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In the beginning, it is necessary to explain what dashboards are. They are “visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily drilled in and further explored” (Sharda et al., 2018, p. 117). These elements should be designed in a particular manner to convey information effectively. The most important requirement is to display all the needed data on a single screen (Sharda et al., 2018). There should be a balance between visualization and text tables. In particular, graphs and pictures should be supported with words and figures to explain their meaning. A practical tip is to place numbers in context to contribute to a better understanding of their meaning. Consequently, this information demonstrates that managers should draw careful attention while creating business intelligence dashboards.
In addition to that, specific conditions can demonstrate when it is necessary to group dashboards and when it is not appropriate. On the one hand, different dashboards should be grouped when they are relatively simple and represent related analyses and visualizations (Haymond, 2022). For example, if two dashboards are simple and focus on the profits and expenses of a single company, it could be rational to group them. On the other hand, not all visuals are simple, meaning that some of them can display many groups of information. For instance, a single dashboard can reveal margin, expenses, revenues, sales distribution, and others (Sharda et al., 2018). That is why it is not reasonable to group such visuals because it will be challenging for the target audience to understand them. Furthermore, it is not appropriate to group dashboards that do not have any related information. For instance, the sales volumes of one firm should not be displayed together with the expenses of another organization.
References
Haymond, S. (2022). Create laboratory business intelligence dashboards for free using R: A tutorial using the flexdashboard package. Journal of Mass Spectrometry and Advances in the Clinical Lab, 23, 39-43. Web.
Sharda, R., Delen, D., Turban, E., Aronson, J. E., Liang, T.-P., & King, D. (2018). Business intelligence, analytics, and data science: A managerial perspective (4th ed.). Pearson.
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