In the era of digitalization, manufacturing companies expect their growing access to data to lead to improvements and innovations. Manufacturing engineers will have to collaborate with data scientists to analyze the ever-increasing volume of data. This process of adopting data science techniques into an engineering organization is a sociotechnical process fraught with challenges. This article uses a participant observation case study to investigate and discuss the sociotechnical nature of the adoption data science technology into an engineering organization. In the case study, a young data scientist/statistician interacted with experienced production engineers in a global automotive organization to mutual satisfaction. However, the case study highlights the mis-aligned expectations between engineers and data scientists and knowledge in what is necessary to successfully benefit from manufacturing process data.
The results reveal that the engineers had an initially romantic and idealistic view on how data scientists can bring value out of dispersed and complex information residing in the multisite manufacturing organization’s datasets in a “magic” way. Conversely, the data scientist had not enough engineering and contextual understanding to ask the right questions. The case reveals important shortcomings in the sociotechnical processes that undergo changes as digitalization is brought into mature engineering organizations and points to a lack of knowledge on multiple levels of the data analysis process and the ethical implications this could have.