Distributed Data Systems for Data Driven Decision Making in Education Management
Abstract
The ongoing digital transformation processes have an impact on the efficiency and effectiveness of data-driven decision-making in education management. There is a significant research gap in understanding those challenges and finding innovative solutions to them. The aim of this work is to analyze the influence of distributed data system integration factors on decision-making accuracy in educational institutions. Based on a review of the existing literature and Analytic Hierarchy Process (AHP) and Natural Language Processing (NLP) methods, we identify the drivers of educational performance as well as the systematic impacts of distributed data analytics on these drivers. The results of the empirical analysis confirmed the hypothesis that to enhance decision-making efficiency in education management, measures should be taken aiming at optimizing data integration, reducing information silos, supporting data accessibility and interoperability, developing advanced analytical frameworks to provide real-time insights and evidence-based strategies. The paper finishes with practical recommendations and policy implications for educational administrators and decision-makers. Thus, integrating distributed data systems boosts the education management framework by helping to streamline operations, enhance predictive analytics, and facilitate data-driven interventions with increased institutional adaptability.