Book description
Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her “Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—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 available online.
Table of contents
- Cover
- In Praise of Executing Data Quality Projects
- Title Page
- Copyright
- Dedication
- ACKNOWLEDGMENTS
- INTRODUCTION
- CHAPTER 1 Overview
-
CHAPTER 2 Key Concepts
- Introduction
- The Framework for Information Quality
- The Information Life Cycle
- Data Quality Dimensions
- Business Impact Techniques
- Data Categories
- Data Specifi cations
- Data Governance and Data Stewardship
- The Information and Data Quality Improvement Cycle
- The Ten Steps Process
- Best Practices and Guidelines
-
CHAPTER 3 The Ten Steps Process
- Introduction
- Step 1 Define Business Need and Approach
-
Step 2 Analyze Information Environment
- Introduction
- Step 2.1 Understand Relevant Requirements
- Step 2.2 Understand Relevant Data and Specifications
- Step 2.3 Understand Relevant Technology
- Step 2.4 Understand Relevant Processes
- Step 2.5 Understand Relevant People/Organizations
- Step 2.6 Define the Information Life Cycle
- Step 2.7 Design Data Capture and Assessment Plan
-
Step 3 Assess Data Quality
- Introduction
- Step 3.1 Data Specifications
- Step 3.2 Data Integrity Fundamentals
- Step 3.3 Duplication
- Step 3.4 Accuracy
- Step 3.5 Consistency and Synchronization
- Step 3.6 Timeliness and Availability
- Step 3.7 Ease of Use and Maintainability
- Step 3.8 Data Coverage
- Step 3.9 Presentation Quality
- Step 3.10 Perception, Relevance, and Trust
- Step 3.11 Data Decay
- Step 3.12 Transactability
- Step 4 Assess Business Impact
- Step 5 Identify Root Causes
- Step 6 Develop Improvement Plans
- Step 7 Prevent Future Data Errors
- Step 8 Correct Current Data Errors
- Step 9 Implement Controls
- Step 10 Communicate Actions and Results
- The Ten Steps Process Summary
- CHAPTER 4 Structuring Your Project
- CHAPTER 5 Other Techniques and Tools
- CHAPTER 6 A Few Final Words
- APPENDIX Quick References
- GLOSSARY
- BIBLIOGRAPHY
- LIST OF FIGURES, TABLES, AND TEMPLATES
- INDEX
- ABOUT THE AUTHOR
- Instructions for online access
Product information
- Title: Executing Data Quality Projects
- Author(s):
- Release date: September 2008
- Publisher(s): Morgan Kaufmann
- ISBN: 9780080558394
You might also like
video
Meet the Expert: Jennifer Yang on Applying Machine Learning Techniques for Data Quality Management
Traditional rule-based data quality management methodology is costly and hard to scale in the big data …
book
Data Driven Business Transformation
OPTIMIZE YOUR BUSINESS DATA FOR FIRST-CLASS RESULTS Data Driven Business Transformation illustrates how to find the …
book
Database Archiving
With the amount of data a business accumulates now doubling every 12 to 18 months, IT …
book
Common Information Models for an Open, Analytical, and Agile World
Maximize the Value of Your Information Throughout Even the Most Complex IT Project Foreword by Tim …