Summary
Problem
How did I know it's a problem?
4
One of the workshops
User statement analysis
Here I observed that…
A typical search performed in the session looked like this.
…users weren't struggling to search, they didn't trust what the search returned.
Why does this problem matter?
Data misinterpretation
Analysts building reports on deprecated or duplicate tables produced conflicting numbers, which surfaced late and cost rework.
Knowledge silos
Knowing which table to trust became tribal knowledge held by a few tenured people, creating a bottleneck and a single point of failure.
Approach
Understanding the opportunities
These opportunities directly shaped the four solutions I designed.
Improving the search
Analysts building reports on deprecated or duplicate tables produced conflicting numbers, which surfaced late and cost rework.
Generating asset information
Knowing which table to trust became tribal knowledge held by a few tenured people, creating a bottleneck and a single point of failure.
Building trust and transparency in search results
Knowing which table to trust became tribal knowledge held by a few tenured people, creating a bottleneck and a single point of failure.
Clear path to make an asset discoverable
Knowing which table to trust became tribal knowledge held by a few tenured people, creating a bottleneck and a single point of failure.
Key decisions
I explored two directions: a single list where Spotlight assets carry a tag, and a two-tab layout separating Spotlight results from everything else.
Descriptions emerged from research as the single most useful attribute for identifying an asset. I collaborated with business and engineering to define which details the AI-generated descriptions should surface, so the output was consistently useful rather than generic.
Final designs
NLP Search Users could describe what they needed in plain language instead of knowing the exact table name — making the catalog usable for people new to the data.
Spotlight Tab Trusted assets get their own tab, with automatic fallback to all results when no Spotlight assets match…
With automatic fallback to other assets when no Spotlight assets match.
AI Descriptions Every asset gets a readable, accurate summary — reducing the need to open an asset just to evaluate it.
Readiness Checklist Owners follow a step-by-step checklist that makes the qualification criteria explicit and the path to nomination clear.
Early Results
Learnings
We started this project trying to fix search. Watching users showed us the real problem was trust. That distinction shaped every decision that followed.
The readiness checklist works well for nominating individual assets, but bulk nomination remains unsolved. Users flagged this in testing, and it's the most immediate gap to address post-launch.Two areas worth exploring next: using AI to review data lineage — the record of where a dataset came from and how it's been transformed — and simplify data quality setup, reducing manual effort for asset owners. And introducing a trust indicator on the asset detail page, either a Spotlight badge or a filter toggle, so trust is visible beyond just search results.