AI Metadata Management transforms static data catalogs into intelligent systems, automating tagging, enhancing discovery, and building trust for enterprise AI, accelerating analytics and reducing risk.

Active metadata is dynamic, real-time metadata used to drive action, automation, and intelligence across the data stack for modern data management.

Self-serve analytics empowers non-technical users to access, explore, and analyze data independently.

Column-level lineage traces individual data columns from source to destination, revealing transformations, dependencies, and usage.

Enterprise data modeling creates a unified data structure, ensuring consistent definitions and reliable analytics across your entire organization.

AI-powered data lineage provides clarity on data flow, helping data teams understand origins, transformations, and impacts with ease.