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