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:
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
About the Author :
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).
