How do Data Science Workers Collaborate? Roles, Workflows, and Tools

By Amy X. Zhang, Michael J. Muller, Dakuo Wang. Proc. ACM Hum. Comput. Interact., 2020

318 citations recorded by the source corpus.

Today, the prominence of data science within organizations has given rise to teams of data science workers collaborating on extracting insights from data, as opposed to individual data scientists working alone. However, we still lack a deep understanding of how data science workers collaborate in practice. In this work, we conducted an online survey with 183 participants who work in various aspects of data science. We focused on their reported interactions with each other (e.g., managers with engineers) and with different tools (e.g., Jupyter Notebook). We found that data science teams are extremely collaborative and work with a variety of stakeholders and tools during the six common steps of a data science workflow (e.g., clean data and train model). We also found that the collaborative practices workers employ, such as documentation, vary according to the kinds of tools they use. Based on these findings, we discuss design implications for supporting data science team collaborations and future research directions.

Subfields: Information Systems, Management Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ab8ba0f2d290a8e56eb61e10027d0b2e57d2d544

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Citations in the corpus

“Everyone wants to do the model work, not the data work”: Data Cascades in High-Stakes AI (2021, background): “ship have long examined the practices of collaboration, problem formulation, and sensemaking, by humans behind the datasets, including data collectors and scientists, [69, 86, 127], and are designing computational artefacts for dataset development [53].”
From Human-Human Collaboration to Human-AI Collaboration: Designing AI Systems That Can Work Together with People (2020, background): “These systems are designed with great promise to work together with doctors [3], customer services, and data scientists respectively [9].”