Building a smart economy: Training the workforce in multi-source data and AI-enhanced economic analysis

Abstract

In the new era of economic analysis, the integration of multi-source data—spanning official, open, and private sources—presents unprecedented opportunities to gain nuanced insights into economic indicators. Traditional economic analyses have predominantly relied on singular data sources, limiting the depth of understanding available to policymakers and analysts alike. This paper explores an advanced framework for economic analysis, combining data from national and international organizations, such as the National Institute of Statistics, Eurostat, UN, and World Bank, with supplementary open and private datasets. By leveraging AI-driven methods, we uncover complex relationships across diverse economic factors, enabling more robust, predictive insights for policy and economic planning. Our approach builds upon the "Analysis of Economic and Social Data" postgraduate program, a modular training platform designed to equip professionals in public administration and private sectors with essential analytical skills. The program’s curriculum encompasses data collection, preparation, statistical analysis, and visualization, taught through Excel, Power BI, R, and Python—ensuring graduates gain hands-on expertise in using the most advanced tools in data analysis. The gradual, applied learning structure of this program provides students with the competencies to conduct multi-dimensional economic analyses, moving beyond isolated datasets to a holistic view powered by AI. We demonstrate how this approach transforms economic analysis, enabling practitioners to tackle real-world challenges through comprehensive case studies and projects. This work underscores the potential of a smart economy powered by a workforce proficient in modern data techniques, fostering data-informed decision-making that aligns with contemporary economic and social demands.