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Data Quality Techniques

Dan Myers
€71.99
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business analysts data quality
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Dan Myers data quality
data auditing techniques
data cleansing methods
data governance and quality
data governance frameworks
data governance professionals
data integrity management
data issue resolution processes
data management
data management best practices
data managers and analysts
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data ownership and accountability
data profiling techniques
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Data Quality Techniques by Dan Myers
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forthcoming
how to improve data quality
how to measure data quality
improving data accuracy and consistency
information quality
information quality management
Kogan Page data
master data management
master data quality
metadata management
mid career data professionals
practical data quality techniques
real world data quality case studies
root cause analysis data issues

Product details

  • ISBN 9781398628021
  • Dimensions: 156 x 234mm
  • Publication Date: 03 Jul 2026
  • Publisher: Kogan Page Ltd
  • Publication City/Country: GB
  • Product Form: Hardback
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Description

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
About Dan Myers
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.

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