Year: 2025
Type: preprint
Abstract: Analysts increasingly explore data through evolving, narrative-driven inquiries, moving beyond static dashboards and predefined metrics as their questions deepen and shift. As these explorations progress, insights often become dispersed across views, making it challenging to maintain context or clarify how conclusions arise. Through a formative study with 48 participants, we identify key barriers that hinder narrative-driven exploration, including difficulty maintaining context across views, tracing reasoning paths, and externalizing evolving interpretations. Our findings surface design opportunities to support narrative-driven analysis better.
Source: arXiv (Cornell University)
Authors: Huang, Oliver, Nobre, Carolina
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6891955436
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