The push to become “data-driven” has changed how HR teams make decisions.
Today, teams have access to more workforce data than ever before—engagement scores, retention rates, performance metrics, and compensation benchmarks. But data alone rarely tells the whole story.
The real value comes from pairing those numbers with what’s happening in the business and how people are experiencing it.
In the latest episode of the People Proud podcast from HiBob, we explored how HR leaders turn people data into meaningful insight and why context makes the difference.
In the conversation, we heard from:
- Jen Ruza, Global EVP of people and experience at VaynerX
- Your hosts, Tali Sachs and Dr. Ken Matos
One idea stood out immediately: Data only becomes useful when it’s paired with the story behind it.
For HR leaders navigating everything from workforce planning to AI-driven productivity, that insight is becoming increasingly important. The real challenge today isn’t accessing people data—it’s knowing how to interpret it.
As Ken puts it, the question isn’t “Do we have a metric?” It’s “What does this metric do? What is the purpose of what you’re doing? … What question does this answer? What purpose does this serve? … What decisions am I going to make off the back of this information?”
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Why qualitative insight often comes before the data
For many HR leaders, the first signal that something is changing comes from conversations and what you hear day to day.
People partners spot trends early—questions from managers, concerns from team members, or small shifts in how teams talk about their work. Those signals often appear long before they show up in engagement scores or retention metrics.
That’s why many HR teams start with qualitative insight.
Jen describes this as a natural part of how people teams operate. Because HR constantly interacts with teams and leaders across the organization, those conversations often reveal what’s worth investigating.
“Oftentimes, the qualitative data comes first,” she explained. “We’re gathering information from conversations across the organization. Then we dive into the numbers to validate what we’re hearing.”
Once a pattern emerges, the next step is to explore the quantitative data. HR teams might look at metrics like performance trends, retention, internal mobility, or engagement scores to see whether the data confirms what they’re hearing on the ground.
Sometimes the numbers support the initial hypothesis. Other times, they tell a different story entirely.
This is where deeper insight begins. When experience and data don’t immediately align, it creates an opportunity to ask better questions and uncover what’s really happening inside the organization.
When the numbers and experience don’t match
Some of the most valuable insights in people analytics show up when the data and the day-to-day experience don’t quite align.
For example, engagement scores might dip while turnover stays stable. At first glance, the numbers may suggest everything is fine, but HR leaders know it’s rarely that simple.
That’s because context matters.
Ken shared a classic example: You can see engagement drop without turnover moving, and it doesn’t mean “nothing’s wrong.”
“A lot of people might see engagement score plummeting, but turnover not changing,” he said. It can be a case of most people in a company living in areas without a robust job market. In cases like this, data might appear to tell two different stories.
“But once that job market changes,” he emphasized, “[the data will] tell us the same story. And so, what we do now and what we do six months from now might be very different.”
Market conditions, organizational changes, leadership transitions, and team dynamics can all influence how people feel—and whether they choose to stay or leave. In some cases, people may feel disengaged but stay put because external opportunities are limited (or because they’re waiting out uncertainty).
That’s why a single metric can be misleading.
When HR teams combine these signals, they can move beyond surface-level numbers and understand what’s really driving them.
And when the data and experience don’t match up, that’s your cue to dig deeper.
Instead of treating conflicting signals as a problem, strong HR teams treat them as a prompt to ask better questions about the employee experience.
Retention alone doesn’t tell the whole story
Retention is one of the most-watched HR metrics, but on its own, it can be misleading.
It’s easy to look at high retention as a win. People are staying, teams are stable, and the culture seems healthy (that’s the goal, right?).
Yes, but the real question isn’t just whether people stay. It’s who is staying—and why.
Ken noted that retention has “flipped,” and that’s why the headline number can be misleading.
Healthy organizations expect some movement as people grow, shift roles, or pursue new opportunities. This was validated by Jen, who noted, “You may not want everybody to stay,” Jen Ruza noted.
Strong HR teams are looking beyond the headline number and asking better questions, such as:
- Are you keeping the right people?
Retention matters only if the organization keeps high-performing talent. - Are people growing or getting stuck?
Long tenure without development can signal stagnation. - What external factors are shaping retention?
A weak job market can keep people in place, even when engagement drops. - Is the workforce evolving with the business?
Metrics like internal mobility and performance add critical context.
Retention is still useful, but only when it’s part of a bigger picture. The real goal is building a workforce that continues to grow and, most importantly, deliver impact where it matters.
AI is forcing HR to rethink productivity metrics
AI is already reshaping how work gets done. But measuring that change? That’s much harder.
Across industries, teams are completing tasks dramatically faster with AI tools.
