managing data science work

It appears to me the cross-industry standard process for data mining (CRISP-DM) is still, almost a quarter century after first having been formulated, a valuable framework to guide management of a data science team. Start with building business understanding, followed by understanding the data, preparing it, moving from modeling to solve the problem over to evaluating the model and ending by deploying it. The framework is iterative, and allows for back-and-forth between these steps based on what's learned in the later steps.

CRISP-DM

It doesn't put too great an emphasis on scheduling the activities, but focuses on the value creation.

The Observe-Orient-Decide-Act (OODA) loop from John Boyd seems to be an analogue concept. Competing businesses would then be advised to speed up their cycling through the CRISP-DM loop, as that's how Boyd stated advantage is obtained - by cycling through the OODA loops more quickly than ones opponent. Most interestingly, in both loops it's a common pitfall to skip the last step - deploying the model / acting.

OODA loop

(Image by Patrick Edwin Moran - Own work, CC BY 3.0)