Data Quality

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.

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