Poor quality data leads to wasted resources, missed opportunities, regulatory risks, lost revenue and tons of times fixing, cleaning and manipulating data. Sorting data quality issues before they impact your business is key to ensuring the success of your organization.
Practical Data Quality Techniques is a practical guide to building up data quality initiatives that will help drive the long-term success of any organization. In this book, mid-career data professionals will learn how to use the Conformed Dimensions of Data Quality framework to define data quality, communicate expectations in a measurable way and track improvements. Including a high-level overview of the dimensions of data quality, the book dives into the key techniques that will help transform disorganized and poor-quality data from a liability into a strategic advantage.
This book highlights numerous key data quality management techniques. It will show the skills data professionals will need to validate data quality, as well as highlights methods for profiling data and techniques to improve consistency. Explaining the role of data governance in data quality projects, it also examines the future of data quality in the age of AI. Practical Data Quality Techniques features practical real-world examples from organizations in the IT, insurance and health care industries who are getting data quality right.
Improve your data and set your business up for success with this guide to identifying, managing and solving your data quality problems.
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
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.
