Why Data Science Projects Fail
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Home > Computing and Information Technology > Computer science > Artificial intelligence > Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype(Chapman & Hall/CRC Data Science Series)
Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype(Chapman & Hall/CRC Data Science Series)

Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype(Chapman & Hall/CRC Data Science Series)


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About the Book

The field of artificial intelligence, data science, and analytics is crippling itself. Exaggerated promises of unrealistic technologies, simplifications of complex projects, and marketing hype are leading to an erosion of trust in one of our most critical approaches to making decisions: data driven. This book aims to fix this by countering the AI hype with a dose of realism. Written by two experts in the field, the authors firmly believe in the power of mathematics, computing, and analytics, but if false expectations are set and practitioners and leaders don’t fully understand everything that really goes into data science projects, then a stunning 80% (or more) of analytics projects will continue to fail, costing enterprises and society hundreds of billions of dollars, and leading to non-experts abandoning one of the most important data-driven decision-making capabilities altogether. For the first time, business leaders, practitioners, students, and interested laypeople will learn what really makes a data science project successful. By illustrating with many personal stories, the authors reveal the harsh realities of implementing AI and analytics.

Table of Contents:
ABOUT THE AUTHORS FOREWORD INTRODUCTION The Sepsis Scourge An Epic Challenge A Focus on Failures: The Purpose Behind Our Literary Venture The Epic Battle Beyond the Clickbait: When Headlines Just Scratch the Surface Data-driven Projects are Complex Begin Your Journey to Outsmart Failure Critical Thinking: How Not to Fail Introduction Bibliography ANALYTICALLY IMMATURE ORGANIZATIONS The AI Hype Mapping the Terrain: Prior Insights What Happened to Best Practices? What Counts as an ADSAI Failure? Our Thesis Facing Challenges Critical Thinking: How Not to Fail Chapter 1 Bibliography STRATEGY RetailCo’s Strategic Nightmare The Difficult and Critical Role of Strategy7Failing to Build Organizational Need Not Understanding the Real Business Problem The Problem with Selecting Good Business Problems Mike’s Story: AI in the Outback Putting the Cart (Technology) Before the Horse (Business) The Solution: Put Economics Back in the Driver’s Seat Resolving Mike’s AI Investment Challenge Solving a Problem That is Not a Business Priority WayBlazer: Companies Will Not Always Pay for the Fancier Mousetrap Challenges in Aligning Vision, Strategy, and Measuring Success Lack of Leadership Buy-in Critical Thinking: How Not to Fail Chapter 2 Bibliography PROCESS Data Quality and Reliability Issues Let the Data Hunt Begin (Un)reasonable Expectations Houston, We Have a Communication Problem Presenting the Message Breaking Down Silos Starting Small and Simple Project Management for ADSAI Asking the Right Questions Critical Thinking: How Not to Fail Chapter 3 Bibliography PEOPLE Lacking the Right Resources The New Digital Divide Analytics (or AI) Translators Where Do You Find Analytics Translators? Strengthening ADSAI Curricula Analytically-driven Leadership Change Management Justification for Change Critical Thinking: How Not to Fail Chapter 4 Bibliography TECHNOLOGY Model Mishaps Misapplying the (Right or Wrong) Model Keep it Simple: Overemphasizing the Model, Technique, or Technology From Sandbox Model to Production System Tools Make Mistakes The Final Hurdle: Proper Data and Tool Infrastructure Critical Thinking: How Not to Fail Chapter 5 Bibliography ANALYTICALLY MATURE ORGANIZATIONS (More) Real-life Failures Outside Influences Humility Small Stumbles, Solid Outcome The Journey to Perfection Critical Thinking: How Not to Fail Chapter 6 Bibliography CONCLUSION Continuing the Success Strategy Process People Technology Summary Final Words Critical Thinking: How Not to Fail Conclusion Bibliography

About the Author :
Douglas Gray is a practitioner, leader, and educator with over 30 years of experience leading award-winning teams at industry luminaries in Analytics, including INFORMS Prize-winning American Airlines and Walmart. His teams have delivered advanced game-changing solutions in the airline operations, healthcare, and omnichannel retail supply chain domains which deliver hundreds of millions of dollars in business value and economic impact annually. He teaches Analytics and AI Strategy at Southern Methodist University (SMU) in the Executive MBA, Executive Education, and MS Data Science programs, and has published over a dozen articles on Analytics best practices and applications. Dr Evan Shellshear is an expert in artificial intelligence with a Ph.D. in Game Theory from the Nobel Prize winning University of Bielefeld in Germany. He has almost two decades of international experience in the development and design of AI tools for a variety of industries having worked with the world's top companies on all aspects of advanced analytical solutions from optimisation to machine learning in applications from HR to oil and gas, and robotics to supply chain. He is also the author of the Amazon best seller, Innovation Tools. Evan is currently based in Brisbane, Australia and is the CEO of a global AI digital platform.


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Product Details
  • ISBN-13: 9781032660301
  • Publisher: Taylor & Francis Ltd
  • Publisher Imprint: Chapman & Hall/CRC
  • Height: 234 mm
  • No of Pages: 208
  • Sub Title: The Harsh Realities of Implementing AI and Analytics, without the Hype
  • Width: 156 mm
  • ISBN-10: 1032660309
  • Publisher Date: 05 Sep 2024
  • Binding: Paperback
  • Language: English
  • Series Title: Chapman & Hall/CRC Data Science Series
  • Weight: 340 gr


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