Your HR data is lying to you. Here's how to fix it

While HR departments across organisations are investing heavily in people analytics, dashboards, and other data-driven tools, not all metrics accurately reflect reality. For instance, when turnover data excludes involuntary exits and falsely shows that retention numbers are strong, it results in the misallocation of resources, wage inequality, and failed interventions. 

The challenge for HR teams is understanding when data-driven insights start giving misleading results, and intervening at the right time to prevent it from becoming a major issue. Another cause for the faulty metrics can be organisations feeding unreliable data to the HR tools. This drives poor decision-making since HR teams are under the impression that the data is correct. 

What’s the reason behind unreliable HR data?

Organisations implement sophisticated HRIS platforms, build elegant dashboards, and hire data analysts to extract insights, yet consistently encounter situations where the conclusions drawn from data contradict what HR professionals who actually work with employees observe daily. When investigated, the pattern reveals a fundamental design flaw: HR data collection was built around administrative convenience rather than analytical accuracy, and nobody went back to fix the foundation.

This problem is compounded because the HR data is often entered by employees who are more focused on keeping their reputation intact rather than accuracy. For instance, when a team manager is drafting a termination letter and labels it as voluntary instead of honestly reporting it as involuntary to make their retention number look better, is knowingly entering wrong data. This has a cumulative effect on the data, as it misrepresents reality and impacts future HR policies in a negative manner. 

How can HR teams measure whether their data is truly reliable?

Most HR teams track data completeness, what percentage of fields are filled, but not data accuracy, which is what actually determines whether the data supports good decisions. Data reliability requires different metrics that reveal whether what the data says happened matches what actually happened.

• Data correction volume: If HR teams are constantly correcting data errors, recoding miscategorised records, or adjusting historical data, the data collection process is not working. High correction volume means the data is systematically unreliable. Measuring how much correction work is required reveals whether quality problems are isolated issues or fundamental process failures.
• Stakeholder trust in data insights: When managers consistently question turnover data or when employees challenge diversity metrics, these trust failures signal data quality problems. Tracking how often data-driven recommendations get challenged or ignored because stakeholders do not trust the data reveals whether quality issues have reached the point where data insights lose credibility.
• Decision reversal due to data corrections: When strategic decisions get reversed or adjusted after discovering the supporting data was wrong, these reversals represent the actual damage bad data causes. Tracking decision changes attributed to data quality failures quantifies the cost of accepting unreliable data.

How can HR teams improve the quality of their historical data?

Reliable HR data is data collected with incentive structures that reward accuracy over flattering metrics, designed with definitions that capture meaningful distinctions, and validated through mechanisms that catch systematic bias before it poisons analysis.

• Separation of data entry from performance metrics: For instance, when the employee entering turnover data is evaluated on retention rates, every data point becomes a negotiation between accuracy and self-interest. Effective data quality design ensures that the employees creating data are not the same employees whose performance the data will measure.
• Granular capture: Coding all departures as either voluntary or involuntary obscures whether the employee was pushed out, whether they left to join a direct competitor, or whether compensation was a factor in their departure. Data quality requires capturing the distinctions that matter for creating an error-free database.  
• Mandatory context fields: When an employee codes a termination as voluntary for an employee who was on a performance improvement plan, the system should get an explanation. These friction points slow data entry but dramatically improve data reliability by making it harder to enter data that is analytically misleading.

What processes can HR teams build to catch data distortion immediately?

Data quality mechanisms work when the employees responsible for data integrity have the capability, authority, and bandwidth to actually identify and correct data that misrepresents reality.

• Data literacy: When an HR professional sees turnover suddenly drop by half, they should immediately question whether actual retention improved or data definitions changed. This healthy scepticism requires training HR teams to understand what the plausible patterns are and what the signs of a data quality issue are.
• Authority to challenge data: When a senior leader's data shows results that conflict with ground truth, junior analysts must have the authority to flag the discrepancy. Data quality requires organisational support for accuracy, even when the inaccurate data is entered by a senior member.
• Time allocation: If HR teams are already operating at maximum capacity and data validation is treated as a secondary task, it will not happen consistently. Effective data quality design allocates specific time for reconciliation, validation, and cleanup, recognising this work as fundamental to analytics-driven processes of the organisation.
• Cross-functional visibility: Employees who see diversity initiatives celebrated while their own experience suggests little change should be able to question whether the metrics are capturing reality. This external validation provides the signal that purely internal data review might miss.

Conclusion

HR departments building reliable analytics are those that design data collection around accuracy from the start, that separate data entry from performance incentives that reward distortion, and that validate data against ground truth rather than assuming system data reflects reality.

Effective data quality acknowledges that employees entering data face incentives that often conflict with accuracy, that administrative data collection was never designed for analytical use, and that sophisticated analytics on unreliable data produce wrong insights. When HR teams build data collection with proper scrutiny, data becomes a strategic asset rather than a liability disguised as insight.