An ever increasing number of use cases for data science are evolving in most companies from nearly every sector. From small businesses to big industries, the number of data scientists is continuously increasing and with them, the size and complexity of data science teams gets bigger. At the same time, it is reported that only a few (22%) data science projects show high revenue and big data projects fail in large numbers (60 to 85%) [Atwal 2020]. This leads to the question: how to deal with the complexity of managing data science teams?
