Passing the Data Baton: A Retrospective Analysis on Data Science Work and Workers

Anamaria Crisan, Brittany Fiore-Gartland, Melanie Tory

View presentation: 2020-10-26T14:45:00Z GMT-0600 Change your timezone on the schedule page
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Our model of data science work synthesized from an extensive and systematic literature review. We summarize data science work processes as constituting four higher order and fourteen lower order processes. Red boarders around the lower order processes highlight where we found explicit evidence in the literature for data visualization as a core component of the work being carried out, these processes were profiling, interpretation, monitoring, and dissemination. We also identified two emergent processes, collaboration and pedagogy, that we believe are of growing importance but not consistently acknowledged to be a part of data science work.


Data science is a rapidly growing discipline and organizations increasingly depend on data science work. Yet the ambiguity around data science, what it is, and who data scientists are can make it difficult for visualization researchers to identify impactful research trajectories. We have conducted a retrospective analysis of data science work and workers as described within the data visualization, human computer interaction, and data science literature. From this analysis we synthesis a comprehensive model that describes data science work and breakdown to data scientists into nine distinct roles. We summarise and reflect on the role that visualization has throughout data science work and the varied needs of data scientists themselves for tooling support. Our findings are intended to arm visualization researchers with a more concrete framing of data science with the hope that it will help them surface innovative opportunities for impacting data science work.