Research & Analysis
I began by interviewing users and analyzing telemetry workflows to understand how engineers process race data under pressure. In addition, I reviewed competitor dashboards and industry practices to identify common pain points such as cluttered visuals, slow anomaly detection, and cognitive overload.

Information Architecture
From these insights, I restructured the dashboard’s hierarchy, prioritizing critical alerts (like tire wear and fuel anomalies) and reducing secondary information. This ensured that high-stakes data was surfaced clearly and could be acted on immediately.

Wire-framing & Prototyping
I created low-fidelity wireframes to visualize simplified layouts and iteratively refined them through feedback loops. Once the structure was validated, I built a high-fidelity interactive prototype that simulated live race conditions, enabling realistic scenario testing.

Usability Testing
I ran Maze usability tests with participants acting as race engineers. The tests measured speed and accuracy of event detection, highlighting key improvements over the baseline. Feedback directly informed refinements that reduced visual clutter and clarified navigation.

Visual Design & Style Guide
Finally, I developed a cohesive visual language that balanced urgency with clarity, leveraging color, typography, and iconography to reduce cognitive load. A style guide was also produced to ensure consistency across future iterations of the dashboard.