growing companies rely on our People Data Cloud™ people analytics platform because it takes all of the heavy lifting out of data extraction, cleansing, modeling, analytics, and reporting of enterprise workforce data. One Model pioneered people data orchestration, innovative visualizations, and flexible predictive models. HR and business teams trust its accurate reports and analyses. Data scientists, engineers, and people analytics professionals love the reduced technical burden. People Data Cloud is a uniquely transparent platform that drives ethical decisions and ensures the highest levels of security and privacy that human resource management demands.
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Featured
9 min read
Aug 16, 2023
As a people analytics professional, you may be tasked with explaining the business case for a people analytics solution to your coworkers and leadership. To do this, you must be prepared to address any potential objections they may have. If you're wondering how to sell your ideas up the chain of command or across departments, here are some tips to help you prepare for those conversations, navigate any obstacles, and bring the right people analytics platform into your organisation: Talking to your CFO and CIO Most HR leaders already understand the importance of people analytics. The leaders who typically create the most hurdles for investing in people analytics are CFOs and CIOs. Let’s explore each. Discussing people analytics solutions with your CFO CFOs are most concerned about budget and how the expense will impact the company’s bottom line. Suppose a budget has not been secured yet. In that case, it will be challenging to convince the CFO to invest in people analytics solutions when the company already spends a significant amount on other platforms. Therefore, you need to restructure your business case as an investment that will lower the overall people expense by giving insights into areas that deliver clear ROI such as reducing attrition, informing the talent acquisition efforts across internal and external resource, feeding a more data led workforce planning strategy and equipping people leaders with the insights they need to make their area of the business more efficient. Connecting business benefits to a return on investment is key. Discussing people analytics solutions with your CIO or CTO Generally, technology leaders of larger organisations want their tech teams to be building the company’s tech solutions. They don’t want their resources to go overlooked or underutilised. So it's understandable that CIOs or CTOs might be concerned about purchasing a product that they don't have to build themselves. They may also have reservations about the new solution’s level of security and compliance. To address these concerns, it's important to emphasise that investing in people analytics software, like One Model, doesn't mean giving up control of the data transformation process. Instead, it allows tech teams to focus on other critical tasks across the business. A few quick conversations with key members of that team will let you know which benefits to focus on when positioning to leadership. Whether you’re talking to a CFO or CIO, the best way to build an effective business case for people analytics is to understand your audience’s concerns and needs. Let’s go over strategies to make your conversations the best they can be: How to Build Your HR Analytics Business Case 1. Work on your positioning. To successfully advocate for people analytics in the workplace, practising and refining your approach is important. This means anticipating potential objections from your manager as well as your cross-functional colleagues and proactively addressing those concerns in your discussions. The best way to uncover these is to ask them some casual questions one-on-one like, “Have you heard of people analytics? What do you think about it?” As we mentioned earlier, you need to know your audience and understand what they need to hear to fully embrace your ideas. You also need to consider what they don’t need to hear. Ask yourself, what are the top three points they need to hear and structure your conversations around that key information? Need help with your positioning? We'd love to help! Connect with us today. Many HR leaders know their reputation is on the line when choosing an enterprise solution. Some go with the most popular tool, but popularity doesn’t necessarily ensure performance. 2. Use data to demonstrate potential impact The most critical aspect of creating an HR analytics business case is to demonstrate how investing in it can make an impact on business outcomes such as employee engagement and retention, customer satisfaction, and financial performance. Showcase data points that support your business case for people analytics, such as market trends or research studies conducted by reputable organisations that show the value of HR analytics solutions. For example, generally speaking, each position that turns over can cost an organisation 33% of that position’s annual salary. Do you know how to calculate the cost of turnover at your company? Is that a metric you can pull quickly? If not, it may even be worth explaining how big of a project that is as an example of why investing in a tool is worth the budget. 3. Invite your team to consider the alternatives. You always want what's best for the team and business, so it's important to encourage discussion and be open to alternative options and new ideas. Invite the team to help participate in problem-solving the company's needs by comparing people analytics with a different type of solution. This approach not only invites discussion and debate, but also ensures that everyone feels heard and valued. By working together to co-create solutions, you can achieve better outcomes and win support more easily. Plus, by involving everyone from the beginning, we can build stronger, more cohesive teams committed to the company's shared goals. Talk through the alternative options of DIY, using a pre-canned vendor, or investing in One Model. Present each option's benefits, drawbacks, and value to the team, then encourage an open discussion and friendly debate to persuade your boss and colleagues. 