- Provides mid-career data professionals with repeatable and measurable techniques for improving data quality
- Shows how the Conformed Dimensions of Data Quality framework can be used in any organization
- Explains how to reduce costs, improve efficiency and ensure regulatory compliance
- Demonstrates how data can be transformed from a liability into a key strategic advantage
- Includes real-world examples from HR, IT, healthcare and financial services that show how organization have improved their data quality
Data Quality Techniques
Strategies for Continuous Data Improvement
Transform your data and set your business up for success with this guide to improving your data quality.
Key features at a glance
EAN: 9781398628007
Edition: 1
Published:
Format: 234x156
320 pages
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.
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