Journey to Data Quality
A guide for assessing an organization's data quality practice and a roadmap for implementing a viable data and information quality management program, based on rigorous research and drawing on real-world examples.
All organizations today confront data quality problems, both systemic and structural. Neither ad hoc approaches nor fixes at the systems leve—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.
HardcoverOut of Print ISBN: 9780262122870 240 pp. | 6 in x 9 in 41 illus.
Paperback$25.00 X ISBN: 9780262513357 240 pp. | 6 in x 9 in 41 illus.
The issue of data quality has become increasingly important over the last decade as the amount of data being collected and stored continues to increase at a rapid rate. These researchers have been at the forefront of understanding the impact and implication of the quality of data on organizations. These issues will continue to grow in importance as nontraditional forms of data are collected.
Veda C. Storey
Tull Professor of Computer Information Systems, Georgia State University