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Data Quality: Empowering Businesses with Analytics and AI [Hardback]

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(ESC Lille, France; Kellogg School of Management, USA)
  • Formāts: Hardback, 304 pages, height x width x depth: 231x160x28 mm, weight: 476 g
  • Izdošanas datums: 02-Feb-2023
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1394165234
  • ISBN-13: 9781394165230
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Data Quality: Empowering Businesses with Analytics and AIPalielināt
  • Formāts: Hardback, 304 pages, height x width x depth: 231x160x28 mm, weight: 476 g
  • Izdošanas datums: 02-Feb-2023
  • Izdevniecība: John Wiley & Sons Inc
  • ISBN-10: 1394165234
  • ISBN-13: 9781394165230
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781394165230.html
Keywords:
"Quality data is the key for business enterprises to offer improved performance in operations, compliance, and decision making. According to McKinsey, data driven organizations provide EBITDA increases between 15 to 25% than peers. However, to be a data driven organization, data quality is very important. But most companies are plagued with poor data quality. A HBR study found that just 3% of the data in a business enterprise meets quality standards. According to Gartner, 27% of data in the world's top companies is flawed--so companies are looking for practical guidance to improve data quality. This book examines the four-phase DARS approach (Define-Assess-Realize-Sustain) for companies to manage high quality data in organizations. This approach providesa combination of strategy and tactical elements to deliver the greatest value from data to the business. It is a playbook that offers prescriptive recommendations based on proven best practices to realize and sustain data quality"--

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.

Foreword xvii
Preface xix
About the Book xix
Quality Principles Applied in This Book xx
Organization of the Book xxi
Who Should Read This Book? xxiii
References xxiii
Acknowledgments xxv
PART I DEFINE PHASE
1(62)
Chapter 1 Introduction
3(14)
Introduction
3(2)
Data, Analytics, Al, and Business Performance
5(1)
Data as a Business Asset or Liability
6(1)
Data Governance, Data Management, and Data Quality
7(3)
Leadership Commitment to Data Quality
10(2)
Key Takeaways
12(1)
Conclusion
13(1)
References
13(4)
Chapter 2 Business Data
17(20)
Introduction
17(1)
Data in Business
18(3)
Telemetry Data
21(1)
Purpose of Data in Business
22(2)
Business Data Views
24(7)
Key Characteristics of Business Data
31(1)
Critical Data Elements (CDEs)
32(2)
Key Takeaways
34(1)
Conclusion
35(1)
References
35(2)
Chapter 3 Data Quality in Business
37(26)
Introduction
37(2)
Data Quality Dimensions
39(12)
Context in Data Quality
51(1)
Consequences and Costs of Poor Data Quality
52(2)
Data Depreciation and Its Factors
54(2)
Data in IT Systems
56(3)
Data Quality and Trusted Information
59(1)
Key Takeaways
60(1)
Conclusion
61(1)
References
62(1)
PART II ANALYZE PHASE
63(50)
Chapter 4 Causes for Poor Data Quality
65(16)
Introduction
65(1)
Data Quality RCA Techniques
66(5)
Typical Causes of Poor Data Quality
71(7)
Key Takeaways
78(1)
Conclusion
79(1)
References
80(1)
Chapter 5 Data Lifecycle and Lineage
81(12)
Introduction
81(1)
Business-Enabled DLC Stages
82(4)
IT Business-Enabled DLC Stages
86(2)
Data Lineage
88(2)
Key Takeaways
90(1)
Conclusion
90(1)
References
91(2)
Chapter 6 Profiling for Data Quality
93(20)
Introduction
93(2)
Criteria for Data Profiling
95(3)
Data Profiling Techniques for Measures of Centrality
98(2)
Data Profiling Techniques for Measures of Variation
100(9)
Integrating Centrality and Variation KPIs
109(3)
Key Takeaways
112(1)
Conclusion
112(1)
References
112(1)
PART III REALIZE PHASE
113(78)
Chapter 7 Reference Architecture for Data Quality
115(18)
Introduction
115(1)
Options to Remediate Data Quality
116(2)
DataOps
118(2)
Data Product
120(3)
Data Fabric and Data Mesh
123(3)
Data Enrichment
126(5)
Key Takeaways
131(1)
Conclusion
132(1)
References
132(1)
Chapter 8 Best Practices to Realize Data Quality
133(28)
Introduction
133(1)
Overview of Best Practices
134(2)
BP 1 Identify the Business KPIs and the Ownership of These KPIs and the Pertinent Data
136(2)
BP 2 Build and Improve the Data Culture and Literacy in the Organization
138(4)
BP 3 Define the Current and Desired State of Data Quality
142(3)
BP 4 Follow the Minimalistic Approach to Data Capture
145(3)
BP 5 Select and Define the Data Attributes for Data Quality
148(4)
BP 6 Capture and Manage Critical Data with Data Standards in MDM Systems
152(3)
Key Takeaways
155(3)
Conclusion
158(1)
References
158(3)
Chapter 9 Best Practices to Realize Data Quality
161(30)
Introduction
161(1)
BP 7 Rationalize and Automate the Integration of Critical Data Elements
162(6)
BP 8 Define the SoR and Securely Capture Transactional Data in the SoR/OLTP System
168(5)
BP 9 Build and Manage Robust Data Integration Capabilities
173(8)
BP 10 Distribute Data Sourcing and Insight Consumption
181(5)
Key Takeaways
186(3)
Conclusion
189(1)
References
189(2)
PART IV SUSTAIN PHASE
191(46)
Chapter 10 Data Governance
193(18)
Introduction
193(2)
Data Governance Principles
195(2)
Data Governance Design Components
197(5)
Implementing the Data Governance Program
202(1)
Data Observability
203(2)
Data Compliance -- ISO 27001, SOC1, and SOC2
205(1)
Key Takeaways
206(2)
Conclusion
208(1)
References
209(2)
Chapter 11 Protecting Data
211(12)
Introduction
211(1)
Data Classification
212(4)
Data Safety
216(2)
Data Security
218(2)
Key Takeaways
220(1)
Conclusion
220(1)
References
221(2)
Chapter 12 Data Ethics
223(14)
Introduction
223(1)
Data Ethics
224(1)
Importance of Data Ethics
224(1)
Principles of Data Ethics
225(1)
Model Drift in Data Ethics
226(2)
Data Privacy
228(2)
Managing Data Ethically
230(5)
Key Takeaways
235(1)
Conclusion
235(1)
References
236(1)
Appendix 1 Abbreviations and Acronyms 237(4)
Appendix 2 Glossary 241(4)
Appendix 3 Data Literacy Competencies 245(4)
About the Author 249(2)
Index 251
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).