Turning Assumptions Into Insights

Executive Summary
Scaling a design system across 48+ brands required more than components and tokens—it demanded real-time visibility into usage, adoption, and trust. I designed and built a comprehensive Health Dashboard leveraging the Figma REST API, D3.js, OpenAI, and soon, MCP to track component insertions, detachment rates, usage consistency, and overall system momentum.
This platform redefined how we measure success. By shifting from gut-feel to quantified engagement, we transformed the system from a static resource into a living product with measurable ROI and cross-functional alignment.
The Problem: What We Couldn’t See
Before the dashboard, adoption was assumed but not proven. Teams claimed to be “using the system,” but we had no idea
- Prioritize training and onboarding
- Justify deprecations or feature gaps
- Identify disengagement or rogue component use
- Validate success across dozens of brands and products
Figma's native analytics provided snapshots but lacked momentum tracking, quality scoring, and real-time signals required to manage a system at enterprise scale.

We needed more than usage counts. We needed to understand who was using the system, how far it extended across teams, and whether those usages remained consistent over time.
Why Not Just Use Figma Analytics?
Figma’s dashboard was a helpful starting point but it didn’t answer the bigger questions:
- How has usage trended over the past 90 days?
- Which components are falling off or being detached?
- Are teams reusing components as intended?
- Where is momentum gaining or fading?

Our custom dashboard filled these gaps—and laid the groundwork for deeper analytics across design and code.
The System I Built
Tech Stack
- Frontend: Next.js 15
- Data Sources: Figma REST API for usage and detachment metrics
- Visualization: D3.js for custom health visualizations
- AI Integration: OpenAI API for generating executive-ready summaries
- Future Integration: MCP (Model Context Protocol) for code-level analytics

The Dashboard in Action
Health Summary
AI-generated weekly insights highlighting key wins, risks, and usage shifts.

Component Insertions (90 Days)
Live usage tracking shows how adoption aligns with system events (new components, education, platform shifts).

Top Component Usage
Identifies most-used and underutilized components to guide future training, support, and deprecation planning.

System Health Breakdown
Radar visualizations scoring adoption coverage, usage growth, consistency, reach, and time to value.

System Health Breakdown
Radar visualizations track the five key dimensions of system value:
- Adoption Coverage
- Usage Growth
- Consistency
- Reach
- Time to Value

What’s Next: Connecting Design and Code
The next phase integrates
- Token usage tracking across repos
- Component adoption in production
- Storybook-to-live mapping
- Cross-source alignment (Figma ↔️ Code ↔️ Usage)
This will position the Health Dashboard as the
Reflections
“If you're not measuring adoption, you're just maintaining inventory.”
This project reshaped how we educate, maintain, and roadmap. By proving the system’s impact with real data, we earned stakeholder buy-in, improved onboarding, and repositioned the design system as a strategic product—not just a tool.
Measured Progress: Week 8 vs. Week 16
To show the tangible impact of the Health Dashboard, we tracked five key metrics over an eight-week span. The results illustrate how visibility, feedback loops, and system-level awareness transformed the design system from a static asset into a data-informed product.