4. Share examples of successful implementations. Circulating case studies and success stories from other organisations is a great way to show the value that people analytics can bring to your business. Show how they overcame similar objections, used data effectively, and achieved tangible business results. 5. Gather Allies You should also identify key allies who can help champion your idea and offer support. Understanding where people will back you up can help you handle objections. Again, the best way to get this support is by having one-on-one conversations starting with open, generalised questions. Additionally, it's important to emphasise how your suggestion to adopt a people analytics solution aligns with the organisation's values and strategy and will ultimately benefit your manager and the company. By carefully considering and addressing these factors, you'll be well-positioned to gain support for your ideas. 6. Focus on benefits, not features. Paint a picture of what life would be like after successfully implementing people analytics within the business — how it could make operations more efficient, cost less time and money (through automation and AI), ensure DE&I goals are met, and improve business outcomes. This will help people visualise and rationalise how HR analytics can benefit the business and make them more likely to buy in. Establishing a framework for moving forward After holding these conversations, the process doesn't necessarily end with a simple approval or rejection. If you get the green light, congratulations are in order! It’s time for you to get to work on implementing a people analytics solution. But if your idea is rejected, don't be discouraged. Most innovators experience numerous rejections before eventually succeeding. Rejection is simply a part of the process, not the end of it. Regardless of the outcome, use this as an opportunity to understand the reasons behind the decision. This requires asking questions and seeking clear feedback from the decision-maker. For acceptances, this will give you points to come back to later down the road. For rejections, understanding their arguments and potential areas of concern will allow you to identify ways you can re-frame your solution differently. Understanding the thought process behind either decision will help you gain support from your ideas in the workplace on future projects. In some cases, this feedback may even prompt the decision-maker to reconsider their position. If nothing else, seeking feedback can create a shared vision and establish a framework for moving forward. If you remain open to collaborating with others and working to address the issues that led to the rejection, there is a greater likelihood that leaders and managers will eventually commit to your idea and get on board. For a great example of another company that had to sell internally and is now winning, read our Tabcorp case study.
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14 min read
Jul 28, 2023
The way a company structures its workforce is crucial to its success. Workforce structures determine how employees are organised, how work is delegated, and how communication flows throughout the organisation. Workforce structures refer to the way a company organises its employees, financial responsibilities, and the relationships among them. It provides a framework for managing and coordinating work activities. There are several types of enterprise structures and your organisation uses several of them, so let’s talk through different ones and see how you can visualise them. Types of workforce structures Most workforce structures can best be displayed as an org chart. An organisational chart, or org chart, is an essential tool for any enterprise structure as it provides a clear and concise visual representation of the hierarchy, roles, and relationships among employees. It enables employees to understand where they fit into the organisation and how their role contributes to the overall goals of the company. Functional One of the most common structures in business today, a functional organisational structure groups employees according to the functions they perform, such as marketing, accounting, or operations. This allows for specialised expertise in each function, where everyone has a defined role and clear lines of communication. Location and structural overlay It may be that only a division of the company is broken up into location-based structures. For instance, this can be common in sales or HR talent acquisition departments where you have an East, Northeast division of responsibility. Supervisor hierarchy You may have also heard of position hierarchy or supervisory hierarchy. This is a slight modification of the traditional Hierarchical model. You can see these a lot in support or in places within the organisation where approvals are needed. Hierarchy establishes the connection between a superior and the subordinates within an organisation. The supervision hierarchy report exhibits the designated supervisor, presenting their immediate reports, followed by their respective reports, and so on. It encompasses the option to include the employee number, along with the name and job title of each individual, based on the chosen level of supervisory depth. A supervisor hierarchy shows who reports to who. It refers to the structure of reporting relationships within an organisation, where supervisors are responsible for overseeing the work and performance of their subordinates. In a typical supervisor hierarchy, each supervisor has a team of employees reporting directly to them. Often for the people running those units, there is a 1:1 but that’s not always perfect. The supervisors themselves report to higher-level managers or executives, forming a chain of command. The reporting relationships follow a top-down approach — with information, instructions, and feedback flowing from higher-level supervisors to lower-level employees. This hierarchical structure ensures clear lines of authority, accountability, and efficient communication within the organisation. Cost centre structure hierarchy The cost centre structure refers to the total collection of different cost types, including both fixed and variable expenses, that