Why data-driven businesses need a data catalog

Enterprises need better tools to learn and collaborate around data sources. Data catalogs with pioneering machine learning capabilities can help you tap your valuable data

Why data-driven businesses need a data catalog

Relational databases, data lakes, and NoSQL data stores are powerful at inserting, updating, querying, searching, and processing data. But the ironic aspect of working with data management platforms is they usually don’t provide robust tools or user interfaces to share what’s inside them. They are more like data vaults. You know there’s valuable data inside, but you have no easy way to assess it from the outside.

The business challenge is dealing with a multitude of data vaults: multiple enterprise databases, smaller data stores, data centers, clouds, applications, BI tools, APIs, spreadsheets, and open data sources.

Sure, you can query a relational database’s metadata for a list of tables, stored procedures, indexes, and other database objects to get a directory. But that is a time-consuming approach that requires technical expertise and only produces a basic listing from a single data source.

You can use tools that will reverse engineer data models or provide ways to navigate the metadata. But these tools are more often designed for technologists and mainly used for auditing, documenting, or analyzing databases.

In other words, these approaches to query the contents of databases and the tools to extract their metadata are insufficient for today’s data-driven business needs for several reasons:

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