How Do Data Science Workers Communicate Intermediate Results?

TL;DR

This paper explores how data science workers communicate intermediate results within teams, identifying key factors and challenges to improve collaborative workflows and tool design.

Contribution

It provides a detailed analysis of the intermediate communication process in data science teams based on interviews, highlighting factors and challenges not previously well understood.

Findings

Characterized communication types, goals, artifacts, and modes

Identified key challenges in current communication practices

Discussed design implications for better tools

Abstract

Data science workers increasingly collaborate on large-scale projects before communicating insights to a broader audience in the form of visualization. While prior work has modeled how data science teams, oftentimes with distinct roles and work processes, communicate knowledge to outside stakeholders, we have little knowledge of how data science workers communicate intermediately before delivering the final products. In this work, we contribute a nuanced description of the intermediate communication process within data science teams. By analyzing interview data with 8 self-identified data science workers, we characterized the data science intermediate communication process with four factors, including the types of audience, communication goals, shared artifacts, and mode of communication. We also identified overarching challenges in the current communication process. We also discussed design implications that might inform better tools that facilitate intermediate communication within data science teams.