Design System Analytics

Design systems codebases need audits, too. Here's a sneak peek at a new tool I've been working on. It's called DSAudit, and it's like Lighthouse, but for your design system's codebase. In our efforts to ensure design system completeness, from tokens to components to documentation, we've been kinda flying blind for a while now. Tools like Storybook help visualize. Linters help catch syntax. But there hasn't been a smart, opinionated tool that evaluates the entire design system holistically. Until now. DSAudit is a local Node-based auditing tool that inspects a full monorepo-based design system and returns: → A health score (modeled after Lighthouse) → Actionable insights across architecture, tokens, accessibility, coverage, and consistency → A full recommendations table → Component-level diagnostics → AI-powered, context-aware chat powered by Claude + MCP knowledge base behind Design Systems Assistant (https://lnkd.in/g2Ugkqp8) If you've used FigmaLint (https://lnkd.in/gDv3FMG3), you know I'm serious about audit tooling. That plugin helps validate your designs before they go to development and helps determine AI readiness. DSAudit picks up where FigmaLint leaves off, ensuring your codebase is production-ready, scalable, and aligned with your design system standards. → Currently built for single-repo design systems → Will support multi-repo ecosystems in a future release → Claude chat is scoped to your codebase + the design systems MCP I built, not just generic LLM info I'm not releasing it just yet. Still working out bugs and validating it across different codebases. But I wanted to post a quick video demo to get some early feedback. Would love to know: • Is this something your team would use? • Would it help your workflow? • Anything missing you’d want to see? Thanks for taking a look. I'm excited about where this is headed! #designsystems #ai

I've seen a lot of change in approaches to measuring design system adoption. Some of them might even be counterintuitive. The most successful design systems aren't built by the most talented designers or developers. They're built by teams who understand that adoption is about psychology, not pixels. 🧵 Some surprising findings that challenge what we think we know: 1. Small teams outperform large ones Conventional wisdom: You need a dedicated design systems team with specialists. Reality: Companies with 2-3 people working part-time on design systems often see HIGHER adoption than those with dedicated 10+ person teams. Why? Smaller teams can't afford to build in isolation. They're forced to collaborate closely with users from day one. 2. Documentation quality has almost ZERO correlation with adoption Anecdotally speaking, super well documented systems are adopted at the same rate as those with minimal documentation. What DOES correlate with adoption? Having a strong CHAMPION within each product team. 3. The adoption curve has flipped 2018: Design systems started with design, then engineering slowly adopted. 2024: Engineering teams are now driving adoption, with designers following. This is an interesting shift that orgs haven't always recognized. 4. Perfect components REDUCE adoption Teams that wait until components are "perfect" before releasing see lower adoption rates than those who release early and iterate based on feedback. Your design system should ship with rough edges. Let users help you sand them down. 5. Traditional ROI metrics are misleading. Measuring "time saved" isn't really that useful. But you know what is? Measuring things like: Reduced meetings Fewer design reviews Faster PR approvals Lower QA rejection rates These are the pain points teams actually care about. It's not always about better components—it's about better integration with workflows. Leading companies are now: ➡️ Embedding system usage into performance metrics ➡️ Creating adoption playbooks specific to each department ➡️ Building tools that meet teams where they are Design systems aren't just a product. They're a culture change. And culture change doesn't come from building the perfect button. It comes from understanding people. What has your org learned about design system adoption?

Been thinking about and researching how Design System teams can demonstrate ROI/impact. Here's an amalgamation of thoughts/advice based on content put out by others and my own experience: 1️⃣ Quantitative Metrics for Efficiency Use quantitative metrics to demonstrate how the DS is helping ship better products faster, such as: - Task completion time - Time from idea to market - Tech debt reduction - Accessibility You can even take these a step further to calculate the monetary impact of time/resource savings by estimating $/hr * time saved. 2️⃣ Collect Qualitative Feedback Gauge interest and sentiment from teams who are using the DS: - Satisfaction surveys - Team participation in DS office hours and channels 3️⃣ Customer Data Correlation: In addition to data collected on your internal teams, you could even look to see how customer data correlates with DS usage: - Analyze impact on feature adoption - Customer satisfaction with DS-built experiences 4️⃣ Strategic Presentation: Once you're ready to present the information, think about how you want to present them and to who. Who in leadership do you need buy in/support from? What do they care about? Show how your design system work moved the needle on key projects and company wide OKRs. 5️⃣ Your Turn! What's your team's approach to demonstrating impact? Share helpful metrics you've discovered Hope you find these insights valuable! 🌟 #designsystems #ROIImpact