constitute the overall expenditures of a business. This is where the financials are run. It’s normally wrapped up in the chart of accounts. Organisations use the cost centre structure to establish pricing and pinpoint opportunities for minimising costs. This is typically how the finance system works and who is financially accountable for the funds that they spend. This can be different from who runs the business units. Therefore, this view can often be out of alignment with the structural hierarchy. To put it simply, it’s because Finance runs the financials and HR runs the business structure. This type of view often coincides with internal company political struggles. Why? Finance likes to be in control of its space and typically doesn’t like HR veering into it. But if HR can get a cost structure into a people data view, it’s typically a good thing. For instance, this will allow the finance team to get activity- or project-based accounting, or the total cost of the project including the hard numbers and people resources to make real assessments on the ROI of various initiatives. You can only get this view when finance and people data are combined. Matrix structure While not easy to visualise, this structure is really important to get right. A Matrix workforce structure generally refers to a type of organizational setup where employees are assigned to multiple reporting lines or managers simultaneously, as opposed to a traditional hierarchical structure where each employee reports to only one manager. In a matrix workforce, individuals are part of cross-functional teams and can work on various projects simultaneously, often with different sets of colleagues and supervisors. The matrix structure is most often used in large, complex organizations that handle multiple projects simultaneously and require a high degree of collaboration across departments. It is commonly found in industries such as technology, engineering, consulting, and pharmaceuticals. Additionally, matrix structures are prevalent in multinational corporations, where teams need to coordinate and work across different geographical regions. How does One Model help? As you can see, getting different views of the various structures within your business can have profound impacts on your understanding. One Model creates alignment for customers, so they can pivot between those different views with the included people insights. This is really important so you can create a mapping between your financial structures and people structures to become the translator that brings that world together within the organisation. Senior leaders typically want to see where all the money is being spent and where the people are so they can make informed decisions. So views that bring this data together make One Model incredibly valuable to our current customers. Our products empower you to change the view with a click of a button, so you get a complete view of what is actually going on. You can also cross-tabulate those views and link them together. Want to see One Model in action? Watch this quick demo video. How security plays into analysing workforce structures A basic organisational breakout may not be too concerning, but once you start applying analytics to your charts to get a better understanding of how key insights or talents are distributed throughout your organisation, you run into issues. That’s why having a tool like One Model with strong roles-based security that locks sensitive information to specific roles allows you to create a public view that instantly keeps your data safe. Security plays a crucial role in analysing workforce structures by focusing on access controls, user authentication, data protection, security awareness, incident response, vendor and third-party risk, and compliance with regulations. By incorporating security considerations into workforce analysis, organisations can identify vulnerabilities, mitigate risks, and establish a robust security foundation for their operations. Explore the evolution of workforce models Want to learn more about the evolution of workforce planning models over the past four decades and the key role that enterprise segmentation plays in achieving great analytics? Watch our webinar with Peter Howes, a thought leader and pioneer in the field of analytics and strategic planning models. He discusses how these structures have changed to a more strategic approach that’s focused on meeting the needs of the business. 7 benefits of incorporating people analytics into your workforce structures Incorporating people analytics into various workforce structures can provide organisations with valuable insights and significant benefits. People analytics, also known as HR analytics or workforce analytics, involves gathering and analysing data about employees to make informed decisions and improve organisational performance. Here are seven ways incorporating people analytics can positively impact workforce structures: 1. Data-Driven Decision-Making: People analytics paired with workforce structure views allows organisations to base their decisions on objective data rather than relying solely on intuition or anecdotal evidence. By overlaying workforce data on top of various structures, organisations can gain insights into critical aspects such as employee performance, engagement, turnover, and productivity to quickly see where trouble resides in the business. These data-driven insights enable more informed decision-making in areas like talent acquisition, talent development, succession planning, and performance management. 2. Talent Acquisition and Retention: People analytics inserted into your workforce structure views can highlight where your most loyal and high-performing employees exist. Seeing this allows you to identify the most effective recruitment channels, evaluate candidate profiles, and predict the likelihood of candidate success — so your team can build impactful strategies. By analysing data on employee turnover and retention, organisations can better visualise the factors influencing attrition rates and develop targeted retention strategies. It can also facilitate the identification of high-potential employees for succession planning and talent development initiatives. 