Data-Imagined Decision Making in Organizations: Do Visualization Tools Run in the Family?

Angela Locoro () and Aurelio Ravarini ()
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Angela Locoro: Università Carlo Cattaneo—LIUC
Aurelio Ravarini: Università Carlo Cattaneo—LIUC

A chapter in Digital Business Transformation, 2020, pp 63-76 from Springer

Abstract: Abstract ThisLocoro, Angela paper reports of an experimental crossover betweenRavarini, Aurelio two different perspectives of organizational activities: decision making and data management. Although there are ever growing contact points between the two, it is also true that in enterprises data-driven decision making often shows many room for improvements. A converging direction of these two aspects of organizational routine could be that of comparing and coupling decision making steps, activities and characteristics with data visualization properties, capabilities and enablers of information sharing and assimilation. This study goes in this direction, by proposing an exploratory analysis of decision making models and data visualization characteristics in order to extract a set of common aspects of decision making and to configure a set of connections between them and data visualization tools features. These connections may serve to investigate the strength of synergies between decision making activities and data management visualization, their effectiveness for data-driven decision making and the margin of improvements with respect to the current decision routines in enterprises. This study contributes to set the terrain for making a clearer picture of the strengths and weaknesses of data-driven decision making, to find implications for design of data visualization tools for supporting decision making activities, and to provide indications of how proactively data visualization toolboxes should run in the family at all decision levels and for each role in organizations.

Keywords: Data visualization; Decision making; Data visualization features; Decision making models (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-47355-6_5

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DOI: 10.1007/978-3-030-47355-6_5

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