Data artists develop stories and visuals together through sketches and prototypes
Understanding Creative Design Practices among Data Artists
Human-Computer Interaction
Summary
Creative data visualizations tell stories and invite people to explore information, but how artists design these visuals is not well known. The authors studied data artists through projects, interviews, and design tasks, finding that artists combine exploring data with sketching and prototyping to shape their stories and images. They get ideas from data, other artworks, everyday sights, and personal memories. Artists usually focus on one design direction at a time since making multiple options is costly, balancing meaning, beauty, clarity, and what they can do.
What this means in practice
- •For data visualization tool developers: Create tools that link storytelling, design references, and real data to support iterative design and creativity.
- •For ai assistant developers: Develop AI features that help test and revise visualization alternatives while keeping designers’ creative choices intact.
Authors
Tianwei Ma, Anna Offenwanger, Naimul Hoque
Abstract
Creative and artistic data visualizations communicate stories and invite engagement, yet how designers develop their expressive forms remains poorly understood. We investigate the design process of data artists through three complementary studies: an analysis of 40 public project accounts, artifact-anchored interviews with seven experienced data artists, and a design task-based study with eight data artists. We find that stories and visual forms develop together through data exploration, reference adaptation, sketching, and prototyping. Inspiration comes from data, existing work, everyday imagery, and personal experience. Sketches help develop mappings, while prototypes with real data can reshape representations and intended stories. Designers consider multiple possibilities but typically develop one direction at a time, partly because producing alternatives is costly. Their choices balance meaning, visual appeal, readability, and feasibility. These findings inform tools connecting stories, references, and real data, including AI assistance that supports testing and revising alternatives while preserving designers' creative judgment.