A pharmaceutical company's advanced analytics division is preparing for an external audit. To satisfy FDA traceability rules, the lead data scientist must show, for every engineered feature used in a production model, the original data source, transformation logic, version history, and accountable business owner. The organization structures data practices according to the DAMA-DMBOK framework. According to DAMA-DMBOK, which knowledge area should be primarily responsible for providing the processes and repositories that enable the team to capture and publish this end-to-end lineage information?
DAMA-DMBOK assigns responsibility for business glossaries, data catalogs, and lineage repositories to the Metadata Management knowledge area. Metadata Management maintains information about data definitions, origins, movement, and usage, allowing analysts and auditors to trace features back to raw sources. Data Quality Management focuses on fitness-for-use checks such as accuracy and completeness, Data Security Management protects confidentiality and integrity, and Data Architecture Management defines the overall structural blueprint of data assets. None of those knowledge areas is chiefly tasked with documenting or exposing lineage, so they cannot satisfy the traceability requirement as completely as Metadata Management.
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What is Metadata Management in the DAMA-DMBOK framework?
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Why is Data Quality Management not sufficient for lineage traceability?
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What types of repositories support lineage documentation in Metadata Management?