Expectation vs. Reality in People Analytics

People Analytics promises a smarter, data-driven HR function. But many companies jump in with unrealistic expectations. They want dashboards, predictions, and automation—without fixing the basics. Below are five common expectations compared to what actually happens when you start doing the work.

1. Expectation: We’ll run predictive models and use machine learning.

Reality: Most teams struggle to maintain clean descriptive analytics.

This is probably the biggest disconnect. Leaders hear terms like “AI,” “machine learning,” and “predictive attrition” and assume it’s plug-and-play. They expect the People Analytics team to hand over insights like, “These 10 employees are about to leave,” or “This profile leads to high performance.”

But the truth is: most organizations can’t even answer basic questions reliably. Like:

• How many active employees do we have?
• How do we define ‘termination’ vs. ‘resignation’?
• Which job families roll up to which departments?

Before you can build predictive models, you need consistent, trusted descriptive data—who, what, when, where. That means:

• Aligning job titles and levels
• Resolving duplicate records
• Fixing reporting lines
• Defining key metrics like attrition, tenure, and internal movement

If your dashboard changes every time someone pulls it, it’s too early for machine learning.

2. Expectation: Leaders will act on the data.

Reality: Many leaders still trust gut over graphs.

Even when you do get the data right and deliver a clean, accurate analysis, the assumption is: “Now they’ll act.” But many leaders don’t.

Here’s why:

• They don’t trust the data (past errors left a mark)
• The insight contradicts their experience
• The format is too complex
• There’s no clear call to action

This isn’t about bad leadership—it’s about habit. Many execs have led teams successfully for years using intuition. So changing behavior requires more than numbers. It takes:

• Building trust over time
• Providing context, not just metrics
• Telling stories, not just showing charts
• Offering specific, low-risk first steps

People don’t ignore data out of spite. They ignore it because it feels unfamiliar or risky.

3. Expectation: We’ll be strategic partners.

Reality: We’re still chasing one-off report requests.

People Analytics is often seen as a strategic arm of HR—helping drive talent strategy, workforce planning, and org design. But in many companies, the team ends up overwhelmed by manual requests:

• “Can you pull the turnover rate for Q1 by country?”
• “Do we have a list of all contractors in Marketing?”
• “What was the average tenure for people who left last year?”

These requests are useful, but they pile up. And they keep the team reactive, not strategic.

The root issue is often a mix of poor tooling and unclear boundaries. If basic data access isn’t self-serve, people default to emailing the People Analytics team. And if priorities aren’t aligned with leadership, time gets eaten up by low-impact tasks.

To move toward strategy, the team needs a clear intake process, reporting standards, and support from leadership to say “no” or “not now.”

4. Expectation: One tool will solve everything.

Reality: We use multiple tools—and still fall back on Excel.

A common myth is that one magical system will give a full view of the employee lifecycle. But in practice, HR data lives in:

• Workday or Bamboo for core HR
• Greenhouse or Lever for recruiting
• CultureAmp or Peakon for engagement
• ADP or Gusto for payroll
• Google Sheets or Excel for one-off tracking

Integration between these tools is messy. APIs break. Definitions conflict. And when the heat is on, people revert to what they know—Excel.

This doesn’t mean the tech stack is broken. It just means:

• You need to agree on what tool is the “source of truth” for each data point
• You need to map definitions clearly across systems
• And you need to be okay with some level of manual work at the start

Tool complexity is normal. Clarity on process is what makes it manageable.

5. Expectation: Insights will spark massive change.

Reality: Small wins build trust over time.

It’s tempting to think that one killer dashboard or one amazing deck will change how the business operates. But culture change is slow. Data adoption happens gradually.

That one chart on time-to-fill that nudges a recruiter to rethink their funnel? That’s a win.

That simple headcount tracker that helps a director plan for backfills? Another win.

Over time, small wins like these show the value of data in everyday decisions. That builds credibility. It also creates pull—leaders begin to ask, “What else can we see?”

Think of adoption as a slow burn, not a firework. Consistency matters more than flash.

Final Thought

The gap between expectation and reality in People Analytics isn’t a failure—it’s the norm. Every team starts somewhere. Usually, that means broken data, unclear ownership, and a backlog full of ad hoc report requests. And that’s okay.

The real work isn’t just about fancy tools or machine learning models—it’s about changing how decisions get made. And that kind of change takes time. It takes:

• Patience to build trust with leaders who still rely on instinct
• Discipline to document processes and define terms clearly
• Focus to keep improving the basics while nudging toward bigger questions
• Persistence to advocate for good data practices, even when no one’s asking for them

It’s also about redefining what success looks like. Not a flashy dashboard that no one uses, but real adoption:
A manager who checks a tracker every week. A recruiter who changes how they evaluate candidates. An exec who starts asking for trends before making decisions.

Those moments—small, real, and sticky—are what build a data-informed culture. So don’t chase perfection. Build momentum. One fix, one habit, one conversation at a time.

And if you're wondering how to move forward from wherever you are today, that’s exactly what my series Climbing the Staircase of People Analytics is for.

Each article walks through a step—from cleaning the mess, to building trust, to finally scaling and shaping the future. Because closing the gap isn’t about big leaps—it’s about climbing, one step at a time.