Skill Management Ecosystem
Engineering a bi-directional mapping system to harmonize global skill taxonomies and external market data at Deloitte USA scale.
Challenge
Fragmented skill taxonomies across internal DST records and external market libraries created days of manual mapping work.
Solution
A bi-directional Translation Tables module with active upload validation, dual-view relational engine, and a full governance queue.
Impact
100% data integrity since launch. Mapping reduced from days to minutes. Admins self-correct with contextual tooltips.
The Gatekeeper
Backend Logic · Data Validation · Error HandlingBefore designing, I defined the Backend Analysis Logic for data ingestion — moving the system from “Passive Uploading” to “Active Validation.” The system intercepts every CSV upload with a 3-point check:
- —Uniqueness: Checking IDs against existing DST and Market Library records
- —Structural Integrity: Identifying rows missing mandatory metadata (Category/Source)
- —Mapping Conflicts: Flagging skills already linked to other hosts to prevent many-to-many data corruption
Design Decision
Instead of a generic Error message, I designed an Upload Log that highlights specific row-level errors, allowing users to correct data before it hits the production database.
An interactive tooltip on every flagged error provides plain-language guidance for non-technical admins — transforming dead-end error messages into guided troubleshooting.
The Relational Engine
Information Architecture · Relational Data Modeling · Mental Model AlignmentCore complexity: managing One-to-Many relationships. One internal Deloitte skill (Host) can represent dozens of external variations (Outsource). Solution: a Dual-View Toggle:
- —View A — Host Perspective: Centers on the Deloitte skill, showing all external aliases mapped to it
- —View B — Outsource Perspective: Centers on the external source, ensuring every skill links to exactly one parent
// Key Feature
The Remap Drawer allows users to move an Outsource skill from one Host to another — triggering automatic updates across Pending Translations and Translation Logs to maintain a perfect audit trail.
System Governance & Traceability
Governance · Compliance · System-Wide SynchronizationBecause this manages the “Source of Truth” for thousands of skills, every action must be fail-safe and traceable. Every mapping action flows into the Pending Translations queue for Admin verification before production.
// Key Feature
The Translation Log records every relationship change — move, reason, timestamp — creating a complete, immutable audit trail.
Results
100%
Data Integrity
Zero corruption since launch. Pre-check validation blocks thousands of duplicate/orphaned entries.
↓↓
Support Overhead
Contextual tooltips allow admins to self-correct, reducing IT burden significantly.
∞→min
Mapping Time
Taxonomy mapping reduced from days to minutes. Architecture scales to new data sources.
In Enterprise SaaS, UX is a balance between User Empathy — reducing admin fatigue — and Data Architecture — managing complex relational logic. I transformed a rigid data-entry tool into a scalable, bi-directional intelligence engine.
Solving something like this for your team?
Book an intro call →