Enterprise Knowledge Management: The Data Quality Approach

Today, companies capture and store tremendous 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.

Expert advice from a highly successful data quality consultantThe only book on data quality offering the business acumen to appeal to managers and the technical expertise to appeal to IT professionalsDetails the high costs of bad data and the options available to companies that want to transform mere data into true enterprise knowledgePresents conceptual and practical information complementing companies' interest in data warehousing, data mining, and knowledge discovery

1. Introduction 
2. Who Owns Information?
3. Data Quality in Practice
4. Economic Framework of Data Quality and the Value Proposition
5. Dimensions of Data Quality
6. Statistical Process Control and the Improvement Cycle 
7. Domains, Mappings, and Enterprise Reference Data
8. Data Quality Assertions and Business Rules
9. Measurement and Current State Assessment
10. Data Quality Requirements
11. Metadata, Guidelines, and Policy
12. Rule-Based Data Quality
13. Metadata and Rule Discovery
14. Data Cleansing
15. Root Cause Analysis and Supplier Management
16. Data Enrichment/Enhancement
17. Data Quality and Business Rules in Practice
18. Building the Data Quality Practice