3. Performance Management: Incorporating people analytics into an enterprise structure allows organisations to evaluate employee performance objectively and uncover great leaders and employees. By analysing performance data, organisations can identify top performers, evaluate goal attainment, and provide targeted feedback and development opportunities. People analytics can also help uncover performance patterns and trends, enabling managers to make data-driven decisions regarding promotions, rewards, and recognition. 4. Employee Engagement and Satisfaction: Organisational structures paired with people analytics provides a map of employee engagement levels, job satisfaction, and factors that impact overall employee experience. This will quickly allow you to understand the health of various teams within your business. By analysing data from employee surveys, feedback platforms, and other sources, organisations can identify areas for improvement and take proactive measures to enhance employee engagement and satisfaction. 5. Workforce Planning and Optimisation: Workforce hierarchy paired with people analytics plays a vital role in strategic workforce planning and optimisation. By analysing workforce data, organisations can assess their workforce's current and future needs, identify employee gaps, and develop strategies for workforce development and succession planning. People analytics can also help optimise workforce structures by identifying areas of organisational inefficiency or redundancy, enabling resource allocation and restructuring initiatives. 6. Diversity and Inclusion: Where do your DE&I community members reside in your org? Which areas of the business are most diverse? Incorporating people analytics into your workforce structure can support diversity and inclusion efforts by analysing demographic data. This allows organisations to assess representation, identify potential biases, and implement targeted diversity and inclusion initiatives. 7. Predictive Analytics and Future Insights: People analytics enables organisations to leverage predictive analytics to forecast future trends and outcomes related to the workforce. By analysing historical data, organisations can identify patterns and make predictions about attrition rates, talent shortages, skill requirements, and workforce needs. These insights allow proactive planning and decision-making, ensuring the organisation is prepared for future workforce challenges. In summary, workforce structures already exist in your organisation, the question is can you use them to better understand your business and create efficiencies? If you can’t, or if the process is a major project for your HR team, then you need to consider people analytics software like One Model that empowers you to transform how your leaders make decisions. We’d love to show you how One Model can help your organisation make better talent decisions. Request a demo today!
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5 min read
Jul 27, 2023
The performance management process can be a source of frustration and wasted time for many businesses. A 2014 Deloitte University study found that 58% of companies surveyed believed their performance reviews were a waste of time. This sentiment has not improved in the years since. In response, Deloitte and other major companies, including Accenture, Adobe, GE, Goldman Sachs, IBM, Microsoft, and SAP, had at the time abandoned traditional annual performance reviews in favor of more effective approaches. At the time, data from Towers Watson shows that 14% of companies have already eliminated performance ratings, with an additional 24% considering doing the same. These are large, enterprise companies making the switch to new performance management methods. These are well-established businesses that carefully consider and test new programs before fully implementing them. These companies discovered that alternative methods can provide more valuable insights into employee performance and that the resources and time dedicated to performance reviews can be better allocated elsewhere. While there are many arguments for abandoning traditional performance reviews, the increased velocity of employee performance metrics for employees is a key factor to consider. By collecting and analyzing data in a more timely manner, businesses can make faster and more informed decisions about their employees to drive the performance of the company. What is data velocity? Data velocity is one of the 3Vs of Big Data. The other two are X and Y. The concept of data velocity refers to the speed at which data is collected and analyzed. Traditionally, HR data has been collected at a relatively slow pace, with annual performance reviews being a common example. But this slow process means that data collected can quickly become incomplete, outdated, and subject to biases (recency), making it less useful for informed decision-making. For performance management, higher velocity data would mean multiple data points throughout the year instead of one annual performance review. But to increase the velocity of HR data, companies may need to adopt new technologies and approaches that enable more frequent and efficient data collection and analysis. Traditional systems that handled annual performance reviews may not make the transition to a higher velocity approach. This might include the use of specialized systems for check-ins and pulse surveys or working with HR tech startups that specialize in real-time performance management. If you look closely at any of the companies that have dropped annual performance reviews, they aren't actually eliminating performance management or even the review process. Instead, they’ve adopted technologies that enable them to switch to a high-velocity alternative. Accelerating performance management to the next level Think about your FitBit or smartwatch if you have one. If it only told you once a year how many steps you’d taken, it wouldn't give you much insight into how to change your habits. The critical piece of that technology is the velocity of the data collection. That enables you to know when you've been sitting too long reading HR analytics articles and that you should get up and take a walk. When