What hr analytics is
HR analytics is the use of workforce data, headcount, hiring, attrition, attendance, leave, overtime, performance and payroll cost, to understand what is happening in the organisation and to support decisions.
Why it matters
A company-wide average hides most of what matters. An attrition problem is usually concentrated in one team, one tenure band or one manager, and only analysis at that level shows where to act.
Common challenges
Data in many places
When attendance, payroll and performance live in separate tools, combining them is a project.
Definitions
Two reports calculating attrition differently produce two different numbers.
Reports nobody uses
Dashboards built without a question to answer become wallpaper.
The process, step by step
- 1
Start with questions
Decide what leadership needs to know.
- 2
Agree definitions
Define each metric once.
- 3
Break it down
Look by department, tenure, grade and location.
- 4
Act and track
Change something and watch the metric respond.
Best practices
- Define each metric once and use it everywhere.
- Always look below the company average.
- Pair every number with the question it answers.
Mistakes to avoid
- Reporting only company-wide averages.
- Building dashboards before agreeing definitions.
- Treating correlation in HR data as proof of cause.
How software helps
Analytics on an integrated HRMS reads from one record, so attendance, leave, overtime, attrition and payroll cost can be sliced the same way without exporting and merging spreadsheets.
Questions about hr analytics
Commonly as the number of leavers in a period divided by the average headcount over that period. The key is to agree one definition and use it consistently.
Those that answer leadership's current questions. Headcount, attrition, overtime, leave utilisation and payroll cost are common starting points.
Because problems are usually concentrated. A healthy average can hide one team with very high attrition.
The use of workforce data, such as headcount, hiring, attrition, attendance, leave, overtime, performance and payroll cost, to understand what is happening in the organisation and support decisions.
By department, tenure, grade and location. Problems are usually concentrated in one team, tenure band or manager, and only analysis at that level shows where to act.
Usually because the metric is defined differently in each. Defining each metric once and using that definition everywhere avoids conflicting figures.
Not on its own. Treating correlation in HR data as proof of cause is a common mistake; the data shows where to look, and action and tracking show what works.
Dashboards built without a question to answer become wallpaper. Starting with what leadership needs to know, and pairing each number with that question, keeps them useful.