Why Data Science Needs Product Managers
In 2019, Gartner predicted that 80% of AI projects wouldn’t scale to provide business value — that the data science projects would fail. While numerous other posts (see here, here, and here) have provided differing reasons for data science failure, they mask the underlying cause: the lack of a data science product manager.
Most project failures in any business can be boiled down to two main causes: a failure of personnel or a failure of process (however, failures of process are typically caused by failures of personnel). As Ray Dalio stated in The Principles, “Getting the right people in the right roles in support of your goal is the key to succeeding at whatever you choose to accomplish.” Data science projects are no different. In this article, I discuss some of the common reasons data science projects fail and how data science product managers are key to preventing these failures.