<![CDATA[Academic quality assurance (QA) in higher education institutions (HEIs) typically relies on conