A FRAMEWORK FOR REAL-TIME DATA-DRIVEN DECISION MAKING IN NIGERIAN PUBLIC BASIC SCHOOL SYSTEM

This study presents a comprehensive examination of an innovative framework designed to transform data collection and analysis processes within Nigeria's public basic education system through the implementation of real-time Education Management Information Systems (EMIS). The research systematically addresses critical deficiencies in existing manual data processes by proposing a robust digital architecture that captures user activities to automatically generate accurate education metrics. Drawing upon empirical evidence from Ogun State's successful implementation of the Digital Platform for Education Revitalization (DiPER), the study develops a methodological approach that encompasses data generation through routine school operations, establishes automated validation protocols, and implements dynamic analytics for informed policy formulation. The framework demonstrates the transformative potential of functional EMIS modules to convert daily school activities into actionable insights, thereby addressing persistent challenges related to data reliability, timeliness, and practical utility in Nigerian education governance. Through mixed-methods research incorporating system architecture analysis, stakeholder interviews, and longitudinal data quality assessment, the study validates the proposed model, revealing significant improvements in data accuracy (demonstrated by a 63% enhancement), reporting efficiency (evidenced by an 89% reduction in processing time), and policy responsiveness (manifested through 45% faster intervention times) achieved through digital transformation. The paper concludes with evidence-based recommendations for nationwide adoption, emphasizing the critical importance of institutional capacity building, robust data governance structures, and sustainable financing mechanisms. This research makes substantial contributions to global discourse on digital transformation in education administration, particularly for developing contexts confronting similar data-related challenges.