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“Data Quality” books focus on the principles, methods, and tools for ensuring that data is accurate, complete, consistent, timely, and fit for purpose. This category spans measurement frameworks and metrics; data governance and stewardship; data profiling, cleansing, enrichment, and deduplication; metadata, lineage, and master/reference data management; and monitoring and observability across modern data pipelines. Titles balance technical practices with organizational processes, covering roles, policies, and compliance considerations, and often include case studies that show business impact on analytics, operations, and AI/ML. Aimed at data engineers, analysts, stewards, and decision-makers, these books provide practical guidance for building trustworthy data and a durable culture of quality.

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