Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

TL;DR

This study explores the comprehensive outer-loop workflow of data scientists collaborating with clients, revealing six stages that extend beyond technical analysis to include trust-building, framing, bridging, and counseling, with implications for education and tool development.

Contribution

It introduces a novel six-stage outer-loop workflow for data science collaborations, expanding understanding beyond traditional technical processes.

Findings

Data scientists work in six distinct outer-loop stages.

Trust-building and emotional support are integral to successful collaborations.

The workflow has implications for education and tool development in data science.

Abstract

Data scientists often collaborate with clients to analyze data to meet a client's needs. What does the end-to-end workflow of a data scientist's collaboration with clients look like throughout the lifetime of a project? To investigate this question, we interviewed ten data scientists (5 female, 4 male, 1 non-binary) in diverse roles across industry and academia. We discovered that they work with clients in a six-stage outer-loop workflow, which involves 1) laying groundwork by building trust before a project begins, 2) orienting to the constraints of the client's environment, 3) collaboratively framing the problem, 4) bridging the gap between data science and domain expertise, 5) the inner loop of technical data analysis work, 6) counseling to help clients emotionally cope with analysis results. This novel outer-loop workflow contributes to CSCW by expanding the notion of what collaboration means in data science beyond the widely-known inner-loop technical workflow stages of acquiring, cleaning, analyzing, modeling, and visualizing data. We conclude by discussing the implications of our findings for data science education, parallels to design work, and unmet needs for tool development.