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The paper by Nadia Boukhelifa, Marc-Emmanuel Perrin, Samuel Huron et James Eagan, “How Domain Experts Analyse Uncertain Data: A Task Characterisation Study”, received the “Honorable Mention award” which is reserved to the 5% top publications at the ACM SIG CHI 2017 conference.
Uncertainty plays an important and complex role in data analysis and affects many domains. To understand how domain experts analyse data under uncertainty and the tasks they engage in, we conducted a qualitative user study with 12 participants from a variety of domains. We collected data from audio and video recordings of think-aloud demo sessions and semi-structured interviews. We found that analysts sometimes ignore known uncertainties in their data, but only when these are not relevant to their tasks. More often however, they deploy various coping strategies, aiming to understand, minimise or exploit the uncertainty. Within these coping strategies, we identified five high level tasks that appear to be common amongst all of our participants. We believe our findings and further analysis of this data will yield concrete design guidelines for uncertainty-aware visual analytics.