Implementasi Data Warehouse Untuk Integrasi Data Akademik Perguruan Tinggi - Varian Maulana Putra Nursawa; Supardi Supardi; Anisah Anisah (Jurnal Minfo Polgan, 2026)
Academic data within higher education institutions is generally scattered across various operational systems, such as academic information systems, new student admissions systems, learning management systems, financial systems, and accreditation repositories. This situation often leads to data redundancy, inconsistent reporting, delays in academic monitoring, and limited support for strategic decision-making. This research aims to design and describe the implementation of a data warehouse model to integrate academic data within the higher education environment. The research methodology employs a design science approach supported by dimensional modelling and extract-transform-load procedures. The proposed model integrates data on students, lecturers, courses, curricula, registration, grades, attendance, and graduation into a centralised analytical repository. The implementation design was carried out through five main stages: requirements analysis, data source mapping, data staging and cleansing, dimensional schema development, and dashboard-based reporting. The research results comprise a prototype architectural design consisting of staging tables, a single main academic fact table, supporting fact tables, and several configured dimensions, including student, study programme, course, semester, lecturer, and academic status dimensions. The discussion indicates that a data warehouse can improve data consistency, accelerate the preparation of academic reports, support the provision of accreditation evidence, and strengthen academic performance monitoring. This study concludes that the implementation of a data warehouse is a strategic solution for transforming fragmented academic data into integrated, historical, and decision-oriented information. Future research should evaluate the model using actual institutional data, measure query performance, and develop an architecture for academic predictive analytics.