GUIDE
The Data Catalog Primer for Enterprise AI
Data catalogs are going through a paradigm shift – again. And this time, it could make or break your AI.
INSIDE THE GUIDE
Inside the guide
The Problem
Data catalogs weren't built for AI.
Traditional data catalogs can’t deliver the right context at the right time, so AI repeatedly fails in production. The answer isn’t better models – it’s a new approach to data catalogs that treats context as infrastructure, making AI accurate, governed, and successful at scale.
Get the EbookThe Solution
Our Approach
4 Pillars of Data Catalogs
Programmable bots, embedded collaboration, visibility, and open standards.
Active Metadata Management
Always-on systems that collect, process, and operationalize metadata.
The Context Layer
Delivers understanding to AI and orchestrates metadata across systems.
Metadata Lakehouse
Turns metadata from a lookup system into a context store.
95%
AI pilots that fail in production without context
5x
increase in query accuracy with embedded context
70%
faster delivery of new data assets with metadata
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