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.

Chapter 1 Overview

If the state of quality of your company's products and services

was the same level of quality as the data in your databases,

would your company survive or go out of business?

-Larry English

A corollary: If the state of quality of your company's data was

the same level of quality as your company's products and

services, how much more profitable would your company be?

- Mehmet Orun

In This Chapter

The Impact of Information and Data Quality

About the Methodology: Concepts and Steps

Approaches to Data Quality in Projects

Engaging Management

Information quality problems and their impact are all around us: A customer does not receive an order because of incorrect shipping information; products are sold below cost because of wrong discount rates; a manufacturing line is stopped because parts were not ordered-the result of inaccurate inventory information; a well-known U.S. senator is stopped at an airport (twice) because his name is on a government "Do not fly" list; many communities cannot run an election with results that people trust; financial reform has created new legislation such as Sarbanes-Oxley.1

Information is not simply data, strings of numbers, lists of addresses, or test results stored in a computer. Information is the product of business processes and is continuously used and reused by them. However, it takes human beings to bring information to its real-world context and give it meaning. Every day human beings use information to make decisions, complete transactions, and carry out all the other activities that make a business run. Applications come and applications go, but the information in those applications lives on.

That's where information quality comes into play. Effective business decisions and actions can only be made when based on high-quality information-the key here being effective. Yes, business decisions are based all the time on poor-quality data, but effective business decisions cannot be made with flawed, incomplete, or misleading data. People need information they can trust to be correct and current if they are to do the work that furthers business goals and objectives.

A firm's basis for competition ... has changed from tangible products to intangible information. A firm's information represents the firm's collective knowledge used to produce and deliver products and services to consumers. Quality information is increasingly recognized as the most valuable asset of the firm. Firms are grappling with how to capitalize on information and knowledge. Companies are striving, more often silently, to remedy business impacts rooted in poor quality information and knowledge.

- Kuan-Tsae Huang, Yang W. Lee,
and Richard Y. Wang2

Tom Redman says it well:

The costs of poor quality are enormous. Some costs, such as added expense and lost customers, are relatively easy to spot, if the organization looks. We suggest (based on a small number of careful, but proprietary studies), as a working figure, that these costs are roughly 10 percent of revenue for a typical organization.... This figure does not include other costs, such as bad decisions and low morale, that are harder to measure but even more important.3

What is the cost to a company of the sales rep, publicly announced to have won the top sales award for the year along with the trip to Hawaii, only to have it rescinded a few days later because the sales data were wrong? Does the resulting embarrassment and low morale influence that sales rep's productivity

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 Steps(TM) 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 Steps(TM) Overview
Definitions of Data Categories