you can collect higher velocity data, the time gaps between data points shrink, which then lets a learning algorithm better understand the data. When an algorithm can make sense of your data across time, that's when you can start to make predictions or better segment the employee population. The use of higher velocity data in HR can greatly improve the accuracy and effectiveness of employee performance analytics. By collecting data at a faster rate, businesses can better understand how performance metrics for employees change over time and identify trends and patterns faster throughout the year. This can lead to more informed decision-making and the ability to make predictions or segment the employee population. While these approaches may involve significant changes, they can ultimately provide more valuable insights into employee performance and drive business success. Why has the velocity of HR data lagged behind other fields? The lack of progress in increasing the velocity of HR data collection and analysis can be attributed to the challenges of changing established practices and the difficulties of collecting data from employees. Not to mention that HR departments are stretched to their limits in terms of data collection and may not have the resources, tools, or capacity to gather data at a faster pace without technological support. Without the right technology, it’s difficult to implement a high-velocity performance management system that can provide accurate, timely insights into employee performance. Traditional methods are not sufficient for statistically sound, bias-free analysis (some companies are still recommending post-it notes in a drawer to record employee achievements). In order to effectively collect and analyze work performance data in real-time, HR departments need access to digital technologies specifically designed for this purpose. This can be frustrating for HR professionals who are eager to adopt modern, data-driven approaches to performance management. But as new technologies are developed and made available, it’ll be easier for HR departments to implement high-velocity performance management systems that drive business success and improve employee performance. "As we strive to improve performance management" "In our efforts to harness the power of HR data" "As we move towards more data-driven approaches to performance management" "In our pursuit of high-velocity HR data" "As we continue to evolve our performance management strategies" As the field of HR evolves and the demand for high-velocity data increases, companies have seemingly three options: build their own technology in-house, purchase a solution from a vendor, or risk falling behind. But with One Model, companies have a fourth option: build+. They get the benefits of starting with a robust system as well as the ability to customize and make the solution fit their needs 100%. Learn more about build+ in One Model's latest whitepaper. Add image and cta for whitepaper.
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13 min read
Jul 26, 2023
At some point, every successful People Analytics team will develop a meaningful partnership with the Finance organization. Unfortunately, this partnership is usually not easily achieved and it's quite normal for initial alignment efforts to last for a couple of years (or more!). We are delighted to repost this insightful blog post authored by Nicholas Garbis on May 4, 2021. Revisiting his valuable insights will help us all foster a deeper understanding of how HR and Finance can collaborate more effectively. A new or maturing People Analytics team may fail to recognize the effort level required and not prioritize the work needed to establish this critical partnership with Finance. They do so at their own peril. The day will inevitably arrive when a great analytics product from the PA team will be dismissed by senior leaders when they see the foundational headcount numbers do not match. The PA team will be lacking in a clear explanation that is supported by the CFO and Financial Planning & Analysis (FP&A) leaders. But why is this the case? And how can HR and People Analytics teams do a better job of establishing the partnership? Analyzing the analytics conflicts between finance and HR Lack of alignment on workforce data At the heart of the issue is a lack of alignment on the most basic workforce metric: headcount. Both Finance and HR teams are often sharing headcount data with senior leaders. In many companies, the numbers are different. This creates distrust and frustration, and I will contend that, given Finance’s influence in most organizations, the HR team is on the losing end of these collisions. End result is that the organization spends time debating the figures (at a granular level) and misses the opportunity to make talent decisions that support the various company strategies (eg, growth, innovation, cultural reinvention, cost optimization). While headcount is at the foundation, there are several other areas where such disconnects arise and create similar challenges: workforce costs, contingent workers, position management, re-organizations, workforce budgets/plans, movements, etc... Solving the basic headcount alignment is the first step in setting the partnership. Source of the Disconnect: "Headcount Dialects" and "Dialectical Thinking" The disconnect in headcount figures is nearly always one of definition. Strange as it may sound, Finance and HR do not naturally count the workforce in the same way. It's as if there is a 'headcount dialect" that each needs to learn in order to communicate with the other. Therefore, if they have not spent some intentional, focused time on aligning definitions and processes, they will continue to collide with each other (and HR will fail to gain the trust needed to build an analytics/evidence-based culture around workforce decisions). The dialectical thinking challenge is for Finance and HR to recognize that the same data can be presented in (at least) two different ways and both can be simultaneously accurate. It is for the organization to determine which definition is considered "correct" for each anticipated use case (and then stick to that plan). Primary disconnection points Two primary areas