Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype

Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype book cover

Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype

Author(s): Douglas Gray (Author), Evan Shellshear (Author)

  • Publisher: Chapman and Hall/CRC
  • Publication Date: 15 Aug. 2024
  • Edition: 1st
  • Language: English
  • Print length: 222 pages
  • ISBN-10: 1032660309
  • ISBN-13: 9781032660301

Book Description

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.

Editorial Reviews

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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Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls

Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls book cover

Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls

Author(s): Joyce Weiner (Author)

  • Publisher: Springer International Publishing AG
  • Publication Date: 18 Dec. 2020
  • Language: English
  • Print length: 65 pages
  • ISBN-10: 3031005570
  • ISBN-13: 9783031005572

Book Description

Recent data shows that 87% of Artificial Intelligence/Big Data projects don’t make it into production (VB Staff, 2019), meaning that most projects are never deployed. This book addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid those pitfalls. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections.

Editorial Reviews

About the Author

Joyce Weiner is a Principal Engineer at Intel Corporation. Her area of technical expertise is data science and using data to drive efficiency. Joyce is a black belt in Lean Six Sigma. She has a B.S. in Physics from Rensselaer Polytechnic Institute, and an M.S. in Optical Sciences from the University of Arizona. She lives with her husband outside Phoenix, Arizona.

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电子书代发PDF格式价格30我要求助
未经允许不得转载:Wow! eBook » Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls