Visual Analytics of Business Environment Data: Principles of Selection and Functional Capabilities of Graphical Methods

This article examines the role of visual analytics in processing and interpreting business environment data amid growing information volumes and increasingly complex management tasks. It is substantiated that the effectiveness of graphical data presentation depends not only on the capabilities of advanced analytical tools but, above all, on the extent to which the selected graphical method aligns with the characteristics of data and the analytical objective.The methodological framework of the study draws on general scientific and specialized methods, including systems analysis, classification, systematization, comparison, logical analysis, and graphical methods. A systematization of graphical methods resulted in the selection of six main functional groups: analysis of dynamics, comparison of values, structural analysis, identification of changes and deviations, distribution analysis, and correlation analysis.A systematic sequence for selecting a graphical method is substantiated according to the following logic: “data characteristics – analytical task – functional objective – graphical method.” The key factors underlying informativeness and accuracy of visual presentation are identified: the alignment of scale, visual encoding, the number of graphical elements, and the detailing of analytical task.The proposed comprehensive approach to classifying graphical tools enables to minimize cognitive distortions in information interpretation, to ensure high accuracy in managerial decision-making, to enhance the overall quality of analytical support for business processes, to optimize communication across management levels, and to facilitate the effective digital transformation of enterprises.The practical significance of the study lies in the potential application of the proposed approach in preparing analytical reports, conducting KPI monitoring, designing management dashboards, and using other business analytics tools. Further research may focus on setting up the criteria for evaluation of visualization effectiveness, experimental assessment of their informativeness, and designing intelligent tools for computerized selection of graphical methods.