Journey to Data Quality (Hardcover)

Description

All organizations today confront data quality problems, both systemic and structural. Neither ad hoc approaches nor fixes at the systems level--installing the latest software or developing an expensive data warehouse--solve the basic problem of bad data quality practices. Journey to Data Quality offers a roadmap that can be used by practitioners, executives, and students for planning and implementing a viable data and information quality management program. This practical guide, based on rigorous research and informed by real-world examples, describes the challenges of data management and provides the principles, strategies, tools, and techniques necessary to meet them.

The authors, all leaders in the data quality field for many years, discuss how to make the economic case for data quality and the importance of getting an organization's leaders on board. They outline different approaches for assessing data, both subjectively (by users) and objectively (using sampling and other techniques). They describe real problems and solutions, including efforts to find the root causes of data quality problems at a healthcare organization and data quality initiatives taken by a large teaching hospital. They address setting company policy on data quality and, finally, they consider future challenges on the journey to data quality.

Yang W. Lee is Associate Professor at the Information, Operations, and Analysis Group in the College of Business Administration at Northeastern University.

Leo L. Pipino is Professor Emeritus of Management Information Systems at the University of Massachusetts, Lowell.

James D. Funk is Founder and Chief Information Architect at Beyond Accuracy, LLC.

Richard Y. Wang is Director of the MIT Information Quality Program (MITIQ), Codirector of the Total Data Quality Management Program at MIT (MIT TDQM), and University Professor at the University of Arkansas at Little Rock, where the first master's degree program in Information Quality has been established. 
 
 

Table of Contents

 Preface ix
 
 Acknowledgments xi
 
1. Introduction 1
 
2. Cost/Benefit Analysis 13
 
3. Assessing Data Quality, Part I 27
 
4. Assessing Data Quality, Part II 53
 
5. Sampling for Data Quality Assurance 67
 
6. Understanding the Anatomy of Data Quality Problems and Patterns 79
 
7. Identifying Root Causes of Data Quality Problems:
A Case of a Health Care Organization 109
 
8. Managing Information as Product 125
 
9. Developing Information Product Maps 137
 
10. Data Quality Initiatives:
A Case of a Large Teaching Hospital 151
 
11. Data Quality Policy 171
 
12 End of the Journey 201
 
 References 215
 
 Index 223