Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value
- 5h 55m
- Eric Anderson, Florian Zettelmeyer
- McGraw-Hill
- 2021
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.
About the Authors
Eric Anderson and Florian Zettelmeyer are tenured professors at Northwestern University’s Kellogg School of Management. They are known across academic and business arenas as go-to experts in data analytics.
In this Book
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Chapter 1: AI and Analytics are a Leadership Problem
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Chapter 2: A Framework for AI and Analytics Success
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Chapter 3: Exploratory Analytics—What is Going on with My Data?
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Chapter 4: Distinguishing Good from Bad Analytics
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Chapter 5: Anatomy of a Crystal Ball
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Chapter 6: A Smarter Crystal Ball
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Chapter 7: Designing Your Data for Analytics—Experiments and Quasi-Experiments
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Chapter 8: Working with Data You Have Part 1—Using Opportunistic Data
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Chapter 9: Working with Data You Have Part 2—Learning from Natural Experiments
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Chapter 10: Optimizing and Scaling Your Decisions
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Chapter 11: Identifying Opportunities and Planning for AIA
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Chapter 12: Understanding Barriers to Success
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Chapter 13: Organizing for Success
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Story 1: Allstate Builds Firmwide DSIQ
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Story 2: Vanguard Builds an Ecosystem of Analytics Excellence
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Story 3: Canadian Tire Creates an Enterprisewide Analytics System
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Story 4: Royal Caribbean Sets Sail for Continuous Analytics Improvement
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Story 5: Accenture Builds Analytics Capability for Competitive Advantage
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Notes
