The Unmet Data Visualization Needs of Decision Makers within Organizations

Publication date

2022-12-01

Authors

Dimara, EvanthiaORCID 0000-0001-5212-7888ISNI 0000000506363504
Zhang, Harry
Tory, Melanie
Franconeri, Steven

Editors

Advisors

Supervisors

Document Type

Article
Open Access logo

License

taverne

Abstract

When an organization chooses one course of action over alternatives, this task typically falls on a decision maker with relevant knowledge, experience, and understanding of context. Decision makers rely on data analysis, which is either delegated to analysts, or done on their own. Often the decision maker combines data, likely uncertain or incomplete, with non-formalized knowledge within a multi-objective problem space, weighing the recommendations of analysts within broader contexts and goals. As most past research in visual analytics has focused on understanding the needs and challenges of data analysts, less is known about the tasks and challenges of organizational decision makers, and how visualization support tools might help. Here we characterize the decision maker as a domain expert, review relevant literature in management theories, and report the results of an empirical survey and interviews with people who make organizational decisions. We identify challenges and opportunities for novel visualization tools, including trade-off overviews, scenario-based analysis, interrogation tools, flexible data input and collaboration support. Our findings stress the need to expand visualization design beyond data analysis into tools for information management.

Keywords

Data analysis, Data visualization, Decision making, Organizations, Task analysis, Tools, Uncertainty, business intelligence, interview, management, organizations, survey, visualization, Taverne, Software, Signal Processing, Computer Vision and Pattern Recognition, Computer Graphics and Computer-Aided Design

Citation

Dimara, E, Zhang, H, Tory, M & Franconeri, S 2022, 'The Unmet Data Visualization Needs of Decision Makers within Organizations', IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 12, pp. 4101 - 4112. https://doi.org/10.1109/TVCG.2021.3074023