Data catalog tools help organizations meet the growing demands of data management. They support data democratization and enable non-technical users to easily navigate through data, see the business context and manage resources more effectively.
They also help data stewards demonstrate the provenance of data for establishing compliance to governmental regulations, and support data risk management. Additionally, they provide data quality management capabilities to help users identify errors and correct them continuously.
Modern data catalogs often rely on artificial intelligence (AI) and machine learning (ML) to automatically evaluate the quality of data that flows into them. This can be embedded directly in the catalog so that users are aware of any errors and can quickly correct them, without having to re-create the catalog.
Typical features include search and find functionality that can surface relevant data sets for users. Some also include business glossaries that help users understand what different terms mean in context of a particular piece of data.
Collaborative metadata management with end users allows teams to share the knowledge and expertise they have about data across the enterprise. This helps create a shared “tribal knowledge” that is key to a healthy data culture.
A catalog that enables collaboration between business and IT professionals is critical for building a trustworthy data culture, implementing a data governance strategy to lock down workflows, and ensuring data privacy compliance with governing regulations like GDPR. It also provides a single source of truth for all stakeholders to share.
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