Executing data quality projects : ten steps to quality data and trusted information / created by Danette McGilvray

Includes bibliographical references and index

Introduction The Reason for This Book Intended Audiences Structure of This Book How to Use This Book Acknowledgements Chapter 1 Overview Impact of Information and Data Quality About the Methodology Approaches to Data Quality in Projects Engaging Management Chapter 2 Key Concepts Introduction Framework for Information Quality (FIQ) Information Life Cycle Data Quality Dimensions Business Impact Techniques Data Categories Data Specifications Data Governance and Stewardship The Information and Data Quality Improvement Cycle The Ten StepsT Process Best Practices and Guidelines Chapter 3 The Ten Steps 1. Define Business Need and Approach 2. Analyze Information Environment 3. Assess Data Quality 4. Assess Business Impact 5. Identify Root Causes 6. Develop Improvement Plans 7. Prevent Future Data Errors 8. Correct Current Data Errors 9. Implement Controls 10. Communicate Actions and Results Chapter 4 Structuring Your Project Projects and The Ten Steps Data Quality Project Roles Project Timing Chapter 5 Other Techniques and Tools Introduction Information Life Cycle Approaches Capture Data Analyze and Document Results Metrics Data Quality Tools The Ten Steps and Six Sigma Chapter 6 A Few Final Words Appendix Quick References Framework for Information Quality POSMAD Interaction Matrix Detail POSMAD Phases and Activities Data Quality Dimensions Business Impact Techniques The Ten StepsT Overview Definitions of Data Categories

Data quality problems cost businesses billions of dollars each year in unnecessary printing, postage, and staffing costs, in the steady erosion of an organization's credibility among customers and suppliers, and the inability to make sound decisions. Danette McGilvray presents a systematic, proven approach to improving data quality by combining a conceptual framework for understanding information quality with techniques and instructions for improving it. The Ten Step approach applies to all types of data and to all types of organizations. * Includes numerous templates, detailed examples, and practical advice for executing every step of The Ten Steps approach. * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from The Ten Step methodology, and other tools and information that is available online