Additional information
by Daniel Myers (Author)
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
Author Biography
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.
Number of Pages: 432
Dimensions: 0.88 x 9.21 x 6.14 IN
Publication Date: July 28, 2026
