What do we talk about when we talk about dashboards?
Abstract
Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.
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Presenting too little or highly aggregated information can decrease transparency and limit critical elaboration of the data.
“Conversely, presenting too little information, or information that is highly aggregated, can decrease transparency and limit critical elaboration of the data”
From Check the box! How to deal with automation bias in AI-based personnel selection (2023) · cites this paper as Sarikaya et al., 2019
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- Authors
- Sarikaya, A., Correll, M., Bartram, L., Tory, M., & Fisher, D.
- Affiliations
- Tableau Software (United States) (US)
- Simon Fraser University (CA)
- Venue
- IEEE Transactions on Visualization and Computer Graphics
- Published
- 2019-01
- Volume
- 25
- Issue
- 1
- Pages
- 682-692
- ISSN
- 1077-2626 (ISSN-L)
- 1941-0506
- 2160-9306
- OpenAlex
- W2888652842
- Cited by
- 380
- References
- 63
- Citations by year
- 2026: 40 · 2025: 64 · 2024: 53 · 2023: 53 · 2022: 47 · 2021: 54 · 2020: 39 · 2019: 28 · 2018: 2
- FWCI
- 15.38
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Visualization and Computer Graphics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Microsoft Research
- Tableau Research
- Simon Fraser University
- NSERC
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Computer Graphics and Visualization Techniques
- Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
- Keywords
- dashboards, data visualization, dashboard design, design space, interaction level
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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