Paper Number
1536
Paper Type
Complete Research Paper
Abstract
Data science practitioners such as data scientists, data engineers and machine learning (ML) engineers are emerging in organizations, following a similar trajectory to previous IT professionals. Current research suggests that these practitioners engage in a more flexible, performative, and craft-like work ethos than traditional IT professionals. However, little is known about how data science practitioners cope with this traditional IT work perception while enacting their “craft” ethos in organizations. We find that data science practitioners increase and decrease the complicatedness of their ML algorithms intentionally throughout the development process. Our findings suggest that, in contrast with the mechanistic and efficiency-focused work ethos of IT professionals in organizations, data science practitioners use the modulation of complicatedness as a mechanism to redeem their identity as craft workers. Our findings have implications for understanding the emergence of data science practitioners, their occupational identity, and the differences in management compared to other IT professionals.
Recommended Citation
Sosa Hidalgo, Mario Alberto; Hafermalz, Ella; Günther, Wendy; and Huysman, Marleen, "The Ongoing Quest for Complicatedness: How Data Science Practitioners Manage Their Emerging Role in Organizations" (2024). ECIS 2024 Proceedings. 12.
https://aisel.aisnet.org/ecis2024/track06_humanaicollab/track06_humanaicollab/12
The Ongoing Quest for Complicatedness: How Data Science Practitioners Manage Their Emerging Role in Organizations
Data science practitioners such as data scientists, data engineers and machine learning (ML) engineers are emerging in organizations, following a similar trajectory to previous IT professionals. Current research suggests that these practitioners engage in a more flexible, performative, and craft-like work ethos than traditional IT professionals. However, little is known about how data science practitioners cope with this traditional IT work perception while enacting their “craft” ethos in organizations. We find that data science practitioners increase and decrease the complicatedness of their ML algorithms intentionally throughout the development process. Our findings suggest that, in contrast with the mechanistic and efficiency-focused work ethos of IT professionals in organizations, data science practitioners use the modulation of complicatedness as a mechanism to redeem their identity as craft workers. Our findings have implications for understanding the emergence of data science practitioners, their occupational identity, and the differences in management compared to other IT professionals.
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.