Developing a Power BI Graduate Employability Dashboard: A Malaysian Public University Case Study

Graduate tracer data can provide institutions with evidence about employment and broader graduate outcomes, but their value depends on consistent preparation and interpretation. This study documents the development and institutional application of the Universiti Teknologi MARA (UiTM) Graduate Employability (GE) Impact Dashboard, a descriptive business-intelligence platform developed in Microsoft Power BI. The study adopts an institutional case-study and system-development design. Seven annual cohorts of Malaysia’s Graduate Tracer Study (Sistem Kajian Pengesanan Graduan, SKPG), covering 2019-2025, were validated, cleaned, harmonised and modelled for institutional analysis. The resulting dashboard supports monitoring of Graduate Employment (GE), Graduate Marketability (GM), qualification-appropriate skilled employment (GBSK), field-of-study alignment (BDB), Graduate Entrepreneurship (GU), salary, employers, employment sectors, selected graduate characteristics and graduate evaluations of educational experiences. Interactive filters allow users to examine trends and descriptive differences by year, level of study, faculty, campus and programme across more than 500 academic programmes, 27 faculties and 35 campuses. Dashboard results are also converted into faculty and campus report cards for leadership review, programme discussion and targeted follow-up. The latest cohort recorded 94.5% GE and 94.6% GM. These figures are reported as institutional outcomes and are not interpreted as evidence of a causal effect of the dashboard or as direct measures of teaching quality. The study’s contribution is an applied and transparent account of how existing national tracer data can be transformed into a common institutional decision-support environment. It demonstrates how descriptive analytics can connect multidimensional graduate-outcome evidence with programme monitoring, review and management action without relying on predictive modelling or artificial intelligence.

graduate tracer study; Power BI; business intelligence; higher education

1. Alkhwaldi, A. F., Abdulmuhsin, A. A., Masa’deh, R., & Abu-AlSondos, I. A. (2025). Business intelligence adoption in higher education: The role of data-driven decision-making culture and UTAUT. Journal of International Education in Business, 18(4), 484-504. https://doi.org/10.1108/JIEB-08-2024-0110 [Google Scholar] [Crossref]

2. Azmir, M. F. B. H., Abd Aziz, M. A. A. B., & Mohd Ariffin, M. R. B. (2026). Development of a graduate employability dashboard for community colleges. International Journal of Research and Innovation in Social Science, 10(26), 4631-4665. https://doi.org/10.47772/IJRISS.2026.1026EDU0361 [Google Scholar] [Crossref]

3. Basir, N. M., Zubairi, Y. Z., Jani, R., & Wahab, D. A. (2022). Soft skills and graduate employability: Evidence from Malaysian tracer study. Pertanika Journal of Social Sciences and Humanities, 30(4), 1975-1986. https://doi.org/10.47836/pjssh.30.4.26 [Google Scholar] [Crossref]

4. de Vreugd, L., van Leeuwen, A., Jansen, R., & van der Schaaf, M. (2024). Learning analytics dashboard design and evaluation to support student self-regulation of study behaviour. Journal of Learning Analytics, 11(3), 249-262. https://doi.org/10.18608/jla.2024.8529 [Google Scholar] [Crossref]

5. Haque, R., Quek, A., Ting, C. Y., Goh, H. N., & Hasan, M. R. (2024). Classification techniques using machine learning for graduate student employability predictions. International Journal on Advanced Science, Engineering and Information Technology, 14(1), 45-56. https://doi.org/10.18517/ijaseit.14.1.19549 [Google Scholar] [Crossref]

6. Kaspi, S., & Venkatraman, S. (2023). Data-driven decision-making for higher education assessments: A case study. Systems, 11(6), 306. https://doi.org/10.3390/systems11060306 [Google Scholar] [Crossref]

7. Ministry of Higher Education Malaysia. (2026). Sistem Kajian Pengesanan Graduan (SKPG) 2026. https://graduan.mohe.gov.my/skpg26/ [Google Scholar] [Crossref]

8. Moo, K. H., & Wan, C. D. (2023). Graduate employability in Malaysia: Unpacking the concept, policy and practices. IIUM Journal of Educational Studies, 11(2), 3-25. https://doi.org/10.31436/ijes.v11i2.471 [Google Scholar] [Crossref]

9. Murat, B. I. S., Ismail, M. H., Pakeer, A. S., & Saedan, N. (2026). From graduate data to institutional action: Malaysian evidence on integrated governance for graduate outcomes. Journal of Islamic, Social, Economics and Development, 11(86), 632-648. https://academicinspired.com/jised/article/view/4569 [Google Scholar] [Crossref]

10. Sarmiento, J. P., & Wise, A. F. (2022). Participatory and co-design of learning analytics: An initial review of the literature. In A. F. Wise, R. Martinez-Maldonado, & I. Hilliger (Eds.), Learning analytics for transition, disruption and social change: Proceedings of the 12th International Conference on Learning Analytics and Knowledge (pp. 535-541). ACM. https://doi.org/10.1145/3506860.3506910 [Google Scholar] [Crossref]

11. Sequeira, R., Reis, A., Alves, P., & Branco, F. (2024). Roadmap for implementing business intelligence systems in higher education institutions: Systematic literature review. Information, 15(4), 208. https://doi.org/10.3390/info15040208 [Google Scholar] [Crossref]

12. Sorour, A., & Atkins, A. S. (2024). Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards. Journal of Electronic Science and Technology, 22(1), 100233. https://doi.org/10.1016/j.jnlest.2024.100233 [Google Scholar] [Crossref]

13. Tee, P. K., Wong, L. C., Dada, M., Song, B. L., & Ng, C. P. (2024). Demand for digital skills, skill gaps and graduate employability: Evidence from employers in Malaysia. F1000Research, 13, 389. https://doi.org/10.12688/f1000research.148514.1 [Google Scholar] [Crossref]

14. Williamson, K., & Kizilcec, R. (2022). A review of learning analytics dashboard research in higher education: Implications for justice, equity, diversity, and inclusion. In Proceedings of the 12th International Conference on Learning Analytics and Knowledge (pp. 260-270). ACM. https://doi.org/10.1145/3506860.3506900 [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study