Data about data — labels and descriptions that tell you what a dataset contains, where it came from, how reliable it is, and how to use it correctly.
Data about data — labels and descriptions that tell you what a dataset contains, where it came from, how reliable it is, and how to use it correctly.
Metadata exists in three main types: technical metadata (schema, data types, storage format, size), business metadata (definitions, ownership, usage rules, sensitivity classifications), and operational metadata (creation date, modification history, access logs). In AI contexts, rich metadata is essential for dataset discovery, quality assessment, provenance tracking, and regulatory documentation. AI-specific metadata may include training recipe documentation, annotation guidelines, inter-annotator agreement scores, and model card fields. W3C SKOS is used to formalise vocabulary metadata; W3C PROV-O represents provenance metadata.
Well-managed metadata reduces the time needed to find, evaluate, and onboard datasets for AI projects — and provides the documentation required for regulatory audits and post-market monitoring.
Like the label on a medicine bottle — the ingredients list, dosage instructions, expiry date, and manufacturer information don't change what the medicine is, but are essential to using it safely and correctly.
Data about data — labels and descriptions that tell you what a dataset contains, where it came from, how reliable it is, and how to use it correctly.
Metadata exists in three main types: technical metadata (schema, data types, storage format, size), business metadata (definitions, ownership, usage rules, sensitivity classifications), and operational metadata (creation date, modification history, access logs). In AI contexts, rich metadata is essential for dataset discovery, quality assessment, provenance tracking, and regulatory documentation. AI-specific metadata may include training recipe documentation, annotation guidelines, inter-annotator agreement scores, and model card fields. W3C SKOS is used to formalise vocabulary metadata; W3C PROV-O represents provenance metadata.
Well-managed metadata reduces the time needed to find, evaluate, and onboard datasets for AI projects — and provides the documentation required for regulatory audits and post-market monitoring.