Data Quality Empowering Businesses with Analytics and AI

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.

PRASHANTH SOUTHEKAL, PHD, is a data, analytics, and AI consultant, author, and professor. He has worked and consulted for over 80 organizations including P&G, GE, Shell, Apple, FedEx, and SAP. Dr. Southekal is the author of Data for Business Performance and Analytics Best Practices (ranked #1 analytics books of all time by BookAuthority) and writes regularly on data, analytics, and AI in Forbes and CFO.University. He serves on the Editorial Board of MIT CDOIQ Symposium and is an advisory board member at BGV (Benhamou Global Ventures) a Silicon Valley-based venture capital firm. Apart from his consulting and advisory pursuits, he has trained over 3,000 professionals worldwide in data and analytics. Dr. Southekal is also an adjunct professor of data and analytics at IE Business School (Madrid, Spain). CDO Magazine included him in the top 75 global academic data leaders of 2022. He holds a PhD from ESC Lille (FR), an MBA from the Kellogg School of Management (US), and holds the ICD.D designation from the Institute of Corporate Directors (Canada).

Foreword

by Bill Inmon

Preface

About the Book

Quality Principles Applied in This Book

Organization of the Book

Who Should Read This Book?

References

Acknowledgments

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 (CDE)

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

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

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: 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

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 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

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