A searchable directory of all the data an organisation has — describing what each dataset contains, where it lives, how good it is, and who owns it.
A searchable directory of all the data an organisation has — describing what each dataset contains, where it lives, how good it is, and who owns it.
A data catalog aggregates metadata from source systems, data warehouses, data lakes, and external datasets to create a unified, searchable view of available data assets. Modern catalogs include active metadata (usage statistics, quality scores, lineage), social features (reviews, ratings, questions), and governance workflows (certification, access requests). For AI teams, a catalog dramatically reduces the time spent finding and evaluating suitable training data. AI-aware catalogs may include specific fields for annotation method, intended ML use, and associated model cards.
Data catalogs reduce the 'data archaeology' time that AI teams spend locating and evaluating datasets, shortening time-to-model while improving data quality through better-informed data selection.
Like a library catalogue — rather than searching through every shelf to find a book on a topic, the catalogue tells you what is available, where it is, and whether it is in good condition.
A searchable directory of all the data an organisation has — describing what each dataset contains, where it lives, how good it is, and who owns it.
A data catalog aggregates metadata from source systems, data warehouses, data lakes, and external datasets to create a unified, searchable view of available data assets. Modern catalogs include active metadata (usage statistics, quality scores, lineage), social features (reviews, ratings, questions), and governance workflows (certification, access requests). For AI teams, a catalog dramatically reduces the time spent finding and evaluating suitable training data. AI-aware catalogs may include specific fields for annotation method, intended ML use, and associated model cards.
Data catalogs reduce the 'data archaeology' time that AI teams spend locating and evaluating datasets, shortening time-to-model while improving data quality through better-informed data selection.