HR analytics is a powerful tool for optimizing workforce management. However, many companies collect vast amounts of data but fail to use it for decision-making. As a result, analytics remains useless, and businesses miss opportunities for growth and cost reduction. Let’s explore the main mistakes in HR analytics and how to fix them.
1. Collecting Data Without a Clear Purpose
One of the most common mistakes is accumulating data without understanding its purpose. Companies track hiring metrics, turnover rates, and employee Net Promoter Score (eNPS) but don’t set goals to improve them.
How to fix it?
Identify key HR metrics that truly impact the business.
Link analytical indicators to business objectives (e.g., reducing hiring costs or increasing employee retention).
Formulate specific hypotheses and test them using data.
From my experience, many managers insist on gathering metrics “just in case” without a clear plan for using them. As a result, HR analytics turns into meaningless reports that no one reads. Only after adopting a data-driven approach can hiring and onboarding processes become more efficient.
2. Lack of Analytical Culture in the Company
Data is collected, but no one analyzes it in depth or uses it for decision-making. HR analytics often remains at the level of reporting rather than becoming a tool for strategic planning.
How to fix it?
Foster a data-driven decision-making culture in HR.
Train HR specialists in basic analytics and data interpretation.
Use dashboards and BI tools (such as Tableau) for data visualization and real-time monitoring of key metrics.
When I introduced dashboards to my team, we immediately saw where processes were failing. For example, we discovered that some hiring sources provided high-quality candidates, while others only created the illusion of a candidate flow. This insight helped us reallocate the budget and speed up vacancy closures.
3. Ignoring Insights from Data
Many companies notice problems but take no action. For instance, a high turnover rate during the probation period may indicate issues in hiring or onboarding processes, yet without deeper analysis and adjustments, nothing changes.
How to fix it?
Implement a corrective action system based on analytics.
If a metric deviates from the norm, identify the cause and develop an improvement strategy.
Regularly review HR processes using data.
At one company, we noticed that turnover during the probation period reached 30%. It turned out that HR and managers had different expectations for candidates. Introducing clear evaluation and onboarding criteria reduced turnover to 12% within six months.
4. Poor-Quality or Inconsistent Data
Errors in data collection, fragmented information sources, and the lack of a centralized storage system can lead to misleading conclusions.
How to fix it?
Centralize HR analytics data in ATS and HRIS systems.
Automate data collection to minimize human error.
Regularly check data accuracy and completeness.
I once encountered a situation where different reports showed conflicting numbers of closed vacancies. The issue stemmed from manual data entry. Implementing a unified ATS system resolved the problem, ensuring accurate data.
5. Focusing Only on Averages
Many companies look at aggregated metrics without considering differences between departments, positions, and teams.
How to fix it?
Analyze data at the department and process level.
Consider the context: compare internal company metrics rather than relying solely on external benchmarks.
Use data segmentation for more precise insights.
6. Insufficient Automation of HR Analytics
Many HR teams still rely on Excel and Google Sheets, which slows down processes and increases the likelihood of errors.
How to fix it?
Use modern ATS, HRIS, and BI platforms to automate analytics.
Set up automated reports and dashboards.
Invest in data integration between systems.
7. Underestimating Employee Retention Metrics
HR teams often focus on hiring metrics while neglecting turnover reasons and employee engagement levels.
How to fix it?
Conduct exit interviews and analyze the results.
Measure eNPS and track its trends.
Implement predictive analytics to identify employees at high risk of leaving.
Conclusion
HR analytics becomes valuable only when data-driven decisions are made. Companies that not only collect but actively use HR metrics gain a competitive advantage: they reduce turnover, cut hiring costs, and improve employee performance. The key is to embrace change and integrate a data-driven approach into workforce management.
From my experience, when HR analytics is done right, it helps not only track the current situation but also predict future trends. For example, analyzing eNPS allowed us to anticipate a wave of resignations and take preventive measures, reducing turnover by 15% in a year. If you’re not yet fully leveraging HR analytics, now is the time to start!
