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.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Technology
Zielgruppe
Für Beruf und Forschung
Managers and IT staff concerned with data quality data warehousing and data mining.
Maße
Höhe: 235 mm
Breite: 178 mm
Gewicht
ISBN-13
978-0-12-455840-3 (9780124558403)
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Schweitzer Klassifikation
David Loshin is President of Knowledge Integrity, Inc., a company specializing in data management consulting. The author of numerous books on performance computing and data management, including "Master Data Management" (2008) and "Business Intelligence - The Savvy Manager's Guide" (2003), and creator of courses and tutorials on all facets of data management best practices, David is often looked to for thought leadership in the information management industry.
Autor*in
President, Knowledge Integrity Incorporated, Silver Spring, MD, USA
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
