Sept 2025

Samantha Speights

EUX Product Database

Users EUX Team Members

The Problem

EUX teams had no central place to find basic product information. The existing SharePoint list was hard to search, often outdated, and required manual upkeep so people resorted to asking around in Slack instead. Teams needed a faster, more reliable way to find and maintain product context.

Goals

  • Streamline data intake with an intuitive interface that simplifies product entry and maintenance

  • Enable robust search and filtering so teams can find product information instantly without asking around

  • Drive data integrity by empowering product owners with a frictionless update workflow

  • Architect the database to support a future AI agent for natural language querying of product data

Contributors Product Team Members

Approvers Design Excellence Team Members

Current State Journey Map

Supporting Data

  • Existing SharePoint list was manually intensive, prone to incomplete data, and had no search capability

  • Teams wasted time on basic product context instead of design and metrics work(include stats)

The Solution

The Workflow

Personas

Approvers Design Excellence TeamMembers

Product Team Members Contributors

EUX Team Members Information Seekers

What I Built

  • Airtable database schema

  • Product list view

  • Product information page

  • Add New Product form

  • Update Product form

  • UXFoundations embedded interface

Usability Testing

Results

The Test

  • 7 participants EUX Designers

  • moderated 1-on-1 sessions

  • Test environment: database embedded in SharePoint with dummy data.

Scenerios

  • Scenario 1 Search: Find the PM, UX designer, and engineer for a product called Asset Manager

  • Scenario 2 Filter: Find all products using the MUI design system

  • Scenario 3 Add: Submit a new product to the database and receive a confirmation email

  • Scenario 4 — Update: Add two new UX designers to an existing product entry


What’s Next?

  1. Iterate on interface based on usability testing findings

  2. Drive product team adoption and database completion (target: 200 entries)

  3. Build AI agent layer to support natural language querying of product data

  4. Explore automation for update workflows to reduce manual overhead