- ホーム
- > 洋書
- > 英文書
- > Computer / Databases
Full Description
Discover how to achieve business goals by relying on high-quality, robust data
In Data Quality: Empowering Businesses with Analytics and AI, veteran data and analytics professional delivers a practical and hands-on discussion on how to accelerate business results using high-quality data. In the book, you'll learn techniques to define and assess data quality, discover how to ensure that your firm's data collection practices avoid common pitfalls and deficiencies, improve the level of data quality in the business, and guarantee that the resulting data is useful for powering high-level analytics and AI applications.
The author shows you how to:
Profile for data quality, including the appropriate techniques, criteria, and KPIs
Identify the root causes of data quality issues in the business apart from discussing the 16 common root causes that degrade data quality in the organization.
Formulate the reference architecture for data quality, including practical design patterns for remediating data quality
Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business
An essential resource for data scientists, data analysts, business intelligence professionals, chief technology and data officers, and anyone else with a stake in collecting and using high-quality data, Data Quality: Empowering Businesses with Analytics and AI will also earn a place on the bookshelves of business leaders interested in learning more about what sets robust data apart from the rest.
Contents
Foreword xvii
Preface xix
About the Book xix
Quality Principles Applied in This Book xx
Organization of the Book xxi
Who Should Read This Book? xxiii
References xxiii
Acknowledgments xxv
PART I: DEFINE PHASE 1
Chapter 1: Introduction 3
Chapter 2: Business Data 17
Chapter 3: Data Quality in Business 37
PART II: ANALYZE PHASE 63
Chapter 4: Causes for Poor Data Quality 65
Chapter 5: Data Lifecycle and Lineage 81
Chapter 6: Profiling for Data Quality 93
PART III: REALIZE PHASE 113
Chapter 7: Reference Architecture for Data Quality 115
Chapter 8: Best Practices to Realize Data Quality 133
Chapter 9: Best Practices to Realize Data Quality 161
PART IV: SUSTAIN PHASE 191
Chapter 10: Data Governance 193
Chapter 11: Protecting Data 211
Appendix 1: Abbreviations and Acronyms 237
Appendix 2: Glossary 241
Appendix 3: Data Literacy Competencies 245
About the Author 249
Index 251
