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References: Metadata Management and Data Governance

  1. Metadata - Wikipedia - Defines metadata and its three main categories (descriptive, structural, administrative), providing the conceptual framework for this chapter's treatment of technical, business, and operational metadata types.

  2. Data Quality - Wikipedia - Covers the dimensions of data quality including completeness, accuracy, consistency, and timeliness, directly matching this chapter's five-dimension framework used to evaluate enterprise datasets for LLM trustworthiness.

  3. Data Steward - Wikipedia - Explains the data steward role, its relationship to data ownership, and its place in governance frameworks — directly supporting this chapter's section on stewardship roles and the governance accountability chain.

  4. Fundamentals of Data Engineering - Joe Reis, Matt Housley - O'Reilly Media - Chapter 9 covers data governance, metadata management, and data quality frameworks, providing hands-on guidance that complements this chapter's treatment of active metadata management and governance roles.

  5. Designing Data-Intensive Applications - Martin Kleppmann - O'Reilly Media - Chapter 11 covers data pipelines and stream processing; the book's treatment of schema evolution and operational metadata directly supports this chapter's concepts of timeliness and active metadata discovery.

  6. Differential Privacy - Wikipedia - Provides the mathematical definition of differential privacy, the epsilon privacy budget, and the Laplace noise mechanism — foundational background for this chapter's section on formal privacy guarantees for aggregate queries.

  7. Access Control - Wikipedia - Explains access control models (DAC, MAC, RBAC) and their application to data systems, supporting this chapter's discussion of how classification tiers drive access control policy enforcement in context graphs.

  8. Data Governance - Wikipedia - Covers governance frameworks, stewardship structures, and policy enforcement mechanisms, providing the organizational context for the data governance framework introduced in this chapter.

  9. NIST Artificial Intelligence Resources - NIST - Provides the NIST AI Risk Management Framework covering trustworthy AI requirements including data quality, governance, and accountability — relevant to this chapter's argument that metadata governance is prerequisite for reliable enterprise AI.

  10. Data Provenance - Wikipedia - Explains provenance tracking concepts including lineage, audit trails, and attribution — foundational for this chapter's discussion of how operational metadata enables LLMs to verify data freshness and trustworthiness before acting on retrieved facts.