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Multi-domain Business Intelligence Model for Educational Enterprise Resource Planning Systems

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Innovative Systems for Intelligent Health Informatics (IRICT 2020)

Abstract

The process of applying multi-domains business intelligence for educational Enterprise Resource Planning (ERP) to obtain valuable, analytic, and predictive data still represents a big challenge for educational institutions. This in turn means that the educational institutions, in which the ERP system are implemented, are still lacking analytical, predictive, and reporting functions that improve decision-making. The aim of this research is to design an Educational Multi-Domain Business Intelligence (EduMdBI) model for ERP systems in higher education institutions, which provides better benefits to the students, lecturers, decision-makers, and universities. EduMdBI consists of three major business intelligence domains: Educational Business Intelligence (EduBI) to produce various types of analytic and predictive reports related to academic data, Financial Business Intelligence (FBI) to enable the decision-makers to obtain the required financial analytic and predictive reports, and Performance Business Intelligence (PBI) to produce the required analytic and predictive reports not only related to staff performance but also to educational institution performance. This research concludes that the EduMdBI model can improve the process of obtaining valuable, analytic, and predictive data and help in making the right decision at the right time. Based on the EduMdBI model a Business Intelligence Application can be further designed and developed to include additional sub ERP modules and business intelligence domains.

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Abdullah, H., Taa, A., Mohammed, F. (2021). Multi-domain Business Intelligence Model for Educational Enterprise Resource Planning Systems. In: Saeed, F., Mohammed, F., Al-Nahari, A. (eds) Innovative Systems for Intelligent Health Informatics. IRICT 2020. Lecture Notes on Data Engineering and Communications Technologies, vol 72. Springer, Cham. https://doi.org/10.1007/978-3-030-70713-2_108

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