articles

Currently, digitalization processes are actively developing. Digital tools make it possible to strengthen a systematic approach to monitoring, analyzing and forecasting socio-economic indicators. The introduction of information and analytical systems (IAS) for the study of socio-economic processes improves the decision-making process in public administration and it is an urgent task. This paper outlines the principles and approaches to the analysis and forecast of socio-economic processes, implemented in the construction of an information-analytical system. The structure of the IAS includes a database of statistical data, an analytical subsystem and a subsystem for visualizing the results. The database is developed in the MS SQL Server program and it uses official statistical data posted on the websites of state statistics, the Federal Treasury of the Russian Federation, ministries and departments. It contains information on the total population and in the division by groups, on the population density in the distribution by age of inhabitants; fertility, mortality, natural population growth, migration; statistical data on human and production resources, gross regional product. The analytical subsystem of the IAS is developed in the Microsoft Visual Studio environment and implemented in the high-level C# programming language. It contains algorithms for analysis, modeling and forecasting indicators of regional socio-economic processes based on the integrated use of dynamic models of various types. Visualization and display of the results of the work of the IAS is given in the form of tables, graphs, diagrams, cartograms according to official statistical data of one of the regions of the Russian Federation – the Udmurt Republic. The developed IAS, unlike other information systems, analyzes not only indicators characterizing the state and dynamics of the socio-economic system, but also examines the structure of ongoing processes in terms of time, territory, and age of the objects under study. This system allows to quickly track changes in the dynamics and structure of socio-economic indicators and provide it to decision makers in a timely manner.