Building the Business Case for People Analytics: 4 Leaders on the Journey from Credibility to Impact

“Guessing sucks. We want data.”

That was the sentiment shared by Paul Rubenstein, Visier’s Chief Customer Officer and Chief Talent Evangelist, at the 2025 People Analytics Summit.

And from the response I witnessed among attendees at the event, he’s not alone. 

Rubenstein was joined by Mark Berry (Chief People Officer, Inari Agriculture), Andrea Lebar (Global Talent and Engagement Leader, Zimmer Biomet), and Jeremy Shapiro (Workforce Analytics Leader, Merck) to discuss making the business case for people analytics investments at large enterprises.

The challenge, Rubenstein stressed, isn't demonstrating the value of analytics—most executives get that data matters. The real challenge is articulating how that value benefits the business. Here's what these leaders have learned.

The era of urgency: Moving beyond "answer factory mode"

Rubenstein frames the opportunity in terms of the business environment today’s CHROs operate in. "We live in an incredibly uncertain world, and the speed at which we have to change direction and make decisions is unprecedented," he explains. "The CHRO is under pressure to deliver business impact. One of the best things they can do is start using people data to drive outcomes, to shape lots of distributed decisions across the company by putting facts in front of people."

This creates opportunities to stop simply answering questions (operating in “answer factory mode”) and to start driving real business impact. Whether organizations are managing costs, preparing for new products, or navigating strategic shifts, people analytics offers a way to connect talent decisions to measurable, and meaningful, outcomes.

Your first investment isn't technology, it's actually curiosity. 

When Berry thinks about scaling analytics, he doesn't think about software first. "Scaling analytics is about anthropology,” he says, it’s the study of how humans react when you show them data they didn't expect. "The first investment companies need to make is not in software; it's in curiosity. You can't buy that."

At Inari Agriculture, Berry has trained his HR team to "act like detectives" when examining data. He encourages them to ask "What's the story here?" rather than simply reporting numbers. "Once curiosity takes root," he observes, "demand for analytics spreads faster than free coffee in a break room."

Barry takes this reframing a step further. The now commonly used term “people data” is, in itself, problematic, Berry believes. “People data is business data. I don't even know why we call it something else," he says. 

Instead of positioning analytics as an HR need, present it as a business capability that happens to sit within HR. Show how people analytics enables better decisions across the organization, from finance to operations to strategic planning.

Consider how you’ll emphasize this integration in your own planning. Think about how analytics will work within existing business rhythms, not as a separate system sitting alongside them. 

What you can do: When building your business case, emphasize the human side just as much as the technology. Train team members to approach data with curiosity, to create early wins that demonstrate value, and to build data literacy across business leadership. Technology enables these capabilities, but a culture of curiosity makes them stick.

Build analytics into how the business already operates

Lebar's experience at Zimmer Biomet illustrates what successful integration looks like. "We've embedded key performance indicators throughout our operating mechanisms on a monthly and quarterly basis," she explains. "Our business leaders are speaking to these metrics, not just HR."

This works because it makes analytics part of how the business operates, not a separate HR initiative. As Rubenstein notes, “People follow the data behaviors of the leader they want to be.” 

When executives bring facts to decisions, it signals to the organization: this is how we work here.

This doesn't mean reducing people to numbers. It means using data to make better human decisions: who to hire, how to develop managers, where to invest in engagement, and when to intervene on retention.

This integration plays out in everyday decisions. A regional manager notices turnover climbing in one location. Within minutes, they can drill into whether it's driven by compensation gaps, manager effectiveness, or workload issues. They take action within days instead of waiting weeks for an HR investigation.

Similarly, before quarterly planning sessions, executives can review workforce capacity analytics alongside revenue forecasts, ensuring they staff for growth without the costly lag of reactive hiring.

Lebar has also found that simplicity drives adoption. Her team standardized dashboards so that within seconds, leaders know if they're seeing good or bad news. And within a minute they understand key drivers, and from there they can drill down for more details. 

What you can do: Strip your business case down to the essentials. Pick one business decision that happens repeatedly (hiring, planning, retention), show exactly how analytics will improve it, and demonstrate the impact in terms your stakeholders already measure. Save the advanced capabilities for year two.

Lead with what matters to your stakeholders

Berry learned an important lesson early on: "I used to impose what I thought was important on the intended audience, without recognizing they might not be at that place yet."

His approach now? Start with what matters most to your stakeholders. When working with boards, he asks what's most compelling to them, then provides analytics that address those needs. "To the degree that I'm open to share with them what's important to them, they may be more open to listen."

Sometimes this means starting with basic information if that's what builds trust. You must understand your audience deeply. What decisions keep them up at night? How can people analytics enhance their decision-making?

For instance, this might mean starting with basic workforce planning data if that builds initial credibility, then expanding to predictive analytics once trust is established. The goal isn't to showcase everything analytics can do—it's to demonstrate value in terms your stakeholders understand and care about.

What executive stakeholders care about, of course, is specific, and demonstrable, bottom line impact.

What you can do: Pilot analytics integration with one business unit first. Choose a team with a receptive leader, embed metrics into one existing meeting they already attend, and document what changes. Use that story—with before/after results—as proof for your broader business case.

Connect people data to dollars that matter to executives

Vague promises about "saving money" won't persuade budget holders. You need concrete connections between people analytics and business outcomes.

Rubenstein shares examples from Visier's customer base. In sales organizations, companies combine people data, whether that’s manager stability, hiring sources, relative pay, training completion, or similar, with performance data from platforms like Salesforce and Gong. This helps them predict which hires will succeed and identify which management practices drive results.

In retail and banking, organizations can connect engagement scores, attendance, training, and safety records with same-store sales, profitability, and shrinkage. “The manager matters,” Rubenstein emphasizes. When you can see the relationship between how managers lead and business results, you can identify effective practices and understand which investments deliver sustainable outcomes.

What you can do: Pick your company's top business priority right now (growth, efficiency, retention). Find one example where people decisions directly impacted that outcome, good or bad. Quantify it, then lead your business case with that story. Real examples with real dollars are what get budgets approved.

Be prepared: Create collaborative governance before you need it

Shapiro offers a helpful framework for governance, ensuring collaboration and cross-functional involvement. His team at Merck has established data governance processes in HR operations, plus a hybrid layer for AI governance that includes HR, IT, and analytics working together.

He suggests a simple framework for go-forward decisions: “Just because we can doesn't mean we should." Consider, he suggests, who will be helped, whether you should build capabilities yourself or turn to vendors, and whether you're uniquely positioned to solve this problem.

This thoughtful, business-centered approach helps to build trust which is integral to ongoing support, Berry says. “Trust is the real operating system for analytics. You can have the best dashboards in the world. Nobody will trust them if they don't believe in them."

What you can do: Before pitching your business case, map who needs to say "yes" to your analytics initiatives—IT for data access, legal for compliance, finance for budget, business leaders for adoption. Meet with each one individually to understand their concerns and build those answers into your governance approach from day one.

The bottom line

Progress doesn't require perfection. "Take a step forward. Even if you don't feel ready," Lebar says. "Do one thing, then do the next thing. Before you know it, you're going to look back and say, 'We've come really far.'"

These four leaders started where you are—making the case, building credibility, proving value one decision at a time. They emphasized curiosity over technology, integration over isolation, real dollars over vague promises, and collaboration over gatekeeping.

Your business case doesn't need to be perfect. It needs to be clear, concrete, and connected to what your executives already care about. Start there. The rest will follow.