Data catalog tools automate the discovery of data sets and data models throughout an enterprise, allowing business users to find what they need without querying the source systems. Many have metadata management capabilities to organize, describe and track data linesage so that users can understand the relationships between different pieces of information, while others also include a business glossary that systematizes nomenclature for common terms across teams. Some even have proactive observability features that monitor and control data quality at ingestion time to help prevent problems from developing.
The key to using a data catalog effectively is the ability for business and technical users to collaborate. Collaborative metadata management features tap into the subject matter expertise of business users, empowering them to curate data sets and create wiki articles that can be searchable and shared. These collaborative features can help build a data culture within an organization that fosters trust, transparency and accountability in the workplace.
A good data catalog should also provide powerful connectivity, with pre-built connectors and open APIs to extract metadata from almost any source. The catalog should have a powerful search and find functionality, similar to Google’s, which can find relevant information quickly and accurately. This should be coupled with an evaluation capability that allows users to preview a dataset, read user ratings and curator annotations and view data quality information.
All these capabilities are important for the success of a data catalog but for it to truly be a useful tool for business users it needs a core foundation. It’s essential that it be able to automatically detect and link metadata to the appropriate underlying data assets and that it can recognize when metadata is incorrect, missing or out of date. This can be achieved through machine learning and automation so that human error is reduced and new issues are flagged before they cause serious problems for users.
