The Influence of Visual Provenance Representations on Strategies in a Collaborative Hand-off Data Analysis Scenario
Jeremy E. Block, Shaghayegh Esmaeili, Eric D. Ragan, John R. Goodall,, G. David Richardson

TL;DR
This study examines how different types of visual provenance information, such as data coverage and interaction history, influence analysts' confidence, strategies, and behaviors during collaborative data analysis tasks.
Contribution
It provides empirical insights into how specific provenance representations affect analyst confidence and investigation strategies in open-ended scenarios.
Findings
Data coverage increases confidence without restricting exploration.
Interaction history requires more time to interpret and reduces confidence.
Provenance presentation influences analysis behaviors and decision-making.
Abstract
Conducting data analysis tasks rarely occur in isolation. Especially in intelligence analysis scenarios where different experts contribute knowledge to a shared understanding, members must communicate how insights develop to establish common ground among collaborators. The use of provenance to communicate analytic sensemaking carries promise by describing the interactions and summarizing the steps taken to reach insights. Yet, no universal guidelines exist for communicating provenance in different settings. Our work focuses on the presentation of provenance information and the resulting conclusions reached and strategies used by new analysts. In an open-ended, 30-minute, textual exploration scenario, we qualitatively compare how adding different types of provenance information (specifically data coverage and interaction history) affects analysts' confidence in conclusions developed,…
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Taxonomy
TopicsData Visualization and Analytics · Scientific Computing and Data Management · Complex Network Analysis Techniques
