Multimodal business analytics: The concept and its application prospects in economic science and practice

Abstract

One of the problems of business analysis is obtaining and processing an ever-increasing volume of economic, financial, organizational, political and legal data. Multimodal business analytics is a new methodology combining the methods of classical business analysis with big data technologies, intelligent business analytics, multimodal data fusion, artificial neural networks and deep machine learning. The purpose of the study is to determine the conceptual foundations of the phenomenon of multimodal business analytics and substantiate the prospects for its use in economic science and practice. Methodologically, the study rests on the systems approach, i.e., multimodal business analytics is examined as a unique integrated phenomenon comprised of several interrelated components. The evidence base covers research studies of 2000–2022 on multimodal business analytics from Scopus and the Russian online database eLibrary.ru. Empirical methods were used to collect and evaluate the dynamics of the number of relevant publications and their segmentation by subject areas. We have proposed own thesaurus and ontology of the key terms that make up the phenomenon of multimodal business analytics. It is shown that the use of the concept allows expanding the range of data, exposing hidden interrelations of organizational and economic phenomena and synthesizing fundamentally new information needed for effective decision-making in business.

Keywords

• multimodal business analytics
• business analysis
• data mining
• data fusion
• neural networks
• machine learning