The tabular form of data presentation is suitable for storing and processing substantial sets of information. During the analysis phase, companies tend to use graphical representations of data, such as scatter diagrams, graphs, histograms, and bar charts. Graphic form facilitates the perception of information as a whole and demonstrates its features, trends, and anomalies, which are readable and understandable not only to the analyst but also to the final consumer.
Data visualization in the analysis process is not only used for attention grabbing but also for neatness and aesthetics of the business reports and strategic discussions. If companies are looking for patterns and credible results in the analysis process using data visualization, they should know in advance what should be displayed. Even more – they should know what emotions they want to evoke from the stakeholders and decision makers, which in the available data should impress them (Zhong et al., 2016).
Besides, if working visualizations are understandable to the person who is prepared for the perception of the data, it is based on the context of data and experience with it. The final data representations should be immersed in the context of an unprepared person.
In logistics, data visualization allows companies to capture large amounts of information, compress it, and re-arrange to make more concise. Likewise, it makes the perception of complex information more accessible, speeding up comparisons of values, and facilitating the detection of patterns in data (Zhu, Hoon and Teow, 2017). An essential feature of the visualized data is its persuasiveness, so it is crucial to avoid distortion of information in the visualization process. In a good data visualization, the clarity of the overall picture does not interfere with the perception of details (Shahabi et al., 2019). But the main thing that differentiates visualization as an infographic from the use of data visualization as an analysis technique is a clear message. If there is nothing in the data that companies want to show to the receiver, no visualization can make it attractive and functional.
As a part of data visualization, a dashboard is a document with concisely presented statistical data, reports, most often with elements of infographics. Dashboards are also called software interfaces, widgets, desktops of various operating systems. In some cases, this is beautifully designed information with many numbers (Zhu, Hoon and Teow, 2017). A carefully calibrated dashboard is a powerful tool that can be both beautiful and user-friendly. They are actively used everywhere.
For supply chain management, the dashboard gives a brief overview of everything: there can be data on suppliers’ performance, shipments’ quantity, transportation system information, dependencies between supply chain metrics (Kosara, 2016). There can be a significant amount of information, but at the same time, everything should be clear, because the numbers accompany the descriptions. Inevitably, the internal analyst always seeks to put everything on the shelves, or at least compare new and old indicators.
From this perspective, it can be stated that data visualization and dashboards are used to represent the company’s activities, strategic decisions and changes over time. Moreover, these powerful analytical tools allow the company to deliver a clear, concise, and accurate message to stakeholders and decision-makers by indicating necessary dependencies, correlations, and metrics. In return, the use of data visualization and dashboards may increase the reliability and readability of business reports and improve the overall readiness of the company to improve operations or decide over strategic objectives and interventions.
Reference List
Kosara, R. (2016) ‘Presentation-oriented visualization techniques’, IEEE Computer Graphics and Applications, 36(1), pp. 80–85.
Shahabi, V., Emami, M., Monnavari, M.S. and Ghods, F. (2019) ‘Design of a supply chain management dashboard: information sharing to mitigate the bullwhip effect’, Uncertain Supply Chain Management, pp. 33–42.
Zhong, R.Y., Newman, S.T., Huang, G.Q. and Lan, S. (2016) ‘Big Data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives’, Computers & Industrial Engineering, 101(November), pp. 572–591.
Zhu, Z., Hoon, H.B. and Teow, K.-L. (2017) ‘Interactive data visualization techniques applied to healthcare decision making’, Decision Management, 1, pp. 1157–1171.