From Data to Decisions: A Technical-Analytic Framework for University Growth Based on BI, AI and Data Governance
Abstract
As digital transformation accelerates, higher education institutions face mounting pressure to prove their impact through performance and innovation. This paper bridges a critical gap in current research: the lack of a unified, growth-oriented architecture that integrates business intelligence, AI, and data governance, fields typically treated in isolation. Adopting a design-science methodology, the study proposes a technical-analytic framework that aligns data infrastructure with five strategic dimensions: student success, teaching quality, research impact, financial sustainability, and internationalization. The resulting five-layer model, ranging from data integration to strategic decision-making, demonstrates how analytical capabilities drive measurable growth. Crucially, it embeds ethical safeguards and regulatory compliance (GDPR) directly into the architecture, fostering a robust culture of evidence-based decision-making. The study provides university administrators and IT leaders with a clear roadmap for strategic investment, while signaling to policymakers the need to integrate governance and ethical safeguards directly within analytical architectures, rather than alongside them. By delivering an operationally specified model that bridges the divide between digital strategy and technical implementation, this paper offers a high-quality, replicable template for modern institutional development.
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