Data Quality Techniques: Strategies for Continuous Data Improvement

About the Book

Equip yourself with proven techniques to turn poor-quality data from a costly liability into a measurable advantage.

Data Quality Techniques is a hands-on guide for mid-career data professionals who need to transform data into a reliable, strategic asset. Designed around the Conformed Dimensions of Data Quality framework, this book shows how to define and measure data quality and communicate expectations in ways that drive real business impact.

With clear definitions and actionable tools, you'll learn how to:
- Improve data consistency and accuracy
- Uncover hidden data quality issues
- Apply data governance principles to data quality projects
- Anticipate the role of AI in shaping the future of data quality

Packed with worked examples, Data Quality Techniques gives you the frameworks and tools to improve your data so that it supports growth, compliance and smarter decision making.

Themes include: data quality management, data governance, data consistency, AI in data, data profiling, data strategy, data management techniques

Table of Contents:

• Section - ONE: Introduction;
• • Chapter - 01: Why Data Quality Is Important;
  • Chapter - 02: About the Dimensions of Data Quality;
  • Chapter - 03: Industry Alignment of the Dimensions of Data Quality;
  • Chapter - 04: Programs that Support Data Quality;
• Section - TWO: Conformed Dimensions;
• • Chapter - 05: Introduction to Data Quality Measurement using the Conformed Dimensions;
  • Chapter - 06: Completeness;
  • Chapter - 07: Accuracy;
  • Chapter - 08: Precision;
  • Chapter - 09: Consistency;
  • Chapter - 10: Validity;
  • Chapter - 11: Timeliness, Currency and Accessibility;
  • Chapter - 12: Integrity;
  • Chapter - 13: Lineage;
  • Chapter - 14: Representation;
• Section - THREE: Techniques to Manage Data Quality;
• • Chapter - 15: Introduction to Techniques to Manage Data Quality;
  • Chapter - 16: Choosing Your Approach;
  • Chapter - 17: Validation Techniques;
  • Chapter - 18: Completeness and Consistency Techniques;
  • Chapter - 19: Data Profiling Techniques;
  • Chapter - 20: Human Directed Audited Techniques;
  • Chapter - 21: Survey Techniques;
  • Chapter - 22: Data Contracts;
  • Chapter - 23: Appendix

About the Author :
Dan Myers is an experienced data management leader and the Principal of DQMatters. His work focuses on helping large enterprises develop successful data quality initiatives and he has worked with many organizations including Farmers Insurance, Rio Tinto, Apple, and Google. His data quality framework, the Conformed Dimensions of Data Quality, has been adopted by numerous organizations. He is the former president of the International Association for Information and Data Quality and is based in San Jose, CA.

Review :
"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!" "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." "

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.

" "

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.

" "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." "I wholeheartedly recommend Data Quality Techniques. In his explanation of the Conformed Dimensions, Dan Myers provides the "ABC Company" data model, allowing readers to learn data profiling using actual data, along with the SQL structure for the "Open Data Quality Repository" to store the results. By delving deeply into specific measurement techniques, he brilliantly bridges the gap between data quality theory and the realities of practice. It is a must-read for all practitioners taking on the challenge of data quality improvement."