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Tools Aimed at Improving the Quality of Information Support of Management in Conditions of Digitalization

https://doi.org/10.21686/2413-2829-2022-2-82-92

Abstract

The article analyzes the term ‘digitalization’, identifies its specific features implying that this term, in spite of transformations due to complexity of tasks being resolved, does not lose its initial meaning. On the contrary, it was supplemented by new features that do not contradict to the primary definition but make it more topical in view of current objectives. On the basis of obtained results the term ‘information support of management’ was specified. At the present stage of society development the notion of information for providing optimal managerial decision should be understood as the information received by processing such volumes of data, which cannot be processed directly by person. The author shows the use of artificial neuron networks to provide the information support of management. The article substantiates that in order to improve the quality of management in the current conditions it is possible to use tools meant for analyzing big data. It is underlined that we can use data from different sources, internal and external ones. Reliability of these findings was demonstrated by processing the vacancy sample at enterprises of steel-making industry. The author confirmed an opportunity to improve the quality of information support of management through getting new information by using visualization of structured data.

About the Author

Yu. V. Zhurov
Plekhanov Russian University of Economics
Russian Federation

Yuriy V. Zhurov - Candidate of the Department for Organizational and Managerial Innovations

36 Stremyanny Lane, Moscow, 117997



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Review

For citations:


Zhurov Yu.V. Tools Aimed at Improving the Quality of Information Support of Management in Conditions of Digitalization. Vestnik of the Plekhanov Russian University of Economics. 2022;(2):82-92. (In Russ.) https://doi.org/10.21686/2413-2829-2022-2-82-92

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ISSN 2413-2829 (Print)
ISSN 2587-9251 (Online)