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
Discover the key data catalog insights and next-gen strategies in this comprehensive guide.
The Evolution of Metadata Management
A brief history of how metadata management has changed since 1990.
The Problem with Data Catalogs
Why data teams today are searching for a better way to manage their data.
The Era of Data Catalog 3.0
The principles of third-generation data catalogs in the modern data stack.
5 Key Data Catalog Trends
How teams, tech, governance, and metadata changed the game.
The Rise of the Context Layer
The next evolution of data catalogs and how they will power AI.
AI-Ready Context Infrastructure
The foundational capabilities needed to power AI at scale.
Data catalogs weren't built for AI.
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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