Understanding Data Visualization Design Practice
Not on the map. Why?
Abstract (arXiv)
Professional roles for data visualization designers are growing in popularity, and interest in relationships between the academic research and professional practice communities is gaining traction. However, despite the potential for knowledge sharing between these communities, we have little understanding of the ways in which practitioners design in real-world, professional settings. Inquiry in numerous design disciplines indicates that practitioners approach complex situations in ways that are fundamentally different from those of researchers. In this work, I take a practice-led approach to understanding visualization design practice on its own terms. Twenty data visualization practitioners were interviewed and asked about their design process, including the steps they take, how they make decisions, and the methods they use. Findings suggest that practitioners do not follow highly systematic processes, but instead rely on situated forms of knowing and acting in which they draw from precedent and use methods and principles that are determined appropriate in the moment. These findings have implications for how visualization researchers understand and engage with practitioners, and how educators approach the training of future data visualization designers.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Aug 2021.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 2.
5 papers.
- 2024ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generationcited by 118
- 2024C2: Scalable Auto-Feedback for LLM-based Chart Generationcited by 6
- 2025Understanding Why ChatGPT Outperforms Humans in Visualization Design Advicecited by 5
- 2026AnnoBench: A Benchmark for Visualization Annotation Generationcited by 1
- 2026SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Codecited by 0
- 2026SciFigure2Code: An AI-Reconstructed Benchmark for Scientific Figure-to-Code8 Sep 2026
- 2026AnnoBench: A Benchmark for Visualization Annotation Generation28 Jul 2026
- 2025Understanding Why ChatGPT Outperforms Humans in Visualization Design Advice3 Aug 2025
- 2024C2: Scalable Auto-Feedback for LLM-based Chart Generation24 Oct 2024
- 2024ChartMimic: Evaluating LMM's Cross-Modal Reasoning Capability via Chart-to-Code Generation14 Jun 2024