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References: Data Governance and Metadata Management

  1. Metadata - Wikipedia - Surveys the concept of "data about data" across descriptive and administrative categories, directly underpinning the chapter's three-category breakdown of technical, business, and operational metadata.

  2. Data governance - Wikipedia - Explains the organizational discipline of managing data availability, usability, and integrity, matching the chapter's framing of a governance framework's policy, roles, quality, security, and compliance pillars.

  3. Explainable artificial intelligence - Wikipedia - Covers techniques for making machine learning outputs interpretable to humans, supporting the chapter's discussion of graph-native explanations for clinical AI recommendations.

  4. DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition) - DAMA International - Technics Publications - DAMA International is credited with the "DAMA Wheel," the standard diagram placing data governance at the hub of eleven interlocking knowledge areas, a visual framework this chapter's five-pillar model directly echoes.

  5. Master Data Management: The Truth, the Whole Truth, and Nothing But the Truth - David Loshin - Morgan Kaufmann - Loshin is credited with formalizing the "golden record" concept and the registry, hybrid, and transaction-hub MDM patterns the chapter uses to explain how entity resolution produces one trusted patient record.

  6. DAMA-DMBOK: Data Management Body of Knowledge - DAMA International - The globally recognized professional framework this chapter's five-pillar governance model draws from, covering governance, metadata, and quality as interlocking knowledge areas.

  7. Understanding Data Governance - GeeksforGeeks - A practical introduction to data governance policies, roles, and processes that reinforces the chapter's worked example of onboarding a new wearable-device data source.

  8. What Is Data Lineage? - IBM - Explains how organizations track a data element's origin and transformations, directly supporting the chapter's distinction between data lineage, provenance, and traceability.

  9. Advancing Health Data and Metadata Standards - HealthIT.gov (Office of the National Coordinator) - Describes federal initiatives standardizing health data and metadata for interoperability, grounding the chapter's metadata management discussion in real healthcare data-exchange policy.

  10. Explainable AI - IBM - Defines explainable AI and contrasts model-agnostic interpretability techniques with the chapter's graph-native approach of explaining a recommendation through the actual path of nodes and edges traversed.