Buch, Englisch, 352 Seiten, Format (B × H): 159 mm x 236 mm, Gewicht: 599 g
Buch, Englisch, 352 Seiten, Format (B × H): 159 mm x 236 mm, Gewicht: 599 g
ISBN: 978-1-260-45914-2
Verlag: McGraw-Hill Education
Lead your organization to become evidence-driven
Data. It’s the benchmark that informs corporate projections, decision-making, and analysis. But, why do many organizations that see themselves as data-driven fail to thrive? In Leading with AI and Analytics, two renowned experts from the Kellogg School of Management show business leaders how to transform their organization to become evidence-driven, which leads to real, measurable changes that can help propel their companies to the top of their industries.
The availability of unprecedented technology-enabled tools has made AI (Artificial Intelligence) an essential component of business analytics. But what’s often lacking are the leadership skills to integrate these technologies to achieve maximum value. Here, the authors provide a comprehensive game plan for developing that all-important human factor to get at the heart of data science: the ability to apply analytical thinking to real-world problems. Each of these tools and techniques comes to powerful life through a wealth of powerful case studies and real-world success stories.
Inside, you’ll find the essential tools to help you:
- Develop a strong data science intuition quotient
- Lead and scale AI and analytics throughout your organization
- Move from “best-guess” decision making to evidence-based decisions
- Craft strategies and tactics to create real impact
Written for anyone in a leadership or management role—from C-level/unit team managers to rising talent—this powerful, hands-on guide meets today’s growing need for real-world tools to lead and succeed with data.
Autoren/Hrsg.
Weitere Infos & Material
SECTION 1
Introduction
CHAPTER 1 AI and Analytics Are a Leadership Problem
CHAPTER 2 A Framework for AI and Analytics Success
SECTION 2
Consuming AI and Analytics
CHAPTER 3 Exploratory Analytics: What Is Going on with My Data?
CHAPTER 4 Distinguishing Good from Bad Analytics
SECTION 3
Actively Participating in AI and Analytics
PREDICTIVE ANALYTICS
CHAPTER 5 Anatomy of a Crystal Ball
CHAPTER 6 A Smarter Crystal Ball
CAUSAL ANALYTICS
CHAPTER 7 Designing Your Data for Analytics:
Experiments and Quasi-Experiments
CHAPTER 8 Working with Data You Have Part 1:
Using Opportunistic Data
CHAPTER 9 Working with Data You Have Part 2:
Learning from Natural Experiments
MAKING DECISIONS
CHAPTER 10 Optimizing and Scaling Your Decisions
SECTION 4
Executing on AI and Analytics
CHAPTER 11 Identifying Opportunities and Planning for AIA
CHAPTER 12 Understanding Barriers to Success
CHAPTER 13 Organizing for Success
SECTION 5
Success Stories with AIA
STORY 1 Allstate Builds Firmwide DSIQ
STORY 2 Vanguard Builds an Ecosystem of Analytics Excellence
STORY 3 Canadian Tire Creates an Enterprisewide
Analytics System
STORY 4 Royal Caribbean Sets Sail for Continuous
Analytics Improvement
STORY 5 Accenture Builds Analytics Capability for
Competitive Advantage
Acknowledgments
Notes
Index
