My guest in this episode is Evan Shellshear, an expert in artificial intelligence and co-author of the eye-opening book "Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype."
With nearly two decades of experience in developing AI tools, Evan shares his insights into the real challenges and pitfalls of data science projects, and how organizations can overcome these hurdles.
About Evan Shellshear:
Evan is a renowned AI expert with a Ph.D. in Game Theory from the University of Bielefeld. He has worked globally with leading companies across various industries, using advanced analytics to drive innovation and efficiency.
As an author, his work seeks to demystify the complexities of AI and guide organizations toward successful implementation.
Episode summary:
In this episode, we explore the critical themes of Evan's book, which aims to shed light on why so many data science projects fall short of their potential. We unpack the exaggerated promises and oversimplifications that often lead to these failures, and discuss practical strategies to avoid them.
Discussion highlights:
Why Do Data Science Projects Fail?
- Evan discusses the common pitfalls, including unrealistic expectations and lack of understanding of project complexities.
Balancing costs and benefits:
- How organizations can weigh the costs of failure against the potential benefits of successful data science projects.
Avoiding failures:
- Practical advice on increasing success rates by setting realistic goals and aligning projects with business priorities.
Impact of organizational culture:
- How cultural factors within a company can make or break data science initiatives.
Measuring success:
- Effective metrics and indicators for evaluating project outcomes.
You can find out more about Evan's book here, and connect with him via LinkedIn.
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