Data Makes the World Go 'Round
Description
A comprehensive and detailed guide for business and technology leaders ready to implement AI throughout their organizations
A soup to nuts strategy guide for business leaders interested in implementing artificial intelligence in their organizations in a way that drives real-world results, Data Makes the World Go 'Round: The Data, Tech, and Trust Behind AI Success combines specific, actionable advice for technical and business leaders on issues like data management, data architectures, AI tools, AI operationalization, and AI governance. Veteran technology and business analyst, researcher, and leader, Fern Halper, walks you through the organizational and technical factors that determine success in data, analytics, and AI.
This book brings together the insights, case studies, and leader interviews that set out exactly what you need to succeed as you incorporate artificial intelligence throughout your organization. It covers the latest trends in data and AI (and how they're relevant to your top- and bottom-lines), data products, data fabric, and AI responsibility, risk mitigation, and ethics.
Inside the book:
- Specific steps to building the robust internal data foundation you'll need for artificial intelligence implementation
- How to democratize business intelligence so data analysts are free to conduct deeper analyses and perform more sophisticated analytical roles
- Informed advice for building AI models, applications, and innovations, and explanations of best practices for model building aligned with your organization's strategies
Perfect for business and technology leaders working towards a comprehensive data and AI strategy, Data Makes the World Go 'Round: The Data, Tech, and Trust Behind AI Success is a deeply informed, up-to-date, and practical exploration of the foundations of every successful AI transformation - and how you can build them in your own organization.
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FERN HALPER, PhD, has over two decades' experience helping organizations navigate rapid innovation. As a practitioner, industry analyst, researcher, and thought leader, she has led initiatives in e-commerce, big data, cloud computing, and artificial intelligence. She is currently a leader at TDWI, a company dedicated to research and education in data management, BI, AI, and governance.
Content
Acknowledgments xiii
About the Author xv
Introduction 1
Part I The AI Imperative 7
Chapter 1 The Business Case for Analytics and Artificial Intelligence 9
Chapter 2 The Leadership Challenge 21
Chapter 3 The AI Journey 35
Part II Building the Data Foundation for AI 45
Chapter 4 The New Data Landscape 47
Chapter 5 The Need for Modern Data Architectures 57
Chapter 6 Metadata: What It Is and How It Is Evolving 71
Chapter 7 The Data Quality and Data Governance Imperatives 79
Chapter 8 Data Products 87
Part III Democratizing Analytics 97
Chapter 9 The Self-service Imperative 99
Chapter 10 Artificial Intelligence in Business Intelligence 111
Chapter 11 Ensuring Data Literacy 121
Chapter 12 Putting Self-service to Work in Your Company 131
Part IV The AI Build-Out-Ecosystem, Models, Operations, and the Shift to Agents 145
Chapter 13 The Business Strategy Behind the Technology 147
Chapter 14 The AI Ecosystem 159
Chapter 15 Building AI Models 173
Chapter 16 Operationalizing AI 191
Chapter 17 Generative AI 201
Chapter 18 Agentic AI 217
Part V Governing Data and AI-Ensuring Trust and Compliance 229
Chapter 19 Data Governance-Building Trust for AI 231
Chapter 20 Analytics and AI Governance-Managing Risk and Performance 249
Part VI Putting It All Together 279
Chapter 21 Tying It All Together 281
Conclusion 292
Index 293
