Authors: Jiali Liu, Nadia Boukhelifa, James Eagan
Abstract: Data workers come from various domains and engage in data analysis activities as a part of their daily work, but do not formally identify as data scientists. These data workers often need to explore alternatives throughout the sensemaking process, ranging from a diverse set of hypotheses and theories, to a variety of data sources, algorithms, methods, tools, and visual designs. We conducted semi-structured interviews with 12 data workers with different types of expertise in order to better understand and characterize the role of alternatives in their analyses. We conducted four types of analysis to understand (1) why data workers explore alternatives; (2) the different notions of alternatives and how they fit into the sensemaking process; (3) the high-level processes around alternatives; and (4) their strategies to generate, explore, and manage those alternatives. We find that participants' diverse level of domain and computational expertise, experience with different tools, and collaboration within their broader context plays an important role in how they explore these alternatives. These findings call out the need for more attention towards a deeper understanding of alternatives and the need for better tools to facilitate the exploration, interpretation and management of alternatives. Drawing upon these analyses and findings, we present a framework based on participants’ (1) level of focus, (2) abstraction level, (3) analytic processes, and (4) strategies employed. We show how this framework can help understand how data workers consider such alternatives in their analyses and how tool designers might create tools to better support them.