Dynamic ML-Augmented Data Catalogs with Active Metadata
Data catalogs are a critical part of any data (and metadata) management strategy: as the complexity of data ecosystems and
the volume of data flowing through them grows, the traditional approach to a data catalog needs to evolve.
Rethinking data cataloging requires deeper context, breadth of data source coverage, and orchestrated automation to map and catalog sensitive & personal data with deep data insight - incorporating active metadata, direct and inferred attributes, and classifiers.
Download the whitepaper to learn: