Climbing the People Analytics Staircase – Step 2 Deep Dive

From Chaos to Confidence: Mastering Data Quality and Governance in HR

Expanded guidance based on Step 2 of Climbing the Staircase of People Analytics: Why Every Step Matters

Introduction

In the first step of the staircase, we focused on collecting the right data—at the right time, in the right way. But raw data, even if accurately captured, is never ready for analysis on its own. It must be cleaned, structured, and governed with intention. This brings us to Step 2: Data Quality & Management—the engine room where trust is built or quietly broken.

This article takes a technical and operational lens to explore how to make your HR data not just usable, but trustworthy. Because in People Analytics, data that can’t be trusted becomes insight that can’t be used.

The Hidden Cost of Bad People Data

People data touches everything: recruiting decisions, compensation planning, DEI reporting, workforce strategy. But when job levels are inconsistent, managers are outdated, or employee statuses are unclear, all downstream reporting is compromised.

Bad data doesn’t just skew insights—it erodes credibility. And once you lose the confidence of your stakeholders, rebuilding it takes much longer than cleaning a column in Excel.

The real cost of poor data quality isn’t technical—it’s cultural. It’s the silence in a meeting when someone asks, “Can we trust this number?” and no one is sure.

What High-Quality People Data Looks Like

Good data is:

• Accurate: It reflects reality. A “Director” really is a director. An “active” employee really is still working.
• Complete: Key fields are not missing. There are no blanks where a system needs values to calculate or analyze.
• Consistent: The same value (e.g., a department name or location) is entered the same way across the system.
• Timely: It’s updated close to real-time, not weeks after changes occur.
• Traceable: You can track who updated it, when, and why.

Achieving all five of these qualities is not about heroic manual work—it’s about building a data management framework and enforcing it as a team habit.

1. Implement a Human-Centric Governance Model

Start by defining who owns what. In People Analytics, a single “data team” can’t—and shouldn’t—own everything. Data stewardship must be distributed.

Assign data owners for each dataset or system. For example:

• HR Ops owns employee lifecycle data.
• Talent Acquisition owns candidate funnel data.
• Compensation owns salary bands and job levels.
• DEI leads co-own identity-based data with Compliance.

Owners should be responsible for validating data, ensuring definitions are applied correctly, and reviewing quality regularly. They’re not just system admins—they’re partners in accuracy.

Support them with a data governance charter that outlines:

• Field definitions
• Rules for updates
• Data access roles
• Frequency of audits
• Escalation paths when discrepancies are found

This document becomes your single source of truth—not just for data, but for how data is managed.

2. Clean Proactively—Not Just When There’s a Problem

Too often, HR teams wait until there's an issue—like a broken dashboard or an executive question—to clean up their data. But cleaning should be part of your operating model, not a reactive scramble.

Set up automated data quality checks in your HRIS or BI tool (or even via Google Sheets scripts or Excel Power Query). Examples:

• Alerts for duplicate employee IDs
• Flags for employees without assigned cost centers or managers
• Checks for empty required fields in active records

Build regular audits into your calendar—monthly, quarterly, and annually. Make it part of the rhythm of the People team.

When data quality becomes habitual, your analytics becomes stable—and leadership learns to trust it.

3. Create and Maintain a Living Data Dictionary

People data terms get misunderstood all the time. What’s a “termination”? Does it include contract expirations? What defines a “promotion”? Title change? Compensation bump? Both?

To avoid confusion, develop a living data dictionary that includes:

• Clear definitions of each data field
• Allowed values or formatting rules
• Source systems
• Owners and maintainers
• Business context or usage examples

Store this in an accessible place (like Notion, Confluence, or Google Drive) and keep it updated. It’s not a policy—it’s a living reference that reduces errors, speeds up onboarding, and promotes shared language across functions.

4. Set Standards Across Systems—Not Just Within Them

Your HRIS might be clean, but if your ATS or learning platform uses different values for departments or locations, things fall apart fast.

Create cross-system standards by:

• Using global IDs for departments, cost centers, and locations
• Syncing naming conventions across platforms (e.g., "Sales – LATAM" vs. "LATAM Sales")
• Mapping integrations to align field formats (e.g., Workday to Greenhouse, or BambooHR to CultureAmp)

This is especially critical for organizations with hybrid system environments or recent mergers/acquisitions. Without alignment, your reports will always need manual clean-up—and that creates long-term friction.

5. Document Data Lineage to Preserve Trust

Data lineage—knowing where a dataset originated, how it was transformed, and what filters were applied—is critical in People Analytics, especially when building dashboards and models.

In practice, this means:

• Logging the query logic or transformation steps used
• Storing intermediate data sources
• Using BI tool features like Tableau’s data source annotations or Power BI’s model view
• Archiving versions of critical reports or dashboards with timestamped files

When you can show how a number was built, you reduce the chance of misinterpretation. More importantly, you protect the integrity of the insight.

6. Build a Feedback Loop with End Users

Data quality doesn’t improve in isolation. It improves when your end users—HRBPs, managers, recruiters—see themselves as part of the solution.

Enable them to flag errors directly in the system (e.g., via form, ticket, or embedded feedback button). Include a “report issue” link in dashboards. Invite them into quarterly data review meetings. Recognize those who consistently help spot and correct anomalies.

When people across the org feel ownership of data—not just compliance—you create a culture of care. And that culture is the most powerful engine of long-term quality.

Final Thoughts

Clean data isn’t an accident—it’s a system. A habit. A culture. It’s something you build deliberately, enforce consistently, and champion publicly.

Step 2 of the staircase is often invisible to leadership. But it’s felt every time a dashboard loads without error, a trend line tells a reliable story, or a model output lands with confidence.

In the next phase of the series, we’ll climb to Step 3 – Descriptive Analytics, where we begin to turn data into insight, and insight into conversations.

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If you missed the foundation, go back to Step 1 → Building Reliable Data from Day One
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