Details

Data Quality

Empowering Businesses with Analytics and AI
1. Aufl.

von: Prashanth Southekal
25,99 €
Verlag:	Wiley
Format:	EPUB
Veröffentl.:	20.01.2023
ISBN/EAN:	9781394165247
Sprache:	englisch
Anzahl Seiten:	304

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Beschreibungen

Titelbeschreibung

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: <ul> <li>Profile for data quality, including the appropriate techniques, criteria, and KPIs </li> <li>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.</li> <li>Formulate the reference architecture for data quality, including practical design patterns for remediating data quality</li> <li>Implement the 10 best data quality practices and the required capabilities for improving operations, compliance, and decision-making capabilities in the business</li></ul>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 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

Autorenportrait

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).

Back cover copy

PRAISE FOR DATA QUALITY “Incredible! Get ready to be inspired, motivated, and to accelerate on improving your data quality landscape! Regardless of your industry, experience, or role, this book provides the invaluable guidance and framework to start tackling the most critical, common roadblocks encountered with data quality. Spanning the entire data lifecycle, the author has provided trusted, foundational tools with approaches for understandable, everyday business scenarios to demonstrate how organizations can realistically improve their data quality. Incorporating both business performance considerations and technical fundamental best practices together, this timely book is for anyone passionate about truly unlocking the value of their data, engaging and guiding both technical and nontechnical business leaders alike on a critical topic that we should all be passionate about.” —Alena Godin, Chief Data and Analytics Officer, Best Buy, Canada “You may have the best people, processes, technology, analytics, and AI practice in your organization. But if the underlying data that is used is poor in terms of quality, the outcome achieved will be poor. The quality of data driven business decision making/value creation is dependent on the quality of data used. I have been a data quality practitioner for over 30 years. Undoubtedly, this is one of the best books on data quality I have ever read. It provides comprehensive coverage of the end-to-end data quality lifecycle. What is impressive about this book is the language used is simple and understandable by anyone at all levels in an organization and without any technology jargon or tools. A definite book for those who want to be literate on data quality and for those who want to develop and execute a successful data quality program in their organization to derive business benefits.” —Ram Kumar, Chief Data and Analytics Officer, Cigna International Markets, Singapore “In his impeccably well-researched book, Data Quality: Empowering Businesses with Analytics and AI, Prashanth Southekal covers just about all the bases—tying data quality to business outcomes, and laying out the many dimensions of data quality, where data quality fits into the overall data and business lifecycle, and a variety of techniques and best practices for identifying and mitigating data quality issues. We’ll be seeing this book regularly on the desks of business and data executives and practitioners.” —Douglas Laney, Innovation Fellow, Data and Analytics Strategy, West Monroe, USA “The problem is known: leveraging the value of available data … but how? Those promising great insights from analytics often start where this book ends: good data quality. Prashanth Southekal takes up the challenge of addressing what is needed to achieve this. Ensuring good data quality at the source is the only viable approach, knowing that fixing bad data (if possible, at all) is way more expensive and cumbersome. Introducing the Define-Assess-Realize-Sustain Model, the book offers the reader a very structured approach in finding the right, fit-to-purpose, level of data quality and sustaining it. It achieves this by describing the main root causes for poor data quality, offering techniques to identify them, and sharing best practices in data capturing and organization around data governance. This book is a valuable read for every organization that is willing to make the effort to unleash the endless opportunities good data quality entails.” —Astrid von Perbandt, Vice President Group Controlling, LPKF Laser & Electronics AG, Germany