Data Analysis

Data-Driven Design: My Approach to Analysis & Insight

My design process is deeply grounded in data analysis—not just gut instinct. With a Master’s in Data Analytics and over a decade of experience in UX, I’ve made data a cornerstone of my work in everything from design systems governance to AI integration strategy.

A Foundation in Analytics

I hold a Master of Science in Data Analytics, where I gained experience in:

  • Statistical modeling & forecasting

  • Predictive analytics using R and Python

  • Machine learning techniques (KNN, clustering, logistic regression)

  • Data visualization best practices

  • Data scraping and cleaning pipelines

My portfolio includes hands-on work with:

  • Python (pandas, NumPy, TensorFlow, scikit-learn)

  • SQL + R for exploration and analysis

  • Tableau and Excel for dashboarding and executive comms

Applying Analysis in UX & DesignOps

At Edward Jones, I used data to:

  • Prioritize design system investments based on usage analytics and adoption rates

  • Uncover accessibility gaps by running component-level usage audits

  • Track component redundancy and reduce visual inconsistency at scale

  • Measure AI tool impact through pilot feedback and structured post-mortem analysis

Real Projects

Figma AI Pilot Impact Analysis

After leading the internal rollout of Figma’s AI tools, I created a feedback loop that included:

  • Surveys and usage tracking

  • Qualitative interviews

  • Comparative task time benchmarking

This helped us frame the ROI of AI tooling, cut through hype, and build a long-term vision for integration.

Design System Adoption Metrics

I developed and refined frameworks for:

  • Tracking component usage across teams

  • Identifying dead patterns and candidate retirements

  • Identifying when an experimental pattern should be promoted to the full design system

  • Creating shared visibility into design debt

UX Research Data Synthesis

In many projects, I’ve synthesized:

  • Quantitative usability metrics

  • Survey trends

  • Open-ended stakeholder input
    Into insight dashboards and simplified recommendations that empower product teams.

Tools I Work With

  • Languages: Python, SQL, R

  • Libraries: pandas, NumPy, matplotlib, seaborn, TensorFlow, scikit-learn

  • Visualization: Tableau, Power BI, Figma (data-rich design)

  • Soft skills: Stakeholder storytelling, async reporting, design-aligned metrics

Why It Matters

Strong data skills let me connect business goals, technical limitations, and user behavior into unified product decisions. Whether I’m running a workshop, auditing a system, or piloting AI, I’m always asking:

“What does the data tell us—and what do we still need to learn?”

A Few Projects

Here are a few projects that show my back-end experience with data analytics: