The Human-Data-Model Interaction Canvas for Visual Analytics
This work provides a framework for improving VA outreach, interdisciplinary collaboration, and user-centered design, but it is incremental as it builds on existing VA process models.
The paper tackles the challenge of integrating humans, data, and models in Visual Analytics (VA) by proposing the HDMI Canvas, a perspective that systematically characterizes their roles and interactions, as demonstrated through two preliminary case studies.
Visual Analytics (VA) integrates humans, data, and models as key actors in insight generation and data-driven decision-making. This position paper values and reflects on 16 VA process models and frameworks and makes nine high-level observations that motivate a fresh perspective on VA. The contribution is the HDMI Canvas, a perspective to VA that complements the strengths of existing VA process models and frameworks. It systematically characterizes diverse roles of humans, data, and models, and how these actors benefit from and contribute to VA processes. The descriptive power of the HDMI Canvas eases the differentiation between a series of VA building blocks, rather than describing general VA principles only. The canvas includes modern human-centered methodologies, including human knowledge externalization and forms of feedback loops, while interpretable and explainable AI highlight model contributions beyond their conventional outputs. The HDMI Canvas has generative power, guiding the design of new VA processes and is optimized for external stakeholders, improving VA outreach, interdisciplinary collaboration, and user-centered design. The utility of the HDMI Canvas is demonstrated through two preliminary case studies.