jagoda walny

visual thinking with data

Data Changes Everything

It’s easy to assume that the tools and approaches used for general software design apply equally to data visualization design. But data visualization design and interface design are often deeply and fundamentally distinct from one another. We learned this the hard way when we turned our research lab into a collaborative data visualization design studio for a few years. Data permeates visualization interfaces in ways that pose challenges at every stage of the design process. These challenges are even greater within large visualization teams. By reflecting on and articulating these challenges, we hope to inspire new, powerful data visualization design tools and communication processes. Data Changes Everything: How Data Visualization Design and Interface Design are Different (Article on Multiple Views, medium.com)

Publications:

Jagoda Walny, Christian Frisson, Mieka West, Doris Kosminsky, Søren Knudsen, Sheelagh Carpendale, and Wesley Willett. Data Changes Everything: Challenges and Opportunities in Data Visualization Design Handoff. In IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, pp. 12-22, Jan. 2020. doi: 10.1109/TVCG.2019.2934538 (Best InfoVis Paper Award)

Abstract: Complex data visualization design projects often entail collaboration between people with different visualization-related skills. For example, many teams include both designers who create new visualization designs and developers who implement the resulting visualization software. We identify gaps between data characterization tools, visualization design tools, and development platforms that pose challenges for designer-developer teams working to create new data visualizations. While it is common for commercial interaction design tools to support collaboration between designers and developers, creating data visualizations poses several unique challenges that are not supported by current tools. In particular, visualization designers must characterize and build an understanding of the underlying data, then specify layouts, data encodings, and other data-driven parameters that will be robust across many different data values. In larger teams, designers must also clearly communicate these mappings and their dependencies to developers, clients, and other collaborators. We report observations and reflections from five large multidisciplinary visualization design projects and highlight six data-specific visualization challenges for design specification and handoff. These challenges include adapting to changing data, anticipating edge cases in data, understanding technical challenges, articulating data-dependent interactions, communicating data mappings, and preserving the integrity of data mappings across iterations. Based on these observations, we identify opportunities for future tools for prototyping, testing, and communicating data-driven designs, which might contribute to more successful and collaborative data visualization design.

  • Authors: Jagoda Walny, Christian Frisson, Mieka West, Doris Kosminsky, Søren Knudsen, Sheelagh Carpendale, and Wesley Willett
  • Keywords: data visualization, design process, reflection
  • Year: 2020
Stages of a data visualization development process and the dependencies between them.
Stages of a data visualization development process and the dependencies between them.
While some developers design interfaces as they code, they can also choose to use the many commercially available interface design tools that are available for designer+developer teams.
While some developers design interfaces as they code, they can also choose to use the many commercially available interface design tools that are available for designer+developer teams.
Visualization design is currently best supported by dedicated developers. There is an opportunity for commercial tools to support data understanding and design handoff
Visualization design is currently best supported by dedicated developers. There is an opportunity for commercial tools to support data understanding and design handoff
Challenge 1: A late-stage change to the dataset altered the meaning of categories.
Challenge 1: A late-stage change to the dataset altered the meaning of categories.
Challenge 2: Data and interactions create unexpected edge cases.
Challenge 2: Data and interactions create unexpected edge cases.
Challenge 6: Discrepancies between the design and development versions are hard to spot.
Challenge 6: Discrepancies between the design and development versions are hard to spot.
Challenge 6: Discrepancies between the design and development versions are hard to spot.
Challenge 6: Discrepancies between the design and development versions are hard to spot.