Data Quality Techniques

Strategies for Continuous Data Improvement
1. Auflage 2026
ISBN: 978-1-3986-2802-1
Verlag: Kogan Page

Buch, Englisch, 392 Seiten, Format (B × H): 156 mm x 234 mm

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

Autoren/Hrsg.

Myers, Dan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Industrielle Qualitätskontrolle

Inhaltsverzeichnis

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

Über Autor(innen)

Myers, Dan
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.