Beyond Engagement Scores: Smarter HR Metrics

(Part 2: Metrics that Matter series)

If you work in HR or people leadership, you probably have a dashboard problem.

On paper, you’re “data-rich” — headcount trends, turnover, time-to-fill, engagement scores, training hours, performance ratings, DEI stats. The charts are colorful. The PDFs are polished. You may even share them with your Board.

But when you try to answer a simple question like,

“Is our culture helping us meet our goals and increase profits?”

…those same dashboards suddenly feel unhelpful.

In our Metrics that Matter series on the Collaborative Culture podcast, Monica and I dug into this gap, and in Part 2, we invited Nicole Eisdorfer, PhD, to help unpack why so many HR dashboards fail to tell the truth about culture and what to do about it.

This post builds on that conversation and offers a practical way to rethink your HR data so it actually helps you lead.

The real problem isn’t “bad data.” It’s mismatched purpose.

One of Nicole’s core points hit me hard:

HR data isn’t bad because it’s wrong.
It’s bad because it was built for a different purpose than what we’re trying to do with it.

Most HR systems and metrics were originally designed for compliance and transactions, not culture and strategy.

• Systems of record were built to prove you did the thing: hire, pay, terminate, file, train.
• Performance ratings were designed to allocate merit increases and manage payroll budgets, not reflect actual contribution or potential.
• Policies and templates from vendors or SHRM-style best practices were created to keep organizations safe and consistent, not to reflect your specific values and culture.

Over time, those same tools were repurposed to “measure engagement,” “track culture,” and “guide talent strategy.” But the underlying assumptions never changed. You’re trying to get deep, human insight out of tools that were never designed for that.

That’s why your dashboards often feel like a mirage: lots of numbers, very little insight.

“Your systems and processes are how you keep your promises.”

One of my favorite lines from Nicole is:

“Your systems and processes are how you keep your promises.”

If you say, “We value growth, inclusion, and well-being,” but your systems:

• Reward individual heroics over team outcomes
• Penalize PTO usage as a sign of disengagement
• Compress performance ratings into a forced curve to match a merit budget

…then your metrics will quietly teach the opposite lesson.

That’s the heart of the dashboard problem. It’s not just that the data is imperfect. It’s that what you measure and how you measure it becomes a culture signal.

So the question isn’t, “How do we get more HR data?”
The question is, “How do we make the data we already have truer?”

Treat your data as a mirror, not a measurement

Nicole offers a simple but powerful reframe:

Treat your data as a mirror, not a measurement.

Instead of assuming the number is “truth,” you treat each metric as a reflection of the assumptions, constraints, and compromises baked into your systems. From there, you start asking better questions.

Nicole’s Making HR Data Truer worksheet walks through four moves that any HR leader or CPO can use with their existing dashboards:

1. De-label
2. De-compose
3. Cross-link
4. De-assume

Let’s briefly unpack each through a culture lens.

1. De-label: When the name hides more than it reveals

Question to ask:

“Where does the label make this metric sound more objective, or more generous, than it really is?”

Some of the most “official” metrics are the most misleading because the label feels neutral and scientific, while the process behind it is highly political.

Take performance ratings:

• On paper:
  “3 – Meets expectations.”
• In reality:
  Forced curves, calibration meetings, and budget caps mean that a “3” on a high-performing team might reflect someone who is truly exceeding expectations, they just happen to sit on a team where everyone is strong.

So when that rating shows up in a dashboard, it isn’t actually a measure of performance. It’s a resource allocation decision dressed up as objectivity.

Culture implication:
If you use that rating to decide who is “high potential,” who gets stretch assignments, or who is “ready for promotion,” you’re reinforcing distorted definitions of value that have nothing to do with your stated values.

2. De-compose: Averages hide the truth

Question to ask:

“Where, when, and for whom is this happening?”

A single engagement score or turnover rate might look “fine” overall until you break it down by manager, location, role, shift, or tenure and see a 20–30 point spread.

Examples:

• Engagement looks strong overall, but newer employees on one team are clearly checked out.
• Turnover looks manageable, but it spikes in one department after every major project.
• “Time to fill” looks stable, but critical technical roles are dragging while low-impact roles move quickly.

Once you de-compose, you stop asking “Is our culture healthy?” in a generic sense and start asking, “Where is our culture helping us meet our goals, and where is it failing specific groups of people?”

3. Cross-link: When metrics disagree, pay attention

Question to ask:

“What hidden assumptions become visible when I connect this metric to another one?”

Cross-linking is one of my favorite parts of Nicole’s framework because it exposes friction between the stories your numbers claim to tell.

For example:

• High engagement scores + high regrettable turnover = something’s off in how you define or measure “engagement.”
• Strong DEI percentages at the org level + low diversity in leadership roles = structural bottlenecks in promotions and opportunity.
• “Meets expectations” ratings + consistently high outcomes or innovation on a particular team = a performance system used to ration rewards, not reflect actual contribution.

When two metrics that should be related don’t move together, that’s not just a data problem. It’s a culture clue.

4. De-assume: Are your metrics quietly teaching the wrong lessons?

Question to ask:

“Does the way we measure this metric reinforce or contradict what we say we value?”

This is where you look at your HR dashboard and ask:
“If someone only saw these metrics, what would they assume we care about?”

Common disconnects:

• “We value collaboration”
  → Metrics prioritize individual performance scores and stack rankings, not team outcomes.
• “We prioritize well-being”
  → PTO is treated as an “absence problem,” and people are informally punished for using their benefits.
• “We believe in inclusion and belonging”
  → Dashboards track representation at the aggregate level but never look at who is in the room when decisions are made, whose ideas get taken forward, or whose feedback is ignored.

If your metrics constantly contradict your stated values, employees will believe the numbers.

So… what should actually be on an HR dashboard?

A “good” HR dashboard isn’t one with more visuals or fancier tools. It’s one that helps you:

1. See where your systems are out of alignment with your values
2. Spot early warning signs, not just lagging indicators
3. Start better conversations, not shut them down

Practically, that might include:

• De-composed views of engagement, retention, and promotion by manager, level, and demographic group
• Cross-linked metrics (e.g., engagement + turnover, promotion rates + performance ratings, internal mobility + retention of high performers)
• Leading indicators of culture health (quality of 1:1s, psychological safety on teams, cross-functional collaboration, internal referrals, participation in learning)
• Qualitative signals — not just the scores, but what people actually say in comments, listening sessions, and exit interviews

And importantly:
You treat every metric as a conversation starter, not a verdict.

If your dashboards feel like a mirage, you’re not alone, and you don’t have to fix it in isolation

If you’re looking at your HR dashboards and thinking:

• “We’re tracking a lot, but I’m not sure we’re learning anything.”
• “Our engagement survey is not actionable, but the executive team clings to it.”
• “We say we care about culture, but our metrics still look like compliance.”

You’re in good company.

This is exactly the work I do with clients: turning culture data into something you can actually trust and use without burning your team out on yet another survey cycle.

If you’d like help:

• Auditing your current HR and culture metrics
• Designing a “Metrics that Matter” dashboard aligned to your values and strategy
• Or integrating qualitative and quantitative data into a clearer story

👉 Reach out to Culture Grove and let’s talk about your dashboards.
You don’t fix culture, or metrics, by adding more noise. You cultivate better questions, better systems, and better signals.