Business Intelligence System for Lecturer Qualification Analysis: Optimizing ETL Workflows for KPI Extraction based on QOX

Sureena Matayong; Suttida Saebao; Numtip Trakulmaykee

Authors

Sureena Matayong Department of Interdisciplinary Engineering, Faculty of Engineering, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
Suttida Saebao Agricultural Innovation and Management Division, Faculty of Natural Resources, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.
Numtip Trakulmaykee Division of Computational Science, Faculty of Science, Prince of Songkla University, Hat Yai, Songkhla 90110, Thailand.

Keywords:

business intelligence, ETL, qualification analysis, QOX, TAM

Abstract

Evaluating lecturer qualifications is critical for ensuring program compliance with higher education standards in Thailand. This process involves data from multiple sources, leading to redundancy and inaccuracies that complicate data integration and quality, ultimately resulting in delays analysis and decision-making. Business Intelligence (BI) systems are essential for improving data integration and analysis, enabling institutions to make informed decisions through visualizations. However, the effectiveness of these systems relies on a robust Extract, Transform, and Load (ETL) workflow. This study introduces a Quality Objective Matrix (QOX) to evaluate the accuracy, completeness, scalability, and efficiency of the workflow. The methodology included analyzing the QOX criteria for workflow development and measurement. Then, the BI system was developed and evaluated using the Technology Acceptance Model (TAM). Results indicated that the ETL workflow achieved 99.99% accuracy, and completeness was measured at 85%. The processing demonstrate significant scalability and efficiency, saving approximately 3 hours and 19 minutes compared to manual methods. User satisfaction scores averaged 4.58 out of 5 with a standard deviation of 0.5 for information quality, perceived ease of use, and perceived usefulness. This study demonstrates that implementing a robust ETL process within a BI system can significantly improve compliance and operational efficiency in higher education institutions.

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