Data Quality

Data Quality

by Prashanth Southekal
Released February 2023
Publisher(s): Wiley
ISBN: 9781394165230

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Book 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.

Table of contents

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

Product information

  • Title: Data Quality
  • Author(s): Prashanth Southekal
  • Release date: February 2023
  • Publisher(s): Wiley
  • ISBN: 9781394165230