Enterprise Knowledge Management: The Data Quality Approach: The Morgan Kaufmann Series in Data Management Systems
Autor David Loshinen Limba Engleză Hardback – 25 ian 2001
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 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 bad 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
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Specificații
ISBN-13: 9780124558403
ISBN-10: 0124558402
Pagini: 512
Dimensiuni: 178 x 235 x 29 mm
Greutate: 1.09 kg
Ediția:New.
Editura: ELSEVIER SCIENCE
Seria The Morgan Kaufmann Series in Data Management Systems
ISBN-10: 0124558402
Pagini: 512
Dimensiuni: 178 x 235 x 29 mm
Greutate: 1.09 kg
Ediția:New.
Editura: ELSEVIER SCIENCE
Seria The Morgan Kaufmann Series in Data Management Systems
Public țintă
Managers and IT staff concerned with data quality data warehousing and data mining.Cuprins
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