The Human-Data-Model Interaction Canvas for Visual Analytics

TL;DR

The paper introduces the HDMI Canvas, a new framework for visual analytics that systematically characterizes human, data, and model roles, enhancing process design and interdisciplinary collaboration.

Contribution

It presents the HDMI Canvas, a novel perspective that complements existing VA models by emphasizing roles, feedback, and interpretability, aiding process differentiation and design.

Findings

Demonstrated utility through two case studies.

Enhances understanding of VA actor roles and interactions.

Supports design of user-centered VA processes.

Abstract

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.