Data catalogs have entered the mainstream. Over the last few years, they have evolved from a tool for finding datasets into a critical component of data governance and a necessity for any modern, data-driven company. The need for well-governed, easily searchable data assets continues to grow as new trends in analytics, such as data science and machine learning (ML), increase organizational appetites for many types of data. In this environment, a data catalog instills trust in data assets and the analytics outputs they support. By moving closer to an enterprise-wide data catalog, organizations can better meet the needs of new, sophisticated data consumers as well as traditional business intelligence (BI) users.

At its core, a data catalog captures and archives metadata, but today’s data catalogs can do much more. Increasingly, artificial intelligence (AI) automates the more mundane tasks of populating and tagging catalogs, and social tools connect users across the business to document and share tribal knowledge. New personas such as data stewards and data curators rely on catalogs to manage data access controls and to direct self-service analysts toward shared sources of truth. Meanwhile, third-party integrations bring the insights of catalogs to other tools in the data analytics workflow. 

This report will profile three data catalogs, each of which presents different approaches to helping users find data assets. The report will enable data leaders to better understand which data catalogs are best suited to the requirements of their organization.

 Key Takeaways:

• What a data catalog does
• Who uses a data catalog
• Types of use cases    
• Different technological approaches    
• Recommendations for specific data catalogs