Educational administration is increasingly relying on data-driven approaches to improve decisionmaking processes and optimize institutional performance. This study explores the role of predictive analysis in enhancing administrative functions such as student performance evaluation, resource allocation, and dropout risk identification. By leveraging data analytics, patterns and trends can be extracted from institutional datasets to provide actionable insights for administrators. The research highlights the effectiveness of predictive models in supporting evidence-based decisions, leading to improved efficiency, transparency, and accountability within educational institutions. The findings suggest that predictive analysis not only strengthens administrative decision-making but also contributes to long-term strategic planning in the education sector.
Data-driven decision making, educational administration, predictive analysis, student performance, resource allocation, dropout risk, data analytics, institutional efficiency, evidence based decisions, strategic planning