HR Analytics Dashboard
Uncovering the Drivers Behind Employee Attrition
An interactive Tableau dashboard analysing 1,470 employee records to surface attrition patterns across departments, age groups, and satisfaction levels, enabling HR leadership to shift from reactive exits to proactive retention.
Interactive dashboard. Use filters to explore the data.
Attrition Was a Black Box
The organisation was losing talent at an alarming rate, but HR leadership had no visibility into why. Exit interviews captured anecdotes, not patterns. Managers flagged attrition as a problem only after headcount had already dropped, and by then, backfilling was expensive and disruptive.
When I looked at the data, the numbers were stark: overall attrition sat at 16.1%, but beneath that headline figure lay wide variance across departments, age groups, and job roles. R&D alone accounted for 56.12% of all attrition, yet this was buried in spreadsheet exports that nobody was reading.
The core issue wasn't a lack of data. It was a lack of structure. HR had employee records, satisfaction surveys, compensation data, and demographic information, all sitting in disconnected files. Without a unified analytical view, every retention conversation started from scratch.
The data existed. The visibility didn't.
Pain Points Identified
No centralised attrition view
Attrition data scattered across spreadsheets, HRIS exports, and exit interview notes with no single source of truth
Reactive retention strategy
Leadership only addressed attrition after employees had already left, with no early warning system or predictive signals
Hidden departmental disparities
R&D's 56% share of total attrition was invisible in aggregate reports, masking the severity of the problem
Satisfaction data unused
Employee satisfaction surveys were collected but never correlated with attrition outcomes
Demographic blind spots
Age group, education level, and tenure patterns were not factored into workforce planning
16.1%
overall attrition rate across the organisation
Patterns in the Noise
I started with a dataset of 1,470 employee records spanning demographics, job roles, compensation, satisfaction ratings, and attrition status. The first step was exploratory data analysis: profiling distributions, identifying nulls, and understanding the shape of the workforce.
Three patterns emerged quickly. First, the 25 to 34 age group accounted for a disproportionate share of exits. Early career employees were leaving faster than any other cohort. Second, employees who rated their job satisfaction as 1 or 2 (out of 4) had attrition rates nearly triple those who rated 3 or 4. Third, the R&D department wasn't just the largest department; it was haemorrhaging talent at a rate that dwarfed Sales and HR combined.
These weren't just correlations. When I cross-referenced satisfaction with department and tenure, a clear narrative formed: dissatisfied early-tenure R&D employees were the highest-risk segment. This was the insight that would shape the entire dashboard design.
Discovery Process
Data Profiling
Audited 1,470 records across 35 fields, identified key dimensions for attrition segmentation and cleaned inconsistencies
Cohort Analysis
Segmented attrition by age group, department, education field, and job role to isolate high-risk populations
Satisfaction Correlation
Mapped job and environment satisfaction ratings against attrition outcomes, revealing a clear inverse relationship
Cross-Dimensional Analysis
Combined department, tenure, and satisfaction to identify the highest-risk employee segment: early-tenure R&D staff
Key Insight
Dissatisfied early tenure R&D employees were the single highest risk attrition segment, a finding that was invisible in aggregate reporting.
Design for Decision-Making
The goal was not to build a reporting dashboard. Reporting tells you what happened. I wanted to build a decision support tool, one that helps HR leaders ask better questions and act on the answers.
I structured the design around three principles that emerged directly from how stakeholders described their workflow gaps during discovery conversations.
Design Principles
Segment Before You Summarise
Aggregate metrics hide the story. The dashboard leads with department-level and cohort-level breakdowns so users see where attrition is concentrated before looking at totals.
Correlation Over Causation, But Make It Actionable
Satisfaction and attrition are correlated, not causal. The dashboard frames this correlation in terms of actionable segments: which groups are both dissatisfied and leaving, so HR can intervene where it matters.
Self-Service First
Every chart is filter-linked. Clicking a department, age group, or education field updates the entire view — empowering HR business partners to explore their own populations without waiting for an analyst.
Building the Dashboard
I designed and built the dashboard in Tableau, choosing it for its interactive filtering capabilities and Tableau Public publishing for stakeholder access. The layout was structured to guide the user's eye from high-level KPIs down to segmented breakdowns.
Every visual was mapped to a specific question that stakeholders had asked during discovery. No chart existed without a corresponding decision it was meant to support.
Dashboard Components
KPI Summary Bar
Total employees (1,470), attrition count (237), attrition rate (16.1%), and average age (37). The four numbers HR checks first
Department Attrition Breakdown
Donut chart showing R&D (56.12%), Sales (38.82%), and HR (5.06%), immediately surfacing where attrition is concentrated
Age Group Distribution
Bar chart segmented by age bands revealing the 25 to 34 cohort as the highest attrition group across all departments
Satisfaction vs. Attrition Matrix
Heat map correlating job satisfaction levels (1 to 4) with attrition rates. Satisfaction level 1 shows nearly 3x the exit rate
Education Field Analysis
Stacked bar showing attrition distribution by education background. Life Sciences and Medical degrees lead exits
Interactive Cross-Filters
Click any segment to filter the entire dashboard. Department, age group, gender, and education are all interlinked
From Data to Retention Strategy
The dashboard transformed how HR leadership approached attrition. Instead of quarterly reports that arrived too late, they had a live tool that revealed exactly where to focus retention efforts.
The R&D finding alone shifted budget allocation. The department received targeted engagement programmes and mentorship initiatives for early career employees. Satisfaction survey cadence was increased from annual to quarterly for high risk segments identified by the dashboard.
Impact Metrics
1,470
Employee records analysed across 35 data fields
56.12%
R&D's share of total attrition, surfaced as the critical intervention point
3×
Higher attrition rate for employees with satisfaction level 1 vs. level 4
25 to 34 Age Band
Identified as highest-risk cohort for early attrition across all departments
What I Learned
Aggregates Lie
A 16.1% overall attrition rate sounds manageable. But when R&D carries 56% of exits and your highest value talent pipeline sits in the 25 to 34 age band, the aggregate is hiding a crisis. Always segment before you summarise.
Design for Questions, Not Answers
The best dashboards don't provide answers. They help users ask better questions. Interactive filters and cross dimensional views turned passive report consumers into active analysts.
Satisfaction Is a Leading Indicator
By the time someone leaves, the decision was made months ago. Satisfaction data, when properly visualised and correlated with outcomes, becomes the closest thing HR has to a predictive signal.