How Data Workers Cope with Uncertainty

Nadia Boukhelifa (1) , Marc-Emmanuel Perrin (2) , Samuel Huron (3, 4, 5, 6, 7) , James Eagan (2)

1 INRA - Institut National de la Recherche Agronomique
2 LTCI - Laboratoire Traitement et Communication de l'Information
3 Télécom ParisTech
4 SES - Département Sciences Economiques et Sociales
5 I3 SES - Institut interdisciplinaire de l’innovation de Telecom Paris
6 SID - Sociologie Information-Communication Design
7 IP Paris - Institut Polytechnique de Paris

Uncertainty plays an important and complex role in data analysis , where the goal is to find pertinent patterns, build robust models, and support decision making. While these endeavours are often associated with professional data scientists, many domain experts engage in such activities with varying skill levels. To understand how these domain experts (or "data workers") analyse uncertain data we conducted a qualitative user study with 12 participants from a variety of domains. In this paper, we describe their various coping strategies to understand, min-imise, exploit or even ignore this uncertainty. The choice of the coping strategy is influenced by accepted domain practices, but appears to depend on the types and sources of uncertainty and whether participants have access to support tools. Based on these findings, we propose a new process model of how data workers analyse various types of uncertain data and conclude with design considerations for uncertainty-aware data analytics.

Mots clés: data science; qualitative study; uncertainty; data analysis

Domaines: Interface homme-machine [cs.HC]

Nadia Boukhelifa, Marc-Emmanuel Perrin, Samuel Huron, James Eagan. How Data Workers Cope with Uncertainty: A Task Characterisation Study. CHI 2017, ACM, May 2017, Denver, United States. ⟨10.1145/3025453.3025738⟩. ⟨hal-01472865⟩