Paper Number
1532
Paper Type
Complete
Abstract
Data scientists enter organizations with the aspiration to apply their abstract knowledge and transform through data-driven insights any kind of work domain. Yet, they often encounter several challenges while trying to get their ways of knowing accepted in non-tech-driven organizations. It is unclear how data scientists deal with the tension between embodying a "superior" ethos and functioning primarily in a support role. We explore this through a qualitative study of data scientists developing AI for recruitment. We find that data scientists perform “datafying” practices to capture domain experts’ knowledge while defending their self-perceived superior ethos. When the practices and expectations of the domain experts appear to remain elusive, the data scientists resort to “dataficing” which helps adhere to their ethos through data approaches deemed as good-enough. However, in this way the data scientists fail to learn from domain experts and update their work so that they can actually support them.
Recommended Citation
Sosa-Hidalgo, Mario; Pachidi, Stella; Hafermalz, Ella; and Huysman, Marleen, "From Datafying to Dataficing: How Data Scientists Adhere to the Data Science Ethos in the Face of Development Challenges" (2024). ICIS 2024 Proceedings. 25.
https://aisel.aisnet.org/icis2024/digtech_fow/digtech_fow/25
From Datafying to Dataficing: How Data Scientists Adhere to the Data Science Ethos in the Face of Development Challenges
Data scientists enter organizations with the aspiration to apply their abstract knowledge and transform through data-driven insights any kind of work domain. Yet, they often encounter several challenges while trying to get their ways of knowing accepted in non-tech-driven organizations. It is unclear how data scientists deal with the tension between embodying a "superior" ethos and functioning primarily in a support role. We explore this through a qualitative study of data scientists developing AI for recruitment. We find that data scientists perform “datafying” practices to capture domain experts’ knowledge while defending their self-perceived superior ethos. When the practices and expectations of the domain experts appear to remain elusive, the data scientists resort to “dataficing” which helps adhere to their ethos through data approaches deemed as good-enough. However, in this way the data scientists fail to learn from domain experts and update their work so that they can actually support them.
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