Big Data Analytics for Graduate Employability and Sustainable Development: Evidence from Global University Databases

Abstract

This study investigates the determinants of graduate employability and their alignment with the Sustainable Development Goals (SDGs), particularly SDG 4 (Quality Education), SDG 8 (Decent Work), and SDG 9 (Industry, Innovation, and Infrastructure). Using secondary data from QS Graduate Employability Rankings, Times Higher Education indicators, and World Bank Education Statistics (2019–2025), the research applies descriptive statistics, regression analysis, cluster analysis, and interpretable machine learning to identify institutional and contextual predictors of employability. The findings show that industry collaboration and innovation ecosystems are the strongest drivers of employability worldwide, while research intensity exerts conditional influence depending on economic context. Internationalization also contributes employability, but plays a secondary role. Machine learning models confirm the dominance of industry engagement as the most influential factor. By providing a data-driven and scalable framework, the study offers actionable insights for universities, policymakers, and employers seeking to enhance employability outcomes and advance sustainable development through higher education.