HR Analytics Dashboard — Employee Attrition

Business Question

What drives employees to leave — and where should a company focus to reduce attrition? This project explored a standard HR attrition dataset to surface the patterns: which departments, job roles, age groups, marital statuses, working-year bands and overtime conditions correlated with the highest departure rates.

Data Sources

A publicly available HR attrition dataset (IBM HR Analytics sample) with 1,470 employee records, covering department, job role, age, gender, marital status, monthly income, years at company, years with current manager, overtime status and survival outcome (left or stayed).

KPIs

Total headcount, attrition count and rate, active employee count, average age, and overtime exposure rate — broken down across department, job role, age group, gender, marital status, working years, and overtime vs non-overtime cohorts.

Dashboard Design

A single-page Power BI report with colour-coded KPI tiles at the top and six breakdown charts below — department horizontal bars, job role bars, age group columns, gender split, marital status comparison, and a stay-rate-vs-attrition-rate stacked view by overtime status.

The view below is a recreation matching the structure of the published dashboard, with values consistent with the original dataset analysis.

Key Responsibilities

• Connected the Excel dataset to Power BI and cleaned data using Power Query
• Built calculated columns for age group and working-year banding specifically to enable the breakdown analysis
• Created DAX measures for attrition rate, stay rate, active employee count and overtime attrition rate
• Designed the dashboard layout with a minimal colour palette and a consistent gradient pattern across all visuals

Technologies I Personally Used

Insights

• R&D (56.1%) and Sales (38.8%) accounted for nearly all attrition — HR was negligible at 5.1%
• Lab Technicians and Sales Executives had the highest role-level attrition counts
• Age 26–35 showed the steepest departure rate — the early-to-mid career band
• Single employees left more frequently than married or divorced staff
• Employees with 0–5 years at the company drove the bulk of departures
• Overtime workers showed 69.5% attrition vs 10.7% for those without overtime — the starkest single pattern in the data

Business Decisions

This was a learning project — the decisions here were analytical choices rather than operational ones: which attributes deserved their own visual, how to build the age-group column in Power Query rather than Excel, and how to handle the stay-rate vs attrition-rate comparison using DAX.

Business Impact

This was a personal learning project built during the SRCC-GBO programme. It was published on LinkedIn in February 2023 and received engagement from peers and professionals commenting on the attrition patterns surfaced.

Lessons Learned

• Calculated columns built for specific analytical questions (like age bands) are more useful than trying to force raw fields into visuals — the question should come first
• Reducing the colour palette to three main colours and one accent made the dashboard easier to scan than earlier attempts with more varied colouring
• DAX measures like attrition rate and stay rate made cross-filtering reliable — computed values in DAX are more trustworthy than calculated columns when filters need to apply

Reflection

What I learned: this was my first serious Power BI build — the biggest shift was learning to think in measures rather than just columns, and to let the business question drive the visual rather than picking charts first.

What I would improve today: add a manager-level view (years with current manager correlated strongly with attrition in the data), and build a predictive score for at-risk employees using the same features.