A study from OpenAI reports that people can save 40–60 minutes per day using generative AI assistants, with 75 percent reporting improved speed or quality of work.
The result: Work is getting compressed.
Tasks that once took hours can now take minutes. Research that used to take days can get done in an afternoon. And repetitive work—from summarizing documents to generating drafts—can be automated in seconds.
However, that raises a new question for organizations: What do we do with the time AI gives back?
Tali put it bluntly: Speed gains are easy to celebrate but hard to account for. “How are we tracking that repurposed time?” she asked. “We go from three hours to 30 minutes … but there’s no really solid tracking of that.”
And the truth is, most organizations are still figuring out how to track the real value of time saved by AI.
HR leaders are starting to ask new questions about productivity and performance, including:
- Where are teams actually saving time?
AI can accelerate research, writing, coding, and analysis, but the gains vary widely by role. - How are we repurposing that time?
Is it going toward deeper thinking, more projects, or simply more work? - Which team members benefit most from AI tools?
Experience level, job type, and digital fluency can all influence the results. - What new metrics do we need to track?
Traditional productivity measures often miss the distributed impact AI has across workflows.
In other words, organizations are entering a new phase of experimentation: trying to understand how AI changes work and performance across the board.
For HR teams, this means one thing: The metrics that mattered yesterday may not be the metrics that matter tomorrow.
The experiment mindset HR teams need right now
If AI is changing how work gets done, organizations can’t rely on old assumptions about productivity.
They need experimentation.
Ken argued that the only way to learn what’s real is to test it intentionally, not roll out AI and hope for the best.
“We really need to run some experiments,” he said. “Create your group of AI-focused people … Measure … everything that they do … [then] teach the rest of [your people] to make the most of that.”
Right now, many companies are rushing to deploy AI tools across teams, but without clear ways to measure what’s actually improving.
Instead of rolling tools out everywhere at once, organizations can start with small, controlled experiments.
For example:
- Compare AI-enabled workflows with traditional ones. Let one group complete tasks with AI support while another follows the existing process.
- Measure outcomes, not just speed. Faster work doesn’t always mean better work. Look at quality, accuracy, and impact.
- Test across experience levels. Senior team members may use AI differently than juniors and produce very different results.
- Track how work changes, not just how long it takes. AI may shorten some steps while creating new ones elsewhere in the workflow.
This kind of experimentation helps organizations answer critical questions:
- Where does AI genuinely improve productivity?
- Which roles benefit most?
- Where does human judgment still outperform automation?
Without that experimentation, companies risk adopting tools without understanding their real impact.
Human context still matters in an AI-driven workplace
Even as analytics and AI become more powerful, one thing hasn’t changed: Numbers alone don’t explain human behavior.
Tali pointed out a simple signal today’s organizations can learn from: “The big push from consumers is, ‘I want more human, I want more connection.’”
Data can show that engagement dropped, productivity increased, or turnover stayed flat. But it can’t always explain what’s driving those shifts or what to do next.
The gap between data and interpretation is still a challenge. According to Gartner, only 29 percent of HR leaders say their organizations effectively use people data to drive decisions.
To understand what the data really means, HR leaders need multiple layers of insight working together:
- Quantitative data. Metrics like retention, engagement, performance, and productivity trends.
- Qualitative insight. Conversations with team members, managers, candidates, and even alumni.
- Organizational context. Business strategy, market shifts, leadership changes, and workforce dynamics.
When these signals come together, the numbers start to tell a fuller story.
As Jen put it: “You can’t just look at numbers, black and white on a piece of paper, without having the right context behind it.”
In a world where AI can analyze data faster than ever, the real differentiator is the human judgment required to interpret insights.
Key takeaways: Making people data actually useful
Data is becoming central to how organizations understand their workforce. But the most effective HR teams know that metrics alone don’t drive better decisions. Context does.
Here are five principles leaders can use to turn people data into meaningful insight:
- Start with the big—and small—questions. Qualitative insight, what employees and managers are saying, often reveals issues before they appear in the data. Use those signals to guide what metrics you explore.
- Don’t rely on a single headline metric. Retention or engagement scores rarely tell the full story on their own. Combine multiple signals to understand what’s really happening across the workforce.
- Treat conflicting signals as an opportunity. When experience and data don’t match, dig deeper. Misalignment between what people feel and what the numbers show often reveals the most valuable insights.
- Experiment before redefining productivity. AI is reshaping how work gets done, but organizations are still learning how to measure its impact. Small experiments can help identify where AI creates real value.
- Use context to turn data into decisions. Metrics provide signals, but context—organizational, market, and human—explains what those signals mean. The strongest HR teams combine both to guide strategy.
