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Data Quality Techniques

Strategies for Continuous Data Improvement

Dan Myers

Transform your data and set your business up for success with this guide to improving your data quality.

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EAN: 9781398628007

Edition: 1

Published: 03 Jul 2026

Format: 234 x 156

320 pages

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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, industry examples 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 real-world examples from IT, insurance and healthcare, 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

About the authors

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, Apple and Rio Tinto. 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.

Dan Myers

Section - ONE: Introduction;
- Chapter - 01: Why data quality matters;
- Chapter - 02: Communication data requirements with the dimensions of data quality;
- Chapter - 03: Trends in the adoption of the dimensions of data quality;
Section - TWO: Techniques to manage and improve data quality;
- Chapter - 04: Introduction to techniques for management and improvements;
- Chapter - 05: Top-down and bottom-up approaches;
- Chapter - 06: Validating your data quality;
- Chapter - 07: Completeness and consistency techniques;
- Chapter - 08: Methods of profiling data;
- Chapter - 09: Human-directed auditing;
- Chapter - 10: Conducting objective and subjective surveys;
- Chapter - 11: Data contracts and the role of data governance;
Section - THREE: Conformed dimensions – a standard set of dimensions of depth;
- Chapter - 12: Completeness – where to start when you don’t have the data;
- Chapter - 13: Accuracy – when your data isn’t correct;
- Chapter - 14: Precision- how granular does your data need to be;
- Chapter - 15: Consistency – comparing your data to other sources;
- Chapter - 16: Validity – when data isn’t a valid combination;
- Chapter - 17: Timeliness, currency and accessibility – which measure to use;
- Chapter - 18: Integrity – ensuring correct connectivity;
- Chapter - 19: Lineage – building confidence;
- Chapter - 20: Representation – provide more context;
Section - FOUR: Preparing for the future of data quality;
- Chapter - 21: Current versus future improvements with AI;
- Chapter - 22: Improved transparency and privacy with blockchain;