HR’s Uneven Data Landscape

When tackling a people issue—be it turnover, engagement, productivity, or workforce planning—there are often many potential data fields we could turn to. But some fields carry more weight and reliability than others.

Let’s consider some examples.

✅ More Accurate HR Data Fields

These data points typically have clear business consequences if they’re incorrect. Because employees or managers will notice and raise a flag, they tend to be more accurate:

• Salary / Pay GradeIf someone’s pay is wrong, they will speak up. The feedback loop here is strong and immediate—making this field more trustworthy.

• Employee Name, ID, or NRIC/FINTied to compliance, payroll, and legal records. Errors often cause disruptions or risk exposure, which means they’re usually corrected fast.

• Start and End DatesWhile they may seem basic, these dates impact eligibility for long service awards, leave entitlements, and benefits. That creates an automatic feedback loop—if they're wrong, both employee and employer notice.

• Leave BalancesErrors in this field can lead to disputes over time off. Employees are acutely aware of their entitled leave, which helps keep the data clean.

• Job Title / Department / Reporting ManagerThese are often used for workflow approvals and access controls. Inaccuracies can disrupt operations, prompting quick corrections.

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⚠️ Less Reliable (But Commonly Used) HR Data Fields

These data points are more subjective, influenced by human behavior, or collected inconsistently:

• Reason for LeavingThe classic “don’t burn bridges” moment. Exiting employees often opt for polite or politically neutral answers, masking the real reasons. Yet, HR teams often look to this field for answers to turnover questions—only to be puzzled when the data doesn't add up.

• Employee Engagement ScoresWhile valuable in large volumes, these scores are sensitive to timing, sentiment, and doubts about anonymity. Someone's mood on survey day can heavily skew their response.

• Training Effectiveness FeedbackMost commonly gathered via post-training “smile sheets.” These often reflect short-term impressions rather than lasting capability improvements. They tell us how much participants liked the session—not what they learned or applied.

• Skills Inventories / Self-AssessmentsMany organizations maintain skills data based on employee self-declarations. But self-assessments can suffer from overconfidence or modesty. Without external validation or observable performance evidence, these fields can give a misleading picture of actual workforce capability.

• Succession Readiness RatingsOften influenced by internal politics or personal preferences rather than objective criteria. What one manager calls "ready now," another might call "needs development."

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The Real Risk: Misplaced Trust in Messy Data

We’re not saying these “messier” data fields should be discarded. They still offer value—but they’re better suited for validation than diagnosis.

If you’re investigating why your turnover is rising, starting with “reason for leaving” may lead you in circles. But if your own hypothesis (e.g., poor management, workload pressure) aligns with even some of the reported reasons—it adds weight to your findings.

Similarly, engagement scores may validate the sentiment you've picked up through qualitative means. But they’re rarely strong enough to be the starting point for action.

In short: data can confirm or challenge our thinking, but it shouldn’t replace it.

Analytics Is an Art (As Well As a Science)

The popular narrative around analytics often emphasizes precision, automation, and objectivity. But in practice—especially in HR—it’s much messier. Interpreting people data requires judgement, experience, and critical thinking.

At FYT Consulting, we believe that one of the most undervalued skills in workforce analytics is knowing which data to use first. Starting with the right indicators—those that are accurate, timely, and close to the actual problem—can save HR teams weeks of effort and lead to more meaningful, actionable insights.

Let’s Elevate the Discussion

We’d love to hear from you:

• What HR data fields have you found most (or least) useful in driving decisions?

• How do you build confidence in the insights you draw from imperfect data?

• Are there practices or frameworks you use to evaluate data quality in your analytics work?

💬 Join the conversation—comment below or drop us a message. Let’s learn from one another and raise the bar for workforce analytics together.