Data Quality: Empowering Businesses with Analytics and AI

Length: 304 pages
Edition: 1
Language: English
Publisher: Wiley
Publication Date: 2023-02-01
ISBN-10: 1394165234
ISBN-13: 9781394165230
Sales Rank: #503964 (See Top 100 Books)

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.

Cover
Title Page
Copyright
Foreword
Preface
    ABOUT THE BOOK
    QUALITY PRINCIPLES APPLIED IN THIS BOOK
    ORGANIZATION OF THE BOOK
    WHO SHOULD READ THIS BOOK?
    REFERENCES
Acknowledgments
PART I: Define Phase
    CHAPTER 1: Introduction
        INTRODUCTION
        DATA, ANALYTICS, AI, AND BUSINESS PERFORMANCE
        DATA AS A BUSINESS ASSET OR LIABILITY
        DATA GOVERNANCE, DATA MANAGEMENT, AND DATA QUALITY
        LEADERSHIP COMMITMENT TO DATA QUALITY
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 2: Business Data
        INTRODUCTION
        DATA IN BUSINESS
        TELEMETRY DATA
        PURPOSE OF DATA IN BUSINESS
        BUSINESS DATA VIEWS
        KEY CHARACTERISTICS OF BUSINESS DATA
        CRITICAL DATA ELEMENTS (CDEs)
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 3: Data Quality in Business
        INTRODUCTION
        DATA QUALITY DIMENSIONS
        CONTEXT IN DATA QUALITY
        CONSEQUENCES AND COSTS OF POOR DATA QUALITY
        DATA DEPRECIATION AND ITS FACTORS
        DATA IN IT SYSTEMS
        DATA QUALITY AND TRUSTED INFORMATION
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
PART II: Analyze Phase
    CHAPTER 4: Causes for Poor Data Quality
        INTRODUCTION
        DATA QUALITY RCA TECHNIQUES
        TYPICAL CAUSES OF POOR DATA QUALITY
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 5: Data Lifecycle and Lineage
        INTRODUCTION
        BUSINESS-ENABLED DLC STAGES
        IT BUSINESS-ENABLED DLC STAGES
        DATA LINEAGE
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 6: Profiling for Data Quality
        INTRODUCTION
        CRITERIA FOR DATA PROFILING
        DATA PROFILING TECHNIQUES FOR MEASURES OF CENTRALITY
        DATA PROFILING TECHNIQUES FOR MEASURES OF VARIATION
        INTEGRATING CENTRALITY AND VARIATION KPIs
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
PART III: Realize Phase
    CHAPTER 7: Reference Architecture for Data Quality
        INTRODUCTION
        OPTIONS TO REMEDIATE DATA QUALITY
        DataOps
        DATA PRODUCT
        DATA FABRIC AND DATA MESH
        DATA ENRICHMENT
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 8: Best Practices to Realize Data Quality
        INTRODUCTION
        OVERVIEW OF BEST PRACTICES
        BP 1: IDENTIFY THE BUSINESS KPIs AND THE OWNERSHIP OF THESE KPIs AND THE PERTINENT DATA
        BP 2: BUILD AND IMPROVE THE DATA CULTURE AND LITERACY IN THE ORGANIZATION
        BP 3: DEFINE THE CURRENT AND DESIRED STATE OF DATA QUALITY
        BP 4: FOLLOW THE MINIMALISTIC APPROACH TO DATA CAPTURE
        BP 5: SELECT AND DEFINE THE DATA ATTRIBUTES FOR DATA QUALITY
        BP 6: CAPTURE AND MANAGE CRITICAL DATA WITH DATA STANDARDS IN MDM SYSTEMS
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 9: Best Practices to Realize Data Quality
        INTRODUCTION
        BP 7: RATIONALIZE AND AUTOMATE THE INTEGRATION OF CRITICAL DATA ELEMENTS
        BP 8: DEFINE THE SoR AND SECURELY CAPTURE TRANSACTIONAL DATA IN THE SoR/OLTP SYSTEM
        BP 9: BUILD AND MANAGE ROBUST DATA INTEGRATION CAPABILITIES
        BP 10: DISTRIBUTE DATA SOURCING AND INSIGHT CONSUMPTION
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
PART IV: Sustain Phase
    CHAPTER 10: Data Governance
        INTRODUCTION
        DATA GOVERNANCE PRINCIPLES
        DATA GOVERNANCE DESIGN COMPONENTS
        IMPLEMENTING THE DATA GOVERNANCE PROGRAM
        DATA OBSERVABILITY
        DATA COMPLIANCE – ISO 27001, SOC1, AND SOC2
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 11: Protecting Data
        INTRODUCTION
        DATA CLASSIFICATION
        DATA SAFETY
        DATA SECURITY
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
    CHAPTER 12: Data Ethics
        INTRODUCTION
        DATA ETHICS
        IMPORTANCE OF DATA ETHICS
        PRINCIPLES OF DATA ETHICS
        MODEL DRIFT IN DATA ETHICS
        DATA PRIVACY
        MANAGING DATA ETHICALLY
        KEY TAKEAWAYS
        CONCLUSION
        REFERENCES
Appendix 1: Abbreviations and Acronyms
Appendix 2: Glossary
Appendix 3: Data Literacy Competencies
About the Author
Index
End User License Agreement