Formulate the reference architecture for data quality, in
Table of Contents
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
