AI-Enabled Analytics for Business

AI-Enabled Analytics for Business

by Lawrence S. Maisel, Robert J. Zwerling, Jesper H. Sorensen
Released January 2022
Publisher(s): Wiley
ISBN: 9781119736080

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Book description

We are entering the era of digital transformation where human and artificial intelligence (AI) work hand in hand to achieve data driven performance.  

Today, more than ever, businesses are expected to possess the talent, tools, processes, and capabilities to enable their organizations to implement and utilize continuous analysis of past business performance and events to gain forward-looking insight to drive business decisions and actions.  AI-Enabled Analytics in Business is your Roadmap to meet this essential business capability. To ensure we can plan for the future vs react to the future when it arrives, we need to develop and deploy a toolbox of tools, techniques, and effective processes to reveal forward-looking unbiased insights that help us understand significant patterns, relationships, and trends. This book promotes clarity to enable you to make better decisions from insights about the future. 

  • Learn how advanced analytics ensures that your people have the right information at the right time to gain critical insights and performance opportunities 
  • Empower better, smarter decision making by implementing AI-enabled  analytics decision support tools 
  • Uncover patterns and insights in data, and discover facts about your business that will unlock greater performance 
  • Gain inspiration from practical examples and use cases showing how to move your business toward AI-Enabled decision making 

AI-Enabled Analytics in Business is a must-have practical resource for directors, officers, and executives across various functional disciplines who seek increased business performance and valuation. 

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Dedication
  5. Acknowledgments
  6. Introduction
  7. PART I: Fundamentals
    1. CHAPTER 1: A Primer on AI-Enabled Analytics for Business
      1. AI AND ML—SIMILAR BUT DIFFERENT
      2. MACHINE LEARNING PRIMER
      3. ANALYTICS VS. ANALYSIS
      4. BI AND DATA VISUALIZATION VS. ANALYTICS
      5. BIASED VS. UNBIASED
      6. AI AND ROI
      7. CONCLUSION
      8. NOTES
    2. CHAPTER 2: Why AI-Enabled Analytics Is Essential for Business
      1. COMPETITIVENESS
      2. HUMAN JUDGMENT AND DECISION-MAKING
      3. CONCLUSION
      4. NOTES
    3. CHAPTER 3: Myths and Misconceptions About Analytics
      1. DATA SCIENTIST MISCONCEPTION AND MYTH
      2. SHOT IN THE DARK
      3. BASS-ACKWARD
      4. AI IS NOT IT
      5. BIG IS NOT BETTER
      6. NOT NOW
      7. NOTE TO EXECUTIVES
      8. CONCLUSION
      9. NOTES
    4. CHAPTER 4: Applications of AI-Enabled Analytics
      1. FINANCE
      2. SALES
      3. MANUFACTURING AND SUPPLY CHAIN
      4. DEMAND PLANNING AND INVENTORY
      5. CONCLUSION
      6. NOTES
  8. PART II: Roadmap
    1. CHAPTER 5: Roadmap for How to Implement AI-Enabled Analytics in Business
      1. CULTURE
      2. MINDSET
      3. PEOPLE
      4. PROCESS
      5. SYSTEMS
      6. THE ROADMAP FOR IMPLEMENTING AI-ENABLED ANALYTICS
      7. LAUNCHING THE CULTURE OF ANALYTICS
      8. CONCLUSION
      9. NOTES
    2. CHAPTER 6: Executive Responsibilities to Implement Analytics
      1. EXECUTIVE COMMITMENT
      2. ANALYTICS CHAMPION
      3. CHANGE MANAGEMENT
      4. CONCLUSION
      5. NOTES
    3. CHAPTER 7: Implementing Analytics
      1. DEFINE THE PROBLEM
      2. SELECT AN ANALYTICS SOFTWARE POC VENDOR
      3. PERFORM THE ANALYTICS POC
      4. BENCHMARK PEOPLE SKILLSET
      5. SCALE ANALYTICS
      6. ILLUSTRATIVE EXAMPLE OF THE ANALYTICS POC
      7. ANALYTICS POWERHOUSE
      8. CONCLUSION
      9. NOTE
    4. CHAPTER 8: The Role of Analytics in Strategic Decisions
      1. HOW WE TRICK OURSELVES
      2. TACTICS THAT AFFECT STRATEGY
      3. KEY PERFORMANCE INDICATORS (KPIs) AND STRATEGIC OBJECTIVES
      4. THE ANALYTICS SCORECARD™
      5. CONCLUSION
      6. NOTES
  9. PART III: Use Cases
    1. CHAPTER 9: Cases of Analytics Failures from Deviation to the Roadmap
      1. MINDSET COMMITMENT
      2. INSUFFICIENT PEOPLE AND PROCESSES
      3. TOOLBOX CONFUSION
      4. CONCLUSION
      5. NOTE
    2. CHAPTER 10: Use Case: Grabbing Defeat from the Jaws of Victory
      1. POC RESULTS—REALIZING THE THREE GOALS
      2. THE ROI OF AI
      3. FAILURE IS A CHOICE
      4. NOTE
    3. CHAPTER 11: Use Case: Incremental Improvements to Gain Insights
      1. STARTING ANALYTICS
      2. TEST AND LEARN
      3. ASSESSING ANALYTICS PERSONAS
      4. MOVING FORWARD
      5. NOTE
    4. CHAPTER 12: Use Case: Analytics Are for Everyone
      1. THE ROAD TO ANALYTICS
      2. STEPPING INTO ANALYTICS
      3. ANALYTICS IS FOR ALL
  10. Epilogue
    1. NOTES
  11. APPENDIX: Analytics Champion Framework: The Fundamental Qualifications, Skills, and Project Steps for the Analytics Champion
    1. INTRODUCTION
    2. ANALYTICS CHAMPION QUALIFICATIONS
    3. ANALYTICS CHAMPION SKILLSETS
    4. STARTING AN ANALYTICS PROJECT
    5. EPILOGUE
    6. NOTES
  12. About the Authors
    1. LAWRENCE S. MAISEL
    2. ROBERT J. ZWERLING
    3. JESPER H. SORENSEN
  13. About the Website
  14. Index
  15. End User License Agreement

Product information

  • Title: AI-Enabled Analytics for Business
  • Author(s): Lawrence S. Maisel, Robert J. Zwerling, Jesper H. Sorensen
  • Release date: January 2022
  • Publisher(s): Wiley
  • ISBN: 9781119736080