How to Analyze HR Data
How to analyze HR data starts with cleaning your headcount file — standardizing department names, job levels, and employment status — before calculating any workforce metric. Dirty HRIS exports produce misleading turnover rates and headcount counts that don't match what managers see in their own teams.
Upload your HRIS export and ask "What is our turnover rate by department this year?" or "How does tenure distribution compare across job levels?"
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Upload your spreadsheet.xlsx .xls .csv — free to try, no accountOr start with
What this export contains
| Employee ID |
| Name |
| Department |
| Job Title |
| Job Level |
| Hire Date |
| Termination Date |
| Employment Status |
| Salary Band |
| Manager |
What usually goes wrong with it
Department names have dozens of variants
'Engineering', 'Eng', 'Software Engineering', and 'Product Engineering' appear as separate departments in the export, making headcount by department wildly inaccurate without cleanup.
DataMimi Datamimi clusters department name variants and shows you the groups before merging so you confirm the mapping rather than having it applied silently.
Active and terminated employees are mixed in the same file
HRIS exports often include all historical employees, so headcount appears far larger than reality unless filtered to Employment Status = Active.
DataMimi It filters to active employees automatically when you specify the Employment Status column and surfaces the active headcount count alongside total rows.
Turnover rate is calculated differently by every manager
Finance uses end-of-period headcount in the denominator, HR uses average headcount, and neither documents which they used — the same period produces three different turnover numbers.
DataMimi It calculates turnover rate using both methods (end-of-period and average headcount) and labels each clearly so you can pick the one that matches your reporting standard.
Compensation data is in a separate file
Salary information exports separately from the headcount file, so pay equity analysis requires a manual join that most HR teams can't maintain on a regular cadence.
DataMimi It joins compensation and headcount exports on Employee ID and flags rows where the join fails so you know the match rate before running pay equity analysis.
Common questions
How to analyze HR data: what is the most important metric to start with?
Turnover rate by department. It tells you where retention problems are concentrated and is the metric most directly tied to recruiting cost and manager performance.
What is a good employee turnover rate?
Average annual turnover in the US runs 15-20% across industries. Tech and retail run higher (20-30%); government and healthcare run lower (10-15%). Voluntary turnover is the number to watch — involuntary turnover (layoffs) distorts the picture.
What is the difference between voluntary and involuntary turnover?
Voluntary turnover is employees who choose to leave (resignations). Involuntary turnover is exits initiated by the company (terminations, layoffs). Track them separately because they have different causes and different interventions.
How do I calculate average tenure from an HRIS export?
For active employees: subtract Hire Date from today and convert to years. For terminated employees: subtract Hire Date from Termination Date. Average both separately — active tenure and completed tenure tell different stories.
How much does Datamimi cost?
Free plan: $0/month, 40 credits, no credit card required. Lite: $9/month, 400 credits. Starter: $24/month, 1,500 credits, up to 3 simultaneous files. Pro: $59/month, 5,000 credits with rollover. Team: $199/month, 20,000 credits for 5 users.
Try it with your own file
DataMimi reads the file you actually have — merged cells, headers below row one, totals pasted at the bottom — and shows which rows and columns every number came from.
Ask about your file