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Rebuilding Information Architecture

Product

Internal Product Engineering Sharepoint Page

Timeline

2 weeks

Methods

Card Sort (unmoderated)

Context

When I stepped into this project, the study had already been executed but required careful analysis to ensure the results were accurate and actionable. I independently applied dendrogram analysis for the first time - accounting for setup limitations and extracting reliable patterns to deliver a defensible, user-driven information architecture.

The Problem

Internal teams struggled to navigate a shared platform used to store documentation, tools, and resources.

 

Content had grown organically over time, resulting in:

  • Unclear category structures

  • Inconsistent labeling

  • Difficulty locating key information


The team needed a user-driven information architecture to improve findability and usability.

Research Goals

  • Understand how users mentally group platform content

  • Identify intuitive category structures

  • Surface gaps and misaligned terminology

  • Provide a clear foundation for restructuring the platform

My Role

I led the analysis, synthesis, and delivery of insights, including:

  • Interpreting card sort data

  • Identifying patterns using dendrogram analysis

  • Translating findings into actionable IA recommendations

  • Presenting results to product engineering leadership

Methodology

Participants completed an open card sort, organizing platform content into groups that made sense to them.

Analysis focused on identifying:

  • Strongest grouping patterns

  • Secondary associations

  • Content with no clear placement.

Impact

The research directly informed a reorganization of the platform's information architecture, including:

  • Restructuring category groupings

  • Refining labeling and terminology

  • Improving overall navigation clarity


This resulted in a more intuitive and scalable structure for internal teams.

Reflection

I stepped into this study after it had already been designed and launched, which meant working within an existing card sort setup that introduced variability in how participants grouped and labeled content.

To account for this, I took a more interpretive approach during analysis - looking beyond the dendrogram to assess cluster strength, areas of disagreement, and participant intent. This allowed me to focus on patterns that were meaningful and actionable despite inconsistencies in the data.

This reinforced the importance of adapting your analysis to the conditions you inherit, and ensuring that insights remain grounded and reliable even when a study isn’t set up perfectly.

Takeaway: Strong research means delivering reliable insights - even when you have to work within imperfect conditions.

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