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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 analyse the ever-increasing volume of data. This process of adopting data science techniques into an engineering organisation is a sociotechnical process fraught with challenges. This paper uses a participant observation case study to to investigate and discuss the sociotechnical nature of the adoption data science technology into an engineering organisation. In the case study, a young data scientist / statistician interacted with experienced production engineers in a global automotive organisation 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 multi-site manufacturing organisation’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 digitalisation is brought into mature engineering organisations and points to a lack of knowledge on multiple levels of the data analysis process and the ethical implications this could have.

Data Fairy in Engineering Land: The Magic of Data Analysis as a Sociotechnical Process in Engineering Companies