dattribution

Dattribution is a term used in data governance and information management to denote the process of attributing data to its sources and origins. It encompasses documenting provenance, origin, authors, licenses, and usage rights of data across the data lifecycle. The goal is to create a transparent record that allows users to assess trust, quality, and legal compliance.

Core elements include provenance and lineage (where data came from and how it was transformed), metadata (descriptive information about data), licensing and usage rights, versioning and change history, and data quality indicators.

Applications of dattribution include supporting research reproducibility, regulatory compliance, auditing and accountability, risk management, and the responsible use of data in AI and machine learning where training data provenance matters.

Approaches and standards involve metadata schemas such as the W3C PROV and PROV-O, FAIR data principles, data catalogs and data lineage tools, and automated instrumentation in data pipelines. In some contexts, immutable provenance may be pursued using distributed ledgers or other tamper-evident technologies.

Challenges associated with dattribution include protecting privacy and confidentiality, integrating data from heterogeneous sources, scaling to large datasets, evolving ownership and licensing terms, and the difficulty of attributing provenance for aggregated or transformed data.

See also: data provenance, data lineage, data governance.