Context: Higher education institutions (HEIs) increasingly require integrated, reliable, and real-time data to support quality assurance and accreditation processes. However, fragmented information systems hinder institutional evaluation and decision-making. Objective: Design, implement, and evaluate a data integration framework that consolidates critical indicators for strategic monitoring and accreditation support in HEIs. Method: The study followed the Design Science Research (DSR) methodology, integrating Kimball’s dimensional modeling approach. A functional framework was deployed in a public Ecuadorian university. Results: The framework successfully integrated multiple institutional systems through ETL processes into a centralized datamart, enabling automated dashboard generation via Power BI. The solution aligned with national accreditation standards defined by CACES and focused on quantitative indicators. Interviews with institutional stakeholders confirmed high acceptance, reduced reporting times, and improved access to reliable and contextualized data. Conclusions: The implemented framework addressed key limitations in institutional information management and proved effective in supporting accreditation-related decision-making. Its modular design allows for the future integration of predictive analytics and adaptation to other HEIs facing similar challenges.