GENERAL ASSESSMENT OF THE PROSPECTS FOR THE APPLICATION OF THE BIG DATA PARADIGM IN SOCIO-ECONOMIC SYSTEMS

Abstract

The paper assesses the prospects for the application of the big data paradigm in socio-economic systems through the analysis of factors that distinguish it from the well-known scientific ideas of data synthesis and decomposition. The idea of extracting knowledge directly from big data is analyzed. The article compares approaches to extracting knowledge from big data: algebraic and multidimensional data analysis used in OLAP-systems (OnLine Analytical Processing). An intermediate conclusion is made about the advisability of dividing systems for working with big data into two main classes: automatic and non-automatic. To assess the result of extracting knowledge from big data, it is proposed to use well-known scientific criteria: reliability and efficiency. It is proposed to consider two components of reliability: methodical and instrumental. The main goals of knowledge extraction in socio-economic systems are highlighted: forecasting and support for making management decisions. The factors that distinguish big data are analyzed: volume, variety, velocity, as applied to the study of socio-economic systems. The expediency of introducing a universe into systems for processing big data, which provides a description of the variety of big data and source protocols, is analyzed. The impact of the properties of sample populations from big data: incompleteness, heterogeneity, and non-representativeness, the choice of mathematical methods for processing big data is analyzed. The conclusion is made about the need for a systemic, comprehensive, cautious approach to the development of fundamental decisions of a socio-economic nature when using the big data paradigm in the study of individual socio-economic subsystems.