A data-driven governance framework to eliminate design debt at scale.
Achieved 100% data visibility, turning implicit design drift into an audited, searchable asset (41 closed, 101 open).
Systematically resolved 41 issues (28.9%), including 29.7% of high-severity blockers, directly protecting platform quality.
Focused remediation on primary user friction points, driven by Usefulness (31.0%) and Findability (23.8%).
Project Overview
Project Overview
The user problem
As our multi-application B2B/B2B2C enterprise platform scaled across various market localizations and customer segments, continuous descoping under tight Go-To-Market (GTM) deadlines caused severe user experience degradation.
The business objective
Establishing a data-driven governance framework to measure, prioritize, and systematically eliminate platform experience debt at scale.
Transform an unmanaged operational liability into a structured, trackable, and resolvable workflow while ensuring cross functional alignment.
My role and scale of ownership
Led the UX organization through the end-to-end design governance initiative. Drove cross-functional alignment with Product Management and Engineering leadership. Designed the UX Debt Classification Framework, standardized debt taxonomy, and established data visualization mechanics to drive executive-level decision-making.
How I converted UX debt data into Roadmap Execution
Execution
Execution
Understanding the problem
In complex enterprise platforms, trade-offs between speed-to-market and experience quality are common. However, without a dedicated system to track de-scoped experience elements, short-term workarounds turn into permanent operational friction. The issue wasn't just existing debt, but a lack of visibility, which led to organizational action paralysis.
This project took place at a company building complex B2B and B2B2C solutions on a multi-application platform with layered capability and heavy customisation, some workstreams focused on digital transformation, others on continuous enhancement of existing technology.
Main problem categories:
Identifying solutions
"Data is your friend, documentation is your partner."
To move from reactive complaints to strategic execution, I refactored an internal decision log into a scalable UX Debt Framework (originally a simple table of decision, owner and date).
After brainstorming with the team, I reviewed a foundational skeleton for categorisation and we expanded it into a system for tracking issues by platform section and UX issue type.
After a sequence of iterations I evolved it into a more elaborate version where every classification element carried a definition everyone agreed on, covering both issue type and prioritisation.
Standardized Taxonomy & Definitions
UX Debt Classification
| Findability | Information architecture, navigation and content classification. |
| User flow | How well the steps to complete a task match the user's mental model. |
| Usefulness | Whether the solution meets user needs, not just technical requirements. |
| Accessibility | Contrast, focus indicators, and text alternatives. |
| Credibility | Accuracy of quantitative data shown in reports and cost figures. |
| Learnability | Time and guidance required to learn a feature. |
| Interaction design | Component behaviour, transitions, and scrolling effects. |
| UI, aesthetics and DNA | Design system components, padding and visual styling. |
| Clarity of Communications | Labels, headlines, messages and overall written text. |
| Persuasive design | Copy or components designed to nudge user behaviour. |
| Consistency | Consistency of flows, navigation and components across the system. |
Severity Matrix
| Low | Minor cosmetic issue with no impact on task completion. |
| Medium | Aesthetic or minor flow issue affecting discoverability. |
| High | Major flow issue requiring documentation or workarounds. |
| Unusable/Gap | Blocks task completion, needs support or engineering fix. |
Parked due to shifting vision or business priorities.
Product Prioritization
| P1 | Critical, blocks core roadmap delivery or a live commitment. |
| P2 | High value, targeted within the next 1-2 releases once P1 capacity is covered. |
| P3 | Moderate value, paired with related roadmap work rather than scheduled on its own. |
| P4 | Low urgency, logged and revisited during backlog grooming or roadmap planning cycles. |
Early attempts to prioritise old accrued debt with Product managers weren't successful. As it was hard for us to process the growing table, it was equally hard for them, a large volume of data can cause action paralysis, especially against the pull of business-as-usual work.
So I split the main table in two: one for UX (interaction design, flows, UX copy) and one for UI (design system debt and visual polish), then split each into open and closed issues to separate debt still outstanding from debt that had been addressed or ticketed for later.
Data Schema Standardization
Formatted data entry across both Resolved and Unresolved issue tables with explicit attributes (Date Raised, Area, Section, Product Priority, UX Priority, UX Classification, Design Status, Date Closed, Type of Close).
| Date Raised | Area | Section | Product Priority | UX Priority | Ux classification | Design Status |
|---|---|---|---|---|---|---|
Open issue table criteria
| Date Raised | Date Closed | Type of close | Area | Section | Product Priority | UX Priority | Ux classification |
|---|---|---|---|---|---|---|---|
Closed issue table criteria
Measuring the UX debt
"Show the big picture - charts turn data paralysis into action"
Data Visualization
By now it was possible to filter and search across UX debt, nevertheless, while documenting all these issues it seemed to everyone in the team that the amount of debt was only increasing and the table became more daunting every-time we looked at it.
That's when charts came in. Aggregating the same classification data visually opened a new way of seeing the problem, letting us surface what kinds of UX debt existed, the severity of open issues, which sections of the platform generated the most issues, and how many issues we were closing per month.
Unresolved issues by type
Taking action on UX debt
"One step after the other, to continuously close the debt."
The goal was never to close all the debt overnight against a live roadmap, but to show the data and set a plan to work through it: sharing the methodology with Product managers, running sessions to prioritise issues by severity, section and UX category, and identifying new roadmap work that could be paired with related UX debt.
Resolved vs unresolved debt by severity type
Same severity data, as a 100%-stacked comparison between unresolved and resolved decisions.
Unresolved debt by area
Resolved debt by area
Results & Business Impact
Results & Business Impact
Debt made visible, prioritised, and shrinking
After months of consistent classification and reporting, the backlog stopped only growing, issues were being closed as fast as new debt was logged. What began as implicit design drift evolved into a shared, searchable data asset across Product, Design, and Engineering. By embedding experience debt directly into agile roadmaps, we transformed design quality from an ad-hoc afterthought into a repeatable, data-driven operational discipline.
Total Issues Tracked
Achieved 100% data visibility across the platform, turning hidden experience debt into a fully audited data asset (41 closed, 101 open).
Backlog Cleared
Systematically resolved 41 platform issues, including 29.7% of high-severity blockers, directly safeguarding long-term product quality.
Focused on Usefulness
Resolved 13 issues targeting the system's primary source of user friction, resolving critical task-completion issues.
Focused on Findability
Resolved 10 issues targeting navigation and discoverability gaps to streamline core user workflows.
Full visibility into design drift
100% tracking coverage transformed implicit design drift into an audited, searchable data asset shared across Product, Design and Engineering, so nothing fell through the cracks and every trade-off had a documented reason.
High-severity blockers resolved
29.7% of high-severity debt was prioritized and closed, removing major workflow blockers and lowering reliance on support documentation and second-line customer service escalations.
"This initiative shifted the organization's mindset, transforming user experience quality from a subjective design discussion into a steadily decreasing operational metric."
Core friction points targeted
Focusing team efforts on the top UX issues successfully addressed Usefulness (31% of closed issues) and Findability (23.8% of closed issues), the system's two biggest sources of friction.
Debt work built into the roadmap
A data-driven SLA and sizing model integrated UX debt closure directly into agile roadmaps, creating a repeatable mechanism that prevents debt from accumulating unchecked.
Proof the backlog could shrink
Once closures started outpacing new debt, the record itself became evidence for stakeholders that the accrued issues could keep shrinking without derailing delivery.