The article discusses the varying organizational models data science teams can adopt and how these setups impact their effectiveness and team dynamics. It argues that the choice of model greatly influences not only the nature of the work produced but also the morale and goals of the team. With the risk of inefficiency, leadership must carefully select organizational structures that align with specific project goals and team strengths. It presents six models, assessing their benefits and drawbacks, to assist companies in optimizing their data science practices.
Data scientists often operate within various organizational models which significantly influence their output and the overall value to the company.
Adopting the wrong organizational model can limit impact, cause delays, and compromise team morale. Leaders must select models aligned with project goals.
This article identifies six distinct organizational models for data science teams, considering initiators of work, team evaluation, and outputs of the teams.
Each identified model has its unique pros and cons, which can guide companies in choosing the best structure based on their objectives.
Collection
[
|
...
]