Pedagogical technology for the formation of analytical competence in future economists through bi tools

Abstract

Abstract: Purpose. The purpose of this article is to scientifically substantiate, develop, and experimentally verify a pedagogical technology for forming the analytical competence of future economists through the integration of Business Intelligence (BI) tools into the content and process of professional training. The relevance of the study is conditioned by global challenges of digital transformation, increasing demands on economic professionals, and the urgent need to ensure the quality of higher education through the implementation of modern digital tools in the learning process. Methods. The research applied a complex of interrelated methods, including theoretical (literature analysis, modeling, systematization), empirical (observation, testing, surveys, self-assessment), and experimental methods (implementation of the pedagogical technology into educational practice). A four-stage pedagogical model was constructed, consisting of motivational-diagnostic, theoretical-conceptual, practical-operational, and summative-reflective phases. The core technological instruments included BI platforms such as Qlik Sense, Power BI, and Tableau, applied in student case projects, micro-analyses, and dashboard creation. Expected Results. It is anticipated that the implementation of the proposed pedagogical technology within the educational process of higher education institutions offering economics programs may contribute to a positive dynamic in the development of analytical competence. A statistically significant increase in the level of analytical skills in experimental groups is expected, which may be confirmed through comparative analysis methods. Additional anticipated effects include enhanced motivation for analytical activities, increased learner autonomy, the development of critical thinking skills, and more effective use of digital analytical tools. Conclusions. The proposed pedagogical technology has proven to be effective, adaptive, flexible, and scalable. It ensures the comprehensive integration of theory and practice, enhances the quality of professional training in the context of the digital economy, and creates conditions for interdisciplinary convergence in education. Future research directions include adapting the model to other academic specializations and expanding its capabilities through the incorporation of artificial intelligence and advanced analytics. Keywords: analytical competence, Business Intelligence, economics education, pedagogical technology, Qlik Sense, Power BI, Tableau, dashboard, data literacy, digital skills, applied analytics.