Enterprise Knowledge Management: The Data Quality Approach (The...

Features

• Cover Type: Paperback with 491 pages
• Published by: Morgan Kaufmann
• Edition: 1st Edition January 22, 2001
• Written in: English
• ISBN 10 Number: 0124558402
• ISBN 13 Number: 978-0124558403
• Book Dimensions: 9.2 x 7.3 x 1.3 inches
• Weighs: 2.4 pounds

Product Description
Today, companies capture and store awesome amounts of information about every aspect of their business: their customers, partners, vendors, markets, and more. But with the rise in the quantity of information has come a corresponding decrease in its quality--a problem businesses recognize and are working feverishly to solve.
Enterprise Knowledge Management: The Data Quality Approach presents an easily adaptable methodology for defining, measuring, and improving data quality. Author David Loshin begins by presenting an economic framework for understanding the value of data quality, then proceeds to outline data quality rules and domain-and mapping-based approaches to consolidating enterprise knowledge. Written for both a managerial and a technical audience, this book will be indispensable to the growing number of companies committed to wresting every possible advantage from their vast stores of business information.

Key Features
* Expert advice from a highly successful data quality consultant
* The only book on data quality offering the business acumen to appeal to managers and the technical expertise to appeal to IT professionals
* Details the high costs of terrible data and the options available to companies that want to transform mere data into true enterprise knowledge
* Presents conceptual and practical information complementing companies' interest in data warehousing, data mining, and knowledge discovery

Book Info
(Academic Press/Harcourt Science and Technology) A text presenting a flexible, yet precise methodology for defining, measuring, and improving data quality and managing business intelligence. Features a rigorous and methodical approach, instructions in real business terms, and documentation of the high cost of terrible data. Softcover.

Reader Reviews
Poor data quality has a profound effect on our everyday lives - consider the 2000 Presidential election and the Florida recount nightmare. Yet, the extent of poor data quality can be effectively measured and therefore, controlled, when we apply process management, technology, and good old common sense! "Bad data" has traditionally been masked in terms of curious anecdotes and curious stories that propagate through an organization. Yet, poor data quality has a serious effect on a company's bottom line, especially when bad data propagates out to the customer via incorrect billing, wrong delivery addresses, public relations nightmares, etc. In my experience consulting on data management projects, I noticed many patterns associated with data quality problems. In this book, I try to address both the management issues as well as the technical issues associated with the different kinds of problems, and I try to provide a framework for capturing the knowledge embedded in data quality rules and managing those rules as enterprise knowledge. I provide a breakdown of the dimensions of data quality, and delineate a framework for expressing data quality rules, measuring those rules, and assessing levels of data quality in a "Data Quality Scorecard." This scorecard can then be used as a benchmark and basis for a continuous information quality improvement program. In addition, we look at how understanding the business rules associated with the use of information throughout an enterprise can enhance the overall value of the enterprise knowledge asset. Integrating business rules in use across the organization is an important step in enhancing the enterprise knowledge resource, and we have found this to be a successful paradigm in knowledge management applications deployed with our customers. Data quality problems are widespread, menacing, and can cause serious operational and strategic problems in any organization. By reading my book, I hope to expose some of the critical issues associated with poor data quality and to demonstrate that by fixing the root of data quality problems, organizations can reduce costs due to error detection, correction, and rework, and increase profits by making strategic use of high quality information.