Data catalogs are databases that store metadata about data. This information makes it readable and analyzable, and enables people to discover and search for needed data.

Metadata is a standardized language that describes how the information in a digital object is structured. It can also include information about how it relates to other information.

These digital assets are often underestimated, and it is critical that organizations take care of them. Investing in a data catalog is one way to ensure that they are consolidated and utilized for achieving business goals.

Modern data catalogs are built on top of a knowledge graph. They provide a unified access layer to all data teams. The catalog allows users to understand and apply the concepts of business, technical artifacts, and other information sources.

Today, data catalogs are used by many different types of users. Analysts and data consumers can use them to find and join related data across multiple databases.

These tools also help organizations meet regulatory compliance requirements. They often integrate with data governance software. Their features are designed to take advantage of artificial intelligence and natural language queries.

AI/ML-based platforms can perform active metadata management, using AI to analyze similarities between data sets and to identify changes that occur over time. Using pre-built reports, users can track the impact of changes on data assets.

In addition, AI/ML-based platforms can provide predictive analytics. For example, they can identify data characteristics such as value distribution and statistical information.