IBM HR Analytics — Employee Attrition Console
A four-page Power BI console that takes a flat HR file with no dates in it and still answers the three questions an HR director actually asks: who is leaving, why, and who is next.
This project can be shown in full. It uses a public dataset with no sensitive content, so every figure, method and result on this page is the real thing — the visuals are screenshots of the working output, not rebuilds.
Context
The IBM HR dataset is one of the most over-used files in analytics, and almost every version of it online makes the same mistake: it reports a headline attrition percentage as if it were a turnover rate. It is not. The file is a point-in-time snapshot with no date columns at all, so “16.1%” is the share of the recorded population that has left — not an annualised rate.
I decided that constraint was the interesting part. If you cannot build a trend line, you have to earn insight from structure instead: department, role, tenure band, pay. The design brief became “say only what this data can actually support, and say it clearly”.
Approach
- Label the limits on the canvas. Every KPI carries a plain-English qualifier — “share of population, not annualised”, “currency not stated in source”, “at 50% of salary — an assumption, not data”. A reader should not have to guess which numbers are solid.
- A baseline on every comparison. Each attrition bar chart draws the 16.1% population baseline as a dashed reference, so a department is read against the whole rather than against its neighbours.
- Show the denominator. Small groups produce dramatic percentages. Every bar carries its n, so 19.1% attrition in a 63-person HR department is not mistaken for a bigger problem than 13.8% across 961 people in R&D.
- Four pages, four questions. Workforce (what is the shape), Why People Leave (what correlates), Who Is At Risk (where to act), Pay & Equity (is compensation part of it).
The four pages
Built on the public IBM Watson Analytics sample HR dataset — 1,470 employee records. These are real screenshots of the working report.
1 Workforce overview
2 Why people leave
3 Who is at risk
4 Pay & equity
Outcome
39.8% attrition found in one role
The structural read produced sharper findings than a headline rate would have. Sales Representative attrition runs at 39.8% — 2.5× the population baseline, against 2.5% for Research Directors. The onboarding cliff is stark: 36.4% of people in their first year and 34.5% in their second leave, and attrition among the under-25s is 35.8% against 9.2% for 35–44.
Read together, those three findings point at one intervention rather than a general retention programme: the risk is concentrated in early-tenure, younger staff in a specific job family.
What I’d do differently
The replacement-cost figure is an assumption dressed as a metric — 50% of salary is a common rule of thumb, not a measurement, and I labelled it as such on the canvas rather than quietly presenting it as data. With a real cost model per role it would become genuinely decision-grade.
The larger limitation is the dataset itself. Without hire and exit dates you cannot compute a true turnover rate, cohort survival, or seasonality. If I were commissioning this data I would ask for those three columns before anything else.