\n
I’m a fan of data. I’ve spent the better part of my career trying to help organizations move away from gut-feel decisions about their people and toward something more rigorous. So what I’m about to say isn’t an argument against people analytics.<\/p>\n
It’s an argument for using it correctly.<\/p>\n
Organizations are investing heavily in people analytics right now \u00e2\u0080\u0094 HR dashboards, engagement platforms, turnover predictors, workforce planning tools. The technology has gotten genuinely good. And yet I talk to leaders regularly who have access to more people data than ever before and feel less confident about what’s actually happening in their organizations, not more.<\/p>\n
The problem isn’t the data. It’s what we’re actually asking the data to answer.<\/p>\n
What People Analytics Is Good At<\/h2>\n
Let me start with the genuine strengths, because they’re real.<\/p>\n
People analytics is excellent at quantifying outcomes. Turnover rates, time-to-hire, absenteeism, internal mobility, pay equity, succession pipeline depth. These are things that can be measured accurately, tracked over time, and compared across teams, departments, and peers. If you want to know whether your engineering team turns over faster than your sales team, people analytics will tell you. If you want to know whether a particular manager’s team has significantly higher absenteeism than the rest of the organization, the data will surface it.<\/p>\n
It’s also good at identifying correlations that prompt useful investigation. When people analytics shows that employees who complete a particular onboarding experience have 30% better retention at the 18-month mark, that’s worth paying attention to. It’s not proof of causation, but it’s a signal worth following.<\/p>\n
And it’s good at making the case for investment. When HR wants to make the business case for a leadership development program or a culture initiative, having data that connects people practices to business outcomes is enormously useful. This is probably where people analytics has delivered its most durable value.<\/p>\n
Where People Analytics Falls Short<\/h2>\n
Here’s where I push back on the field a little.<\/p>\n
People analytics is good at measuring what happened. It’s much weaker at explaining why. And when it comes to organizational culture, the why is almost everything.<\/p>\n
Your engagement survey tells you that 40% of employees are disengaged. It does not tell you what is driving the disengagement. Is it poor management in specific teams? Unclear career paths? A leadership team that says one thing and does another? Compensation that’s out of step with the market? A culture of overwork that nobody names out loud but everyone experiences? The survey score is real. But the number, by itself, tells you almost nothing about where to intervene.<\/p>\n
Culture is fundamentally a set of shared beliefs, norms, and behaviors. Those things are embedded in stories, in how people talk about what happened at last week’s all-hands, in the unwritten rules about what gets you rewarded or punished here. You can’t fully capture that in a Likert scale. The most important things about culture often exist in the spaces between the questions you thought to ask.<\/p>\n
The Specific Mistake I See Most Often<\/h2>\n
Organizations conflate measurement with insight. They run the survey. They get the scores. They present the scores to leadership. And then \u00e2\u0080\u0094 because the data exists and looks rigorous \u00e2\u0080\u0094 they treat it as a diagnosis when it’s actually just a description.<\/p>\n
“Our culture scores are down in the innovation category” is a description. It is not a diagnosis. Treating it as one leads to interventions that address the symptom (the score) rather than the underlying dynamic.<\/p>\n
I’ve watched organizations pour resources into “innovation culture” programs \u00e2\u0080\u0094 design thinking workshops, hackathons, failure-celebration events \u00e2\u0080\u0094 while completely ignoring the performance management system that was actively penalizing people for taking risks. The analytics said innovation was low. The analytics could not see that the reward system was the problem. So nobody looked there.<\/p>\n
What to Do With the Data Instead<\/h2>\n
People analytics is most useful when it’s paired with qualitative sense-making. The numbers tell you where to look. The conversations tell you what you’re actually seeing.<\/p>\n
When engagement scores drop in a particular division, the right response isn’t to immediately launch an intervention. It’s to go find out what’s happening. Talk to the people. Talk to their managers. Understand the context. The data has pointed you toward a location \u00e2\u0080\u0094 now do the diagnostic work to understand what’s actually going on there.<\/p>\n
Similarly, when people analytics surfaces a correlation \u00e2\u0080\u0094 say, a link between a specific manager’s style and higher team performance \u00e2\u0080\u0094 treat it as a hypothesis to investigate, not a conclusion to act on. Maybe the manager is exceptional. Maybe they’re inheriting better talent. Maybe the team’s performance metrics favor their type of work. The data is telling you something interesting. It is not yet telling you what to do.<\/p>\n
The Culture Measurement Question Worth Asking<\/h2>\n
If your organization is trying to use data to understand its culture, the question I’d suggest starting with isn’t “what are our engagement scores?” It’s “what are the behaviors we actually want to see in this organization, and are we measuring whether they’re happening?”<\/p>\n
That’s a harder question to operationalize. It requires having a clear point of view about what your culture should be \u00e2\u0080\u0094 not just survey questions about whether employees feel valued or understood.<\/p>\n
Our Culture Mosaic Survey<\/a> is built around this distinction. It measures specific behaviors and conditions that drive culture, not just sentiment. The goal is to give organizations data that actually points toward action \u00e2\u0080\u0094 not just a score to report.<\/p>\nThe Honest Summary<\/h2>\n
People analytics is a genuinely useful tool when it’s used for what it’s actually good at: quantifying outcomes, surfacing patterns worth investigating, and making the case for investment in people.<\/p>\n
It is not a substitute for leadership judgment. It is not a diagnostic tool for culture. And it will not tell you what to do \u00e2\u0080\u0094 only where to look.<\/p>\n
The organizations using people analytics most effectively are the ones that haven’t outsourced their thinking to the dashboard. They use the data to ask better questions, then they go ask them.<\/p>\n<\/p><\/div>\n\n","protected":false},"excerpt":{"rendered":"
I’m a fan of data. I’ve spent the better part of my career trying to help organizations move away from gut-feel decisions about their people and toward something more rigorous. So what I’m about to say isn’t an argument against people analytics. It’s an argument for using it correctly. Organizations are investing heavily in people […]<\/p>\n","protected":false},"author":1,"featured_media":7040278,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[140987],"tags":[12765,3733,3794,20622],"dealstore":[],"offerexpiration":[],"class_list":["post-7052838","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-analytics","tag-culture","tag-people","tag-tells"],"yoast_head":"\n
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