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.

Inhaltsverzeichnis

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

Über den Autor / die Autorin

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).

Produktdetails

• Autoren: Kellogg S, P Southekal, Prashanth Southekal, Prashanth (Esc Lille Southekal
• Verlag: Wiley, John and Sons Ltd
• Sprache: Englisch
• Produktform: Fester Einband
• Erschienen: 02.02.2023
• EAN: 9781394165230
• ISBN: 978-1-394-16523-0
• Seiten: 304
• Themen: Naturwissenschaften, Medizin, Informatik, Technik > Informatik, EDV > Informatik; Sozialwissenschaften, Recht,Wirtschaft > Wirtschaft > Einzelne Wirtschaftszweige, Branchen; Informatik, Datenanalyse, Künstliche Intelligenz, KI, AI, computer science, Datenqualität, Data Mining & Knowledge Discovery, Data Mining u. Knowledge Discovery