FamilySearch

Project Type: Internship Project

Duration: 1.5 months

Role: UX Designer & Researcher

Methods: User Interviews, A/B Testing, Prototyping, Usability Testing

// CONTEXT

FamilySearch (Data Quality Score)

FamilySearch’s Family Tree platform helps users preserve and explore their family history. As part of an effort to improve data quality and user trust, the team introduced a Data Quality Score. It's a feature that flags inconsistencies and suggests areas for improvement in the family tree. My task was to redesign this tool for clarity, trust, and usability.

// PROBLEM

Data Quality Issues Were Undermining Trust

Many FamilySearch users encountered inaccurate or conflicting data in their family trees but had little guidance on what needed attention or how to resolve issues. Although an algorithm had been developed to detect such inconsistencies, the early design of the score interface lacked clarity, urgency, and guidance.


Key Pain Points Identified via A/B Testing & User Feedback

Through A/B testing and user interviews, we discovered key usability issues:

  • Unclear iconography: Users didn’t understand what the icons meant or what action they implied.

  • Low urgency and trust: The score didn't convey the importance of addressing issues.

  • Poor interpretability: Users struggled to understand how their score was calculated or how to improve it.


// SOLUTION

Designing a Solution

Design a Tool That Promotes Understanding and Action

Our goal was to reimagine the Data Quality Score tool to build trust and drive user engagement by making the issues clear, actionable, and digestible.


Strategic UX Objectives:

  • Simplify the score display and error presentation

  • Reduce cognitive load through improved visual hierarchy

  • Create urgency and clarity around data improvements


Iterating Till Success

1st Iteration:
Points of Failure:

  • Too much mental load

  • Lack of familiarity

  • Confusing navigation


Final Iteration
Key Feature 1: Clarified Quality Score Summary

Problem

Solution

How

Users struggled to understand what the Data Quality Score meant or how to act on it.

Clarify the score’s purpose and how to interpret it.

Reorganised layout: Issues were grouped and prioritised for easier scanning


Collapsible details: Sections could be expanded or collapsed to suit user preference


Clearer labels and icons: New visual language clarified types of data issues



Key Feature 2: Clarified Quality Score Info Sheet

Problem

Solution

How

Confusions:

  • Iconography and visuals lacked clarity and urgency

  • Users felt overwhelmed with dense or fragmented information

  • Score details didn’t feel actionable or meaningful to end users

Redesigning the Data Quality Score Experience:

  • Simplify UI with better visual hierarchy and clearer icon usage

  • Reduce cognitive load with clean layouts and groupings

  • Make content more actionable and highlight next steps.

  • Unified display: A single side panel displayed all data issues and score insights

  • Score bar redesign: The score bar colour now reflects urgency using an updated colour scale

  • Visible issue count: Users could immediately understand how many issues were affecting their score

  • Improved overview text: Language was simplified and clarified to encourage correction



Key Feature 3: Mobile-First Optimisation

Problem

Solution

How

Difficulty accessing or locating score information from various parts of the app.

Improve discoverability with multiple, logical access points.

  • Touch-friendly interface: Designed for smaller screens with clear hit targets

  • Multiple access points: Users could access the score from several pathways within the app

  • Balance with backend: Worked within system limitations to ensure technical feasibility while enhancing usability


//CONTRIBUTIONS

My Role:

My task was to redesign this tool for clarity, trust, and usability. Here’s a breakdown of what I worked on:

User Research & Usability Tests

  • Conducted 3 rounds of user interviews to understand pain points and gather qualitative feedback

  • Ran A/B tests comparing new designs with the original

  • Iterated on prototypes based on user testing rounds


Cross-Functional Collaboration

  • Worked closely with developers and product managers to ensure designs met technical constraints and could be implemented with minimal performance trade-offs


//RESULTS

Outcome & Impacts

To measure long-term impact, the following metrics could be tracked:

  • Engagement rate: % of users interacting with the score and issue resolution features

  • Completion rate: % of users resolving flagged issues after viewing the score

  • Clarity ratings: User feedback scores around how well they understand the tool

  • Trust indicators: Survey-based trust metrics

// REFLECTIONS & LEARNINGS

What I learned

User Testing Rigour

Asking the right questions during testing unlocked more actionable feedback.


Systems Thinking

Gained insight into how design decisions can impact backend complexity and performance.


Design Adaptability

Learned to adjust design processes to fit evolving project goals and team priorities.


Relationship Sensitivity

Understood the emotional complexity of representing family relationships and how subtle design choices can build or break trust.


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