Insights from GDS: Unlocking the Power of People Data Platforms

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:

1. 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.

2. 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.

3. 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:

1. The Data Foundation
   This is where data is extracted, modeled, and organized. It’s the backbone of everything, including generative AI.

2. 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.

1. 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.

2. 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.

1. 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.

2. 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!

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