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 by Douglas Gray is published by CRC Press and will be available on September 5, 2024. This 208-page book addresses the challenges faced in the fields of artificial intelligence, data science, and analytics, highlighting how exaggerated promises and marketing hype can undermine trust in data-driven decision-making.
Readers will discover insights into the complexities of data science projects and the reasons behind their high failure rates. The authors, drawing on their expertise, emphasize the importance of understanding the realities of implementing AI and analytics. Through personal stories, the book aims to educate business leaders, practitioners, and students about what truly contributes to the success of data science initiatives, countering the prevalent misconceptions in the industry.
Official synopsis Publisher
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.
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