of disconnect are the definition of the term “headcount” and whether a cost or organizational hierarchy is being used. Definition of “Headcount”: There are several components of this, underscoring the need for alignment when it comes to finance headcount vs HR headcount. Using Full-Time Equivalent (FTE) or Employee Count: Employees that are working less than full-time are often in the system with FTE values of 1.0 (full-time), 0.5 (half-time), and every range of fraction in between. The Employee Count, on the other hand, will count each employee as 1 (sometimes lightly referred to as a “nose count” to distinguish it from the FTE values). In some companies, interns/co-op employees are in the system with FTE value of 0, even though they are being paid. Determining Which Status Codes are to be Included: Employees are captured in the HR system as being active or inactive, on short-term or long-term leave of absence (LOA, “garden leave”), and any number of custom values that are used to align with the HR processes. In many companies, the FTE values are updated to align with the change in status. Agreeing on which status codes are counted in "headcount" is required for setting the foundation. Organization versus Cost Hierarchy: The headcount data can be rolled up (and broken down) in at least two ways: based on the organization/supervisor hierarchy structure or based on the cost center/financial hierarchy. Each has its unique value, and neither is wrong -- they are simply two representations of the same underlying data. It’s quite common that insufficient time has been spent in aligning, reconciling, and validating these hierarchies and determining which one should be used in which situations. Organization Hierarchy: This is sometimes called the “supervisory hierarchy” and represents “who reports to whom” up the chain of command to the CEO. This hierarchy is representative of how work is being managed and how the workforce is structured. Each supervisor, regardless of who is paying for their team members, is responsible for the productivity, engagement, performance, development, and usually the compensation decisions, too. Viewing headcount through the organization hierarchy will provide headcount values (indicating the number of resources) for each business unit, each central function, etc... The organization hierarchy is appropriate for understanding how work is being done, performance is being managed, the effectiveness of leaders and teams, and all other human capital management concerns. It is also useful in some cost-related analyses such as evaluation and optimization of span-of-control and organization layers. Cost Hierarchy: This is sometimes referred to as “who is paying for whom” and is rarely in perfect alignment with the organization hierarchy. There is a good reason for this, as there are situations when a position in one part of the organization (eg, research & development) is being funded by another (eg, a product or region business unit). In these cases, one leader is paying for the work and the work is being managed by a supervisor within another leader's organization. I have seen "cross-billing" situations going as high as 20% of a given organization. When headcount is shown in a cost hierarchy, it indicates what will hit the general ledger and the financial reporting of the business units. It has a valid and proper place, but it is mostly about accounting, budgeting, and financial planning. Which business unit is right? The truth is that as long as you have all the workforce data accurately captured in the system, everything is right. This sounds trite, but it puts emphasis on the task at hand which is to determine a shared understanding and establish rules for what will be counted and how, which situations will use which variations, and what agreed-upon labeling will be in place for charts/tables shared with others. Some organizations that have a culture of compliance and governance could set this up as part of an HR data governance effort (where headcount and other workforce metrics would be defined, managed, and communicated). Going further, there is a need beyond the Finance and HR/People Analytics leader to socialize whatever is determined as these running rules across the Finance and HR organizations. These teams all need to be aligned. How does One Model help finance and HR collaborate? With a People Analytics solution like One Model in place, the conversations between HR and Finance can be had with much more clarity and speed. This becomes easier because, within One Model all of the workforce data is captured, data quality is managed, and all related dimensions (eg, hierarchies, employee attributes) are available for analysis. Two examples of content that is specifically designed to facilitate the Finance-HR alignment discussions are: Headcount Storyboard. Setting up a storyboard which shows headcount represented in multiple ways: FTEs versus employee counts, variations of which statuses are included/excluded, etc. This information becomes readily comparable with the metric definitions only a click away. Even better, the storyboard can be shared with the Finance and HR partners in the discussion to explore on their own after the session. One Model is the best tool for counting headcount over time. Hierarchy Storyboard. Providing views of the headcount as seen using the supervisor and cost hierarchies side-by-side will help to emphasize that both are simultaneously correct (ie, the grand total is exactly the same). This can also provide an opportunity to investigate some of the situations where the cost and organizational hierarchy are not aligned. In many cases, these situations can be understood. Still, occasionally there are errors from previous reorganizations/transfers which resulted in costing information not being updated for a given employee (or group of employees). With the data in front of the teams, the discussion can move from “Which one is right?” to “Which way should be used when we meet with leaders next time?” When you have One Model, you can bring HR and Finance together faster and more easily ... and that helps you to accelerate your people analytics journey. Need Help Talking to Finance? Let us know you'd like to chat.
