ENGLISH

Leading with AI and Analytics: Build Your Data Science IQ to Drive Business Value

Book information

Publisher
Mcgraw Hill Professional
Year
2020
ISBN
9781260459159, 1260459152
Language
english
Format
PDF
Filesize
4 MB (4684602 bytes)
Pages
352\353
Time added
2021-10-13 17:00:06

Description

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. Cover Title Page Copyright Page Contents Preface 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

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