Data Quality Techniques

Reviews / Votes

"Dan Myers and I are both proponents of using data quality dimensions to measure and manage various characteristics of data. This book dives deep into those dimensions with definitions, underlying concepts, and how to measure them- yet it is practical and easy to read. It reflects Dan's years of experience and he shares details that will help any practitioner who is responsible for measuring and managing data quality. Learn from this book, put it to use, and keep it on your shelf for continued reference. You will be glad you did!" * Danette McGilvray, renowned data quality expert and author of Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (TM) * "As someone who works deeply in data and AI governance, I appreciate how this book positions data quality as a capability that depends on definitions, accountability, measurement, standards, processes, and ongoing management. The book offers a comprehensive view of data quality, from foundational concepts and dimensions to practical techniques such as profiling, validation, audits, and data contracts. It is a valuable guide for organizations that want to build trust in their data and sustain that trust over time." * George Firican, Founder, LightsOnData * "Dan Myers' book Data Quality Techniques is a well-written and much needed text for data professionals and students alike. All too often, data quality concepts are discussed assuming that the reader can easily understand and correctly apply them. However, in both my academic and professional practice I find this not to be the case. This is why I am especially impressed with how each data quality concept presented in this book is also illuminated with understandable, practical examples.
I am also impressed with the comprehensiveness and the level of detail in this book. While there have been many good books addressing various aspects of data quality, this book is noteworthy for its breadth of coverage. It goes far beyond basic dimensions, error types, and metrics to provide practical insights on data quality management methodologies, auditing methods, the intersection with data governance, the importance of data contracts, and many other topics critical to data quality management success.
The new age of artificial intelligence has brought a sharp focus on data quality. I highly recommend this book to anyone trying to understand how good data quality management practices can add value to an organization.

" * John Talburt, Professor of Information Science, University of Arkansas at Little Rock * "Data Quality Techniques provides an exceptionally well-founded yet highly practical approach to a topic that has become critical for modern organizations: data quality. It succeeds in presenting complex relationships surrounding data quality techniques in a clear and accessible way.
Particularly compelling is the book's clear three-part structure: from a solid introduction to data quality and a structured presentation of Conformed Dimensions and implementation techniques, to the detailed description of individual DQ dimensions, and finally to concrete methods for measuring and managing data quality. The precise differentiation between the various DQ dimensions, the references to underlying concepts, and the illustrative explanations supported by numerous examples make this book a valuable practical reference.
This book is for anyone who needs to work with data quality techniques and wants to help their organization establish a reliable and trustworthy data foundation.

" * Christiana Klingenberg, Executive Data Management Consultant, iDIGMA * "Data Quality Techniques is a standout contribution to modern data management, offering a clear and practical bridge between abstract data quality concepts and measurable, real-world implementation. With its structured approach, from definition to operationalization to quantification, it equips organizations to embed data quality as a governed, auditable capability. This work is highly recommended for data professionals seeking to move beyond theory and deliver tangible, enterprise-wide value." * Howard Diesel, CDO, Modelware Systems; DAMA South Africa Chapter President; and DMBOK v3 Editorial Board member *