Machine learning, neural networks

Classes of big data and their applicability for strategic analysis tasks

I. F. Kuzminov^a, G. N. Kuzmin<sup>b

a</sup> National Research University Higher School of Economics, Moscow, Russia
^b MIREA — Russian Technological University, Moscow, Russia

Abstract: This article examines classes of big data distinguished by the applied criterion of the metasource of origin. This criterion enables organizations to develop targeted data acquisition strategies and flexibly adapt data management approaches to support their analytical activities. The discussion begins with an analytical description of the category “big” as applied to data during the revolutionary development of artificial intelligence technologies, followed by a brief overview of modern strategic analysis as a system of activities. The principles and criteria of the introduced classification of big data are described, and the concept of “meta-source” is defined through a comparative analysis with the notion of “source” using concrete business examples from applied activities in the field of intelligent data analysis. The applicability of different classes of big data is studied in terms of the benefits they provide and the limitations of their use in strategic analytics.

Keywords: big data, strategic analysis, data sources, meta-source, intelligent data analysis, text mining, data management, business process optimization, natural language processing technologies.

DOI: 10.14357/20718594250305