• Rosilavi Mat Jusoh Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pahang, Malaysia
  • Zetty Ain Kamaruzzaman Faculty of Industrial Management, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600 Pahang, Malaysia
  • Galuh Tresna Murti School of Economics and Business, Telkom University, Bandung, West Java, Indonesia




Cross Industry Standard Process for Data Mining (CRISP-DM), Microsoft Power BI, Lifelong Learning, Data-Driven Decision Making


The purpose of this study was to develop a dashboard with information visualisation for the lifelong learning (LLL) programs at Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) and provide recommendations for UMPSA to improve the strategies and decision-making for future LLL program offerings. This study used the Cross Industry Standard Process for Data Mining (CRISP-DM) model created by the International Business Machines Corporation (IBM) and dashboard development using Microsoft Power BI. The updated database was created by obtaining data from the Institute of Graduate Studies (IPS), UMPSA. Such data was then prepared through the process of selecting, cleaning, constructing, integrating, and formatting data. The dashboard was developed using a database for data visualisation and information in the form of reports. Such information can ensure a better decision-making process and the UMPSA management’s readiness in planning the admission process for future students. However, the data management and data quality at UMPSA must be improved. Future research can expand the present investigation by including data from all categories of LLL students at UMPSA to facilitate a more comprehensive analysis. It will also enable the university management to make accurate decisions about program offerings in the future.


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How to Cite

Mat Jusoh, R., Kamaruzzaman, Z. A., & Murti, G. T. (2024). ENHANCING LIFELONG LEARNING: A DATA-DRIVEN DASHBOARD APPROACH FOR STRATEGIC DECISION-MAKING AT UNIVERSITI MALAYSIA PAHANG AL-SULTAN ABDULLAH. International Journal of Industrial Management, 18(2), 118–131. https://doi.org/10.15282/ijim.18.2.2024.10836



Research Article