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

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 is important. Analytics and AI have seized the popular imagination and have been applied within many organizations over the past couple of decades. For the most part, this has been a welcome development. Making decisions based on data and analysis generally leads to better outcomes than those made on intuition or experience. Analytics, data science, and now artificial intelligence (ADSAI, as the authors put it) have led to better marketing offers, more optimized supply chains, better human resource management, and greater productivity by knowledge and creative workers. The authors, both of whom have worked in data science roles in organizations for many years, are in full agreement with me on the potential value of the field. But they have done it a great service by focusing on the many ways in which data science projects can go astray. As they note, data science projects are complex, and they demonstrate that there are multiple ways in which they can fail to deliver value to organizations.