Overview
This conceptual project explores how employment and skills data can be presented clearly, accessibly and with reduced cognitive load for policy analysts and the public.
Government dashboards often contain high-density information… multiple charts, filters, and comparative metrics… Which can overwhelm users if hierarchy and accessibility are not carefully considered.
The goal of this project was to design a scalable, WCAG 2.2 compliant dashboard that translates complex employment data into actionable insight.
The Problem
Users need to:
- Compare employment trends across regions
- Identify industry growth or decline
- Understand skills shortages
- Filter data by age, education and geography
- Interpret trends accurately without misreading the data
Existing dashboards in this space often suffer from:
- Overcrowded layouts
- Inconsistent chart selection
- Over-reliance on colour
- Poor accessibility contrast
- High cognitive load
The challenge was to simplify complexity without oversimplifying the data.
My Approach
1. Defined Primary User Tasks
Before designing visuals, I mapped key user questions:
- Is employment trending up or down in this region?
- Which industries are growing fastest?
- Where are skill shortages emerging?
- How does this region compare nationally?
This informed chart selection and layout hierarchy.
2. Information Hierarchy First, Visuals Second
I structured the dashboard in 3 layers:
Layer 1 – Quick Scan (KPI Summary Row)
High-level metrics with clear trend indicators.
Layer 2 – Pattern Recognition (Trend & Industry Charts)
Line chart for long-term change.
Bar chart for comparative industry growth.
Layer 3 – Deep Analysis (Table + Filters)
Sortable data table with detailed breakdown.
This reduces cognitive overload by supporting progressive disclosure.
3. Accessibility Built In
All components were designed to meet WCAG 2.2 AA standards:
- 4.5:1 colour contrast minimum
- Trend indicators include icons, not just colour
- Keyboard-accessible focus states
- Clear labelling of axes and legends
- Data values visible without hover-only dependency
For data visualisation specifically:
- Colour was never the sole method of communicating change
- Charts included labels and grid support for readability
4. Component-Based System
The dashboard was built using reusable Figma components:
- KPI cards with trend variants
- Chart containers with consistent spacing and export controls
- Accessible filter dropdowns
- Data table rows with hover and focus states
This ensures scalability across additional dashboards or datasets.
Key Design Decisions
Why a Line Chart?
Users need to interpret trend direction and volatility over time.
Why Horizontal Bar Chart?
Improves readability for long industry names and ranking comparison.
Why KPI Row First?
Supports rapid scanning before deeper exploration.
Why Visible Values (Not Hover Only)?
Government users may print dashboards or use assistive tech.
Outcome
This concept demonstrates how complex employment data can be:
- Structured for clarity
- Accessible by design
- Reduced in cognitive load
- Scalable across multiple regions
- Aligned with government standards
It reflects my approach to designing data-heavy interfaces: clarity first, aesthetics second, accessibility always.
Reflection
Designing dashboards is not about adding more charts. It is about helping users interpret data correctly. The most important measure of success is not visual polish, but whether users can draw accurate conclusions quickly and confidently.