GENERALIZED PRINCIPLE FOR SYNTHESIZING INFORMATION TECHNOLOGY FOR INTELLIGENT ANALYSIS OF SOCIO-ECONOMIC DATA OF TERRITORIAL COMMUNITIES

Abstract

In conditions of rapid socio-economic changes and crisis situations, such as a pandemic or war, there is a need to develop effective information technologies for analyzing data of territorial communities (TC). This is due to the need to increase the resilience of the socio-economic infrastructure of communities and ensure its adaptation to the challenges of the time. The generalized principle of information technology synthesis of intelligent analysis of socio-economic data of TC is an important step towards the integration of heterogeneous data, the formation of adaptive approaches to their analysis and the adoption of informed management decisions.
Currently, an urgent problem is ensuring the effective analysis of socio-economic data of territorial communities (TCs), which are characterized by heterogeneity, dynamism, and large volumes. A review of existing approaches has shown that current methods are typically focused on analyzing only one type of data (structured, unstructured, or semi-structured) and do not consider the dynamic nature of socio-economic processes, reducing the efficiency of decision-making. Therefore, the overall aim of this research is to develop an information technology that integrates heterogeneous data, adapts to changes in socio-economic processes, and supports strategic decision-making in TCs. The aim of this study is to propose generalized principles for synthesizing such technology and to improve classification analysis methods for quantitative and textual data. The approach developed in this article enables the integration of structured, unstructured, and semi-structured data, adapts analytical methods to the specifics of socio-economic processes, and reduces decision-making time from days to hours. Additionally, the improved methods for classification analysis of quantitative and textual data enhance the accuracy of indicator analysis and the identification of patterns to support managerial decisions.