Climbing the People Analytics Staircase – Step 1

Smart Data Starts at the Source: Getting Data Collection Right

Part of the series Climbing the Staircase of People Analytics: Why Every Step Matters

Introduction

This article is the first deep dive in my series Climbing the Staircase of People Analytics: Why Every Step Matters, where I explore how organizations can intentionally build analytics maturity step by step. In this piece, we start at the very beginning—the foundational step that often gets skipped or rushed: data collection.

Before insights, before dashboards, before predictive modeling, there’s one question that defines the success of your People Analytics practice: Are we collecting the right data, in the right way?

Smart Data Starts at the Source

In People Analytics, everyone wants to talk about the output. We’re drawn to dashboards, forecasts, and models. But before any of that can happen, we have to begin with the inputs—and they need to be clean, consistent, and purposeful.

Data collection often lives in the shadows. It’s not glamorous, and it rarely makes it into executive summaries. But it is the most critical point in the entire People Analytics journey. Why? Because every analysis you do is only as good as the data you start with. And if that data is flawed, fragmented, or misunderstood, then every insight that follows will be built on shaky ground.

“We Have an HR System” Is Not Enough

One of the most common misconceptions I see is the belief that having an HRIS means data collection is “handled.” But a system doesn’t ensure data quality. It doesn’t standardize job titles or guarantee that lifecycle events are being recorded accurately. That takes intentional design and consistent practice.

The truth is, data quality is shaped at the point of entry. If your onboarding workflow allows for free-text department names, or if location changes are tracked manually through email, then your reporting will always be compromised.

Data collection is not just a technical setup—it’s an operational behavior. It’s about how HR teams, managers, and employees interact with systems and processes on a daily basis.

Structure Creates Trust

Collecting the right data isn’t about capturing everything. It’s about capturing the right things—consistently, and in a way that supports future insight.

That means building structure. Dropdown fields instead of free text. Mandatory fields in onboarding workflows. Clear expectations for updating job titles and reporting lines. System validations that prevent errors before they enter your dashboards.

Equally important is defining what each data point means. A “promotion” might mean one thing to Talent Acquisition and another to Compensation. These small misalignments become big blockers when you try to analyze patterns or trends across your workforce.

Data Lives Everywhere

Today’s people data doesn’t live in just one system. It’s scattered across HRIS platforms, survey tools, learning systems, and spreadsheets. It comes from onboarding portals, engagement platforms, time trackers, and performance reviews.

That distributed reality means data collection must be a shared responsibility. Everyone who touches employee data—whether directly or indirectly—plays a role in shaping its accuracy and usefulness.

To make this work, organizations need to embed data quality into daily workflows. Managers must understand their role in maintaining accurate records. HRBPs must feel empowered to question inconsistencies. Analysts must be able to trace data back to its origin.

When data stewardship becomes a collective habit, not a siloed task, everything downstream becomes more reliable.

Audit, Question, Repeat

One of the biggest risks in this step is the illusion that everything is working because there’s no visible error. But in practice, most systems contain quiet inconsistencies that only surface once you try to analyze them.

Regular audits are key. Check for inactive users in active roles. Look for inconsistent formatting in job titles. Review whether exit reasons are being logged in a standard way. These aren’t technical cleanups—they’re operational insights. They reveal how aligned (or misaligned) your people processes really are.

Human Behavior Shapes Data Integrity

At its core, this step is deeply human. Systems don’t create data—people do. That’s why building awareness and capability around data collection is so important. It’s about training HR teams to think like data stewards, giving managers the tools and reminders they need, and creating systems that make the right thing the easy thing.

When people understand why their inputs matter, they engage differently. When they see how data fuels decisions, they take more care in entering it. That shift—from compliance to ownership—is what turns data collection into a strategic asset.

Final Thoughts

Data collection may be the quietest step in the People Analytics staircase, but it’s the one that defines the stability of everything that follows. Get it right, and you unlock the ability to see, understand, and influence your workforce with confidence. Get it wrong—or ignore it—and you’ll spend your time troubleshooting instead of analyzing.

The first step isn’t the most exciting. But it is the most essential.

In the next article, we’ll climb to the second step—Data Quality & Management—where we explore how trust in data is maintained, structured, and protected.

Want to read the full overview of the staircase? Start here → Climbing the Staircase of People Analytics: Why Every Step Matters

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