Labour Market Analysis: Digitalization, Workforce Development, and Human Capital in the Big Data Economy

The paper studies the aspects of digitalization within the big data economy context regarding professional development; proposes a methodology for working with labour market data to formulate hypotheses, investigate them, and create various taxonomies, such as the labour market skills taxonomy, the digital transformation skills taxonomy. The work considers the main trends, prospects, and some social consequences that shape human capital and workforce potential, which are facilitated by the dynamism of communication tools and the simplification of market participants’ interactions. The paper addresses globalization, education, development of teaching methodologies and the EdTech market, acquisition of new skills, personnel management, information accessibility, its quality, employment forms, and digitalization in general, which is important for understanding the general situation in the labour market and exploring its novelty. The work applies the method of thematic modelling of required competencies and skills through latent Dirichlet allocation, which is visualized by a thematic distance map. Based on clustering principles, this method provides a detailed illustration of cases with a large amount of data and uncertainty, as cluster topics are not predetermined. The work aims to understand the connections between digitalization and social processes to create and maintain sustainable labour relations.