Published: 12 September 2019
Demand for data catalogs is soaring as organizations continue to struggle with finding, inventorying and analyzing vastly distributed and diverse data assets. Data and analytics leaders must investigate and adopt ML-augmented data catalogs as part of their overall data management solutions strategy.
Included in Full Research
- Organizations Continue to Struggle With Manually Identifying and Inventorying Distributed and Heterogeneous Data Assets That Deliver Value
- The Data Catalog Evolution — From Traditional to ML-Augmented
- The Abundance of Data Catalog Tool Choices and Types Is Creating Confusion in Data Catalog Selection
- Siloed Data Catalogs That Do Not Connect to Broader Enterprise Metadata Management Initiatives Will Limit Their Overall Value