Over David Loshin
David Loshin is directeur van Knowledge Integrity Inc.
David Loshin
Morgan Kaufmann Publishers
1e druk, 2010
9780123737175
The Practitioner's Guide to Data Quality Improvement
Specificaties
Ingenaaid, 398 blz.
|
Engels
Morgan Kaufmann Publishers |
1e druk, 2010
ISBN13: 9780123737175
Rubricering
Verwachte levertijd ongeveer 9 werkdagen
Gratis verzonden
Samenvatting
De kwaliteit van uw bedrijfsinformatie is bepalend voor het succes van uw organisatie. Dit naslagwerk biedt handvatten om uw data te verbeteren en te analyseren.
Specificaties
ISBN13:9780123737175
Trefwoorden:business intelligence, Data analyse, business analyse
Taal:Engels
Bindwijze:ingenaaid
Aantal pagina's:398
Uitgever:Morgan Kaufmann Publishers
Druk:1
Verschijningsdatum:22-11-2010
Hoofdrubriek:Organisatiekunde
Inhoudsopgave
Preface
Chapter 1: Business Impacts of Poor Data Quality
Chapter 2: The Organizational Data Quality Program
Chapter 3: Data Quality Maturity
Chapter 4: Enterprise Initiative Integration
Chapter 5: Developing a Business Case and a Data Quality Roadmap
Chapter 6: Metrics and Performance Improvement
Chapter 7: Data Governance
Chapter 8: Dimensions of Data Quality
Chapter 9: Data Requirement Analysis
Chapter 10: Metadata and Data Standard
Chapter 11: Data Quality Assessment
Chapter 12: Remediation and Improvement Planning
Chapter 13: Data Quality Service Level Agreement
Chapter 14: Data Profiling
Chapter 15: Parsing and Standardization
Chapter 16: Entity Identity Resolution
Chapter 17: Inspection, Monitoring, Auditing, and Tracking
Chapter 18: Data Enhancement
Chapter 19: Master Data Management and Data Quality
Chapter 20: Bringing It All Together
Chapter 1: Business Impacts of Poor Data Quality
Chapter 2: The Organizational Data Quality Program
Chapter 3: Data Quality Maturity
Chapter 4: Enterprise Initiative Integration
Chapter 5: Developing a Business Case and a Data Quality Roadmap
Chapter 6: Metrics and Performance Improvement
Chapter 7: Data Governance
Chapter 8: Dimensions of Data Quality
Chapter 9: Data Requirement Analysis
Chapter 10: Metadata and Data Standard
Chapter 11: Data Quality Assessment
Chapter 12: Remediation and Improvement Planning
Chapter 13: Data Quality Service Level Agreement
Chapter 14: Data Profiling
Chapter 15: Parsing and Standardization
Chapter 16: Entity Identity Resolution
Chapter 17: Inspection, Monitoring, Auditing, and Tracking
Chapter 18: Data Enhancement
Chapter 19: Master Data Management and Data Quality
Chapter 20: Bringing It All Together
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