ColVis: Collaborative Visualization Design Workshops for Diverse User Groups

TL;DR

This paper introduces ColVis, a collaborative workshop framework that involves both experts and novice users in data visualization design, aiming to improve understanding of diverse user needs and enhance visualization outcomes.

Contribution

It presents a novel workshop framework for collaborative visualization design involving interdisciplinary users and shares insights from multiple case studies.

Findings

Effective engagement of both experts and novices improves visualization design.

Workshop outcomes provided valuable insights and practical recommendations.

Critical reflection enhanced the workshop process and its applicability.

Abstract

Understanding different types of users' needs can even be more critical in today's data visualization field, as exploratory visualizations for novice users are becoming more widespread with an increasing amount of data sources. The complexity of data-driven projects requires input from including interdisciplinary expert and novice users. Our workshop framework helps taking design decisions collaboratively with experts and novice users, on different levels such as outlining users and goals, identifying tasks, structuring data, and creating data visualization ideas. We conducted workshops for two different data visualization projects. For each project, we conducted a workshop with project stakeholders who are domain experts, then a second workshop with novice users. We collected feedback from participants and used critical reflection on the process. Later on, we created recommendations on how this workshop structure can be used by others. Our main contributions are, (1) the workshop framework for designing data visualizations, (2) describing the outcomes and lessons learned from multiple workshops.