Executing Data Quality Projects
Ten Steps to Quality Data and Trusted Information (TM)


Language: Anglais
Cover of the book Executing Data Quality Projects

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325 p. · 21.6x27.9 cm · Paperback
Information is currency. In today’s world of instant global communication and rapidly changing trends, up-to-date and reliable information is essential to effective competition. 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 Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information, Danette McGilvray presents a systematic, proven approach to improving and creating data and information quality within the enterprise. She describes a methodology that combines a conceptual framework for understanding information quality with the tools, techniques, and instructions for improving and creating information quality. Her trademarked "Ten Steps" 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.
The Reason for This Book
Intended Audiences
Structure of This Book
How to Use This Book

Chapter 1 Overview
Impact of Information and Data Quality
About the Methodology
Approaches to Data Quality in Projects
Engaging Management

Chapter 2 Key Concepts
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 Steps™ 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
Information Life Cycle Approaches
Capture Data
Analyze and Document Results
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 Steps™ Overview
Definitions of Data Categories
Database analysts, data analysts, data administrators, data architects, enterprise architects, data warehouse engineers, business analysts, developers, DBAs, subject matter experts, data modelers, and data stewards and their managers.
Danette McGilvray is president and principle of Granite Falls Consulting, Inc., a firm specializing in information and data quality management to support key business processes around customer satisfaction, decision support, supply chain management, and operational excellence.