AI Tool for HR Analytics — Upload People Data, Ask Questions

What this export contains

What usually goes wrong with it

• Turnover rate by department requires manual formula work

  Your HRIS export has hire and termination dates but calculating annualized turnover rate by department requires date math and division across hundreds of rows.

  DataMimi Ask 'what is the annualized turnover rate by department this year?' and Datamimi uses the hire and termination date columns to calculate and rank each department.

• Compensation equity analysis is politically sensitive and slow

  Comparing salary by level, role, and gender requires pivot tables that take time to build correctly and are often left undone because of the sensitivity.

  DataMimi Ask 'is there a salary difference by gender at the same job level?' and Datamimi filters by level, groups by gender, and calculates average salary for each group.

• Time-to-fill by role is tracked in a different system

  Hiring data lives in an ATS while headcount data lives in the HRIS. Connecting them to understand which roles take longest to fill requires a manual VLOOKUP.

  DataMimi Upload both your ATS and HRIS exports and ask 'which roles had the longest average time to fill this year?' — Datamimi cross-references both files (Starter plan and above).

• High performers who are flight risks are invisible

  Identifying employees with high performance ratings and below-market compensation requires combining the performance and salary columns across hundreds of rows.

  DataMimi Ask 'which employees have a top performance rating but a salary below the department median?' and Datamimi identifies flight-risk employees from your data.

Common questions