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Dimara, E., Zhang, H., Tory, M., & Franconeri, S. (2021). The unmet data visualization needs of decision makers within organizations. IEEE transactions on visualization and computer graphics. 
Resource type: Journal Article
BibTeX citation key: Dimara2021
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Categories: Artificial Intelligence, Cognitive Science, Complexity Science, Computer Science, Data Sciences, Decision Theory, Engineering, General
Subcategories: Augmented cognition, Behavioral analytics, Big data, Decision making, Deep learning, Human decisionmaking, Human factors engineering, Informatics, Machine learning, Psychology of human-AI interaction, Simulations
Creators: Dimara, Franconeri, Tory, Zhang
Collection: IEEE transactions on visualization and computer graphics
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.
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