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Building a Data Catalogue: Making Data Discoverable Across Your Organisation

Data analysts spend 30% of their time searching for data. A well-implemented data catalogue eliminates this waste and accelerates time to insight.

The Discovery Problem

As data volumes grow, analysts struggle to find the right dataset for their analysis. Without a catalogue, they resort to emailing colleagues, duplicating datasets, and working with outdated versions.

What a Data Catalogue Contains

  • Business Metadata: Dataset descriptions, ownership, and business use cases in plain language.
  • Technical Metadata: Schema, data types, partition keys, and storage location.
  • Operational Metadata: Last updated time, row count, data quality score, and freshness SLA.
  • Lineage: Where did this data come from, and where does it flow downstream?

Apache Atlas, DataHub (LinkedIn), Amundsen (Lyft), Alation, and Collibra are leading catalogue solutions across open-source and enterprise categories.