Data quality remains a critical concern for organizations that rely on accurate information for decision-making. This volume from the Morgan Kaufmann Series in Data Management Systems focuses specifically on the accuracy dimension of data quality, offering a structured examination of how data errors occur and how they can be measured, managed, and minimized. The book presents a framework for understanding data accuracy, covering topics such as data entry errors, measurement errors, data integration inconsistencies, and the propagation of inaccuracies through information systems. It provides both theoretical foundations and practical guidance for professionals dealing with data quality issues in databases, data warehouses, and analytical environments.

The text is organized around a clear methodology for assessing and improving data accuracy, including techniques for error detection, correction, and prevention. The author draws on real-world examples and case studies to illustrate common challenges and effective solutions. Readers will find detailed discussions on statistical approaches to measuring accuracy, sampling methods for data quality audits, and strategies for designing systems that maintain data integrity over time. The book also addresses the role of metadata in documenting data quality and the importance of establishing data quality metrics within an organization.

Each chapter builds on the previous one, creating a coherent narrative that moves from problem identification to actionable remediation steps. The target audience includes data managers, database administrators, data analysts, information quality practitioners, and IT professionals who need to ensure the reliability of their data assets. The content assumes a basic familiarity with database concepts but does not require advanced statistical expertise, making it accessible to a broad range of readers. The writing style is technical yet clear, with mathematical formulations kept at a level that supports practical application without overwhelming the reader.

A companion website provides supplementary materials such as sample data sets and tools for implementing the techniques discussed in the book. This resource is particularly valuable for those who wish to apply the concepts directly in their own work environments. For organizations committed to improving their data governance practices, the accuracy dimension is often the first and most crucial aspect to address. The book fills a specific niche in the data management literature by dedicating its entire focus to accuracy rather than covering all dimensions superficially.

This depth allows readers to develop a thorough understanding of the sources of data inaccuracy, the trade-offs involved in different quality improvement strategies, and the ways to sustain quality gains over time. The Morgan Kaufmann Series reputation for high-quality technical publications is reflected in the careful editing and clear presentation of this work. Whether used as a reference handbook next to a data analyst’s desk or as a textbook in a graduate-level data management course, this volume delivers substantial value for anyone serious about data quality.