📍 Problem & Context

The existing analytics platform was a Windows tray–based thick client that offered powerful visualization capabilities but required heavy configuration effort and limited accessibility, particularly for non-Windows users. Chart creation involved navigating dozens of interdependent settings, making rapid, iterative data exploration difficult for data scientists and analysts.

As analytical use cases expanded across teams, the desktop-only architecture became a bottleneck for scalability and collaboration. The goal was to translate complex desktop visualization workflows into a browser-based platform that preserved analytical flexibility while significantly reducing configuration overhead.

🛠 Design & System Constraints

Early in the project, I worked with the CTO and senior engineers to align on system-level constraints and design trade-offs. Key considerations included:

• Preserving system capabilities while reducing cognitive load for configuration-heavy charts

• Translating desktop-only interaction patterns into web-native paradigms

• Designing UI abstractions that aligned with existing data models and rendering pipelines

• Ensuring the web client could scale across diverse datasets and visualization types

To address this, I mapped the full configuration space across chart types to identify shared controls, conditional dependencies, and constraints, which directly informed the structure of visualization controls and frontend state management in the thin-client architecture.

📌 My Role & Contributions

I led the product and design translation layer between the existing system and the new web platform, spanning product definition, design, and frontend execution:

• Defined the migration strategy from thick client to web, determining which interactions required parity and which could be re-abstracted.

• Worked closely with data scientists and analysts to map real analytical workflows to visualization and dashboard requirements.

• Redesigned complex chart configuration models into modular web-based controls.

• Created mockups used to align design and engineering decisions under system and performance constraints.

• Contributed to frontend implementation of interactive visualizations using JavaScript/React and coordinating closely with engineers responsible for data and rendering layers.

The sample interfaces below reflect system-level decisions around state management, control dependency resolution, and progressive disclosure in a complex authoring tool.

📊 Impact & Results

The redesigned platform delivered measurable improvements validated through task-based user testing and performance evaluation:

• Task-based usability testing: In comparative workflow testing, users completed the same chart configuration tasks 20–95% faster depending on chart complexity. Session-time measurements and cursor tracking showed more direct interaction paths, fewer control revisits, and reduced time spent searching for configuration options, indicating lower cognitive and interaction overhead.

• Performance and scalability: Performance testing confirmed that the browser-based visualization system maintained responsive interaction and rendering behavior across diverse datasets and visualization types, supporting scalable analytics workflows previously limited to a desktop environment.

• Practical impact on data storytelling: Internal analysts were able to produce visually coherent dashboards despite limited design experience, shifting effort away from manual appearance tuning and toward analytical reasoning and storytelling.