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5 min read
Jul 21, 2023
Analytics is a funny discipline. On one hand, we deal with idealized models of how the world works. On the other hand, we are constantly tripped up by pesky things like the real world. One of these sneaky hard things is how best to count up people at various points in time, particularly when they are liable to move around. In other words, how do you keep track of people at a given point in time, especially when you have to derive that information from a date range? Within people analytics, you run into this problem all the time. In other areas, it isn’t as big of a deal. Outside of working hours (sometimes maybe during working hours), I run into this when I’m in the middle of a spreadsheet full of NBA players. Let's explore by looking at an easy-to-reference story from 2018. Close your eyes and imagine I’m about to create an amazing calculation when I realize that I haven’t taken player trades into consideration. George Hill, for example, starts the season in Sacramento but ends it in Cleveland. How do you handle that? Extra column? Extra row? What if he had gotten traded again? Two extra columns? Ugh! My spreadsheet is ruined! Fortunately, One Model is set up for this sort of point-in-time metric. Just tell us George Hill’s effective and end dates and the corresponding metrics will be handled automatically. Given the data below, One Model would place him in the Start of Period (SOP) Headcount for Sacramento and End of Period (EOP) Headcount for Cleveland. Along the way, we could tally up the trade events. In this scenario, Sacramento records an outbound trade of Hill and Cleveland tallies an inbound trade. The trade itself would be a cumulative metric. You could ask, “How many inbound trades did Cleveland make in February?” and add them all up. Answer-- they made about a billion of them. Putting it all together, we can say that Hill counts in Cleveland’s headcount at any point in time after Feb 7. (Over that period Cleveland accumulated 4 new players through trades.) So the good news is that this is easy to manage in One Model. Team Effective Date End Date Sacramento 2017-07-10 2018-02-07 Cleveland 2018-02-08 --- The bad news is that you might not be used to looking at data this way. Generally speaking, people are pretty comfortable with cumulative metrics (How many hires did we make in January?). They may even explore how to calculate monthly headcount and are pretty comfortable with the current point in time (How many people are in my organization). However, being able to dip into any particular point in time is new. You might not have run into many point-in-time scenarios before-- or you might have run into versions that you could work around. But, there is no hiding from them in people analytics. Your ability to count employees over time is essential. Unsure how to count people over time? Never fear. We’ve got a video below walking you through some examples. If you think this point in time stuff is pretty cool, then grab a cup of coffee and check out our previous post on the Recruiting Cholesterol graph. There we continue to take a more intense look beyond monthly and yearly headcount, and continue to dive deeper into point-in-time calculations. Also, if you looked at the data above and immediately became concerned about the fact that Hill was traded sometime during the day on the 8th of February and whether his last day in Sacramento should be listed as the 7th or the 8th-- then please refer to the One Model career page. You’ll fit right in with Jamie :) Want to read more? Check out all of our People Analytics resources. About One Model: One Model provides a data management platform and comprehensive suite of people analytics directly from various HR technology platforms to measure all aspects of the employee lifecycle. Use our out-of-the-box integrations, metrics, analytics, and dashboards, or create your own. Its newest tool, One AI, integrates cutting-edge machine learning capabilities into its current platform, equipping HR professionals with readily-accessible, unparalleled insights from their people analytics data.
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4 min read
Jul 10, 2023
Now that many of our customers have complete control and access to their data like never before, they're exploring how to tell better data stories. A fun way to explore this topic is to look at the great examples from the past. Minyard’s example shows us how to tell a story with data Let’s take this example forward to one of history's most famous data stories, Minard’s visualization of Napoleon’s 1812 march into Russia. Edward Tufte, a renowned expert in data visualization, praised Minard's visualization of Napoleon's 1812 march. In his book "The Visual Display of Quantitative Information," Tufte referred to Minard's graphic as "probably the best statistical graphic ever drawn”. Although not directly HR (other than in a dark way, a visualization of workforce attrition over time), we can still analyze how this visual fits into the framework of six effective data storytelling elements and apply those lessons to HR storytelling: Business Objective: In the context of HR, the objective was to convey a message and inspire action. Minard's visualization powerfully demonstrates the disastrous consequences of Napoleon's march, highlighting the importance of understanding the impact of decisions on people. Evidence: Minard's visualization uses data from multiple sources, such as the number of soldiers, their geographic locations, and temperature. In HR data storytelling, this would translate to gathering relevant data from various sources like employee engagement surveys, performance metrics, or attrition rates, to support the narrative. Visuals: Minard's visualization is a clear, engaging visual representation of complex data. Similarly, HR professionals should utilize data visualization tools to create visually appealing and easy-to-understand representations of workforce data. Narratives: Minard's map tells the data-informed story of the march's progression and the resulting loss of soldiers. In HR data storytelling, a compelling narrative should weave together the data and insights, making them relatable and memorable for the audience. Interactivity: While Minard's visualization is static, you could imagine leaders in the armed forces looking for cuts of this data by troop category, demographics, and nationality (pre-GDPR). Interactivity would have allowed Minard to engage quickly with the graphic to see different cuts of the data. HR professionals adapt their data stories based on the audience's questions and feedback, making the story more engaging and dynamic. Action: Minard's visualization serves as a cautionary tale, prompting leaders to consider the consequences of their decisions. In HR data storytelling, ending the story with a clear call to action can drive engagement and ensure the story leads to meaningful change within the organization. By analyzing Minard's data storytelling example in the context of the simple six-element storytelling framework, HR professionals can gain valuable insights on how to create data-informed stories that effectively communicate the human impact of organizational decisions and inspire meaningful change. Check out 8 Essential People Analytics Dashboards 1 Image Source Ready to tell better data stories with your people analytics data? Download our Data Storytelling eBook today.
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5 min read
Jul 05, 2023
HR leaders - are you ready? Starting from Wednesday, July 5th, enforcement begins for Local Law 144 and Department of Consumer and Work Protection (DCWP) Rules. These regulations specifically address the use of Automated Employment Decision Tools (AEDT) found in software used during the job application or promotion process for employers and candidates residing in New York City. Understanding the new AEDT regulation Now, let's break it down in simpler terms. The law and regulations cover AEDT, which is basically any process that uses machine learning, statistical modeling, data analytics, or artificial intelligence to provide simplified output like scores, classifications, or recommendations. These tools are meant to assist or even replace discretionary decision making by humans in the employment process. To comply with these new requirements, employers need to take a few steps. First, you'll need to figure out if any of the software your company uses for hiring or promotions falls under the category of AEDT. In other words, if it's helping with decision-making by "scoring," classifying, or recommending candidates or employees in NYC. This law is broader than similar laws in Illinois and Maryland that focus on facial-recognition software, so it covers commonly used HR software. If you do use software with AEDT, here's what you need to do: (1) make sure a bias audit has been conducted; (2) provide at least 10 days' notice to applicants or employees that AEDT will be used; (3) explain the qualifications the AEDT will consider during assessments; (4) disclose the data source and type of AEDT being used, along with your data retention policy if it hasn't been shared elsewhere; and (5) inform applicants or employees that they have the right to request an alternative means of assessment or a "reasonable accommodation" under other laws. But wait, there's more! The initial bias audit is just the beginning. Employers are also responsible for conducting an AEDT audit annually. And here's the kicker: the results of the bias audit need to be published on your website before using the AEDT. These audits have to be conducted by "Independent Auditors" who are unbiased and not financially connected to the employer or the software vendor. Read more here. Now, let's talk penalties. If you violate any of these requirements, you could face civil penalties. The first violation can result in a $375 fine, while subsequent violations could be at least $500, with a maximum of $1,500. There are separate penalties for violations of the notice and audit requirements. Keep in mind that this new regulation is in line with the EEOC's guidance issued in May 2023, which also requires bias audits, notice, and opt-out provisions. It's all part of the effort to ensure fairness in employment decisions. And it's not just happening in NYC – other jurisdictions are considering similar bills and regulations related to AI and employment decisions. Want to learn more about these regulations and others around the world impacting HR? Take our Regulations and Standards masterclass and be a leader in the space. Understanding your options I think a lot of companies not using One Model are going to have some big challenges. From our experience, most AI tools are like a black box. You cannot get a clear understanding of what is being used in the models that generate the insights you're using. One Model is the complete opposite. Our models are only built from your data. They have outputs that tell you exactly what data is being fed into the models and, in turn, are customizable. In addition, One Model customers benefit from being able to build models beyond talent acquisition including retention, diversity, and more. Would you like to see our AI in action? Meet with us today! Understanding the bigger global picture On a global scale, the EU's Artificial Intelligence Act is also making progress. The European Parliament recently adopted its official negotiating position on June 14, 2023. This Act covers the use of AI in various areas, including employment. If it goes into effect, AI used for employment purposes would likely fall under the "high-risk" category and face greater regulation. So, as HR leaders, it's important to stay informed about these evolving regulations. Make sure to review your software tools, conduct the necessary audits, and provide the required notices to applicants and employees. And keep an eye out for any developments in your local jurisdiction or at the EU level. Good luck navigating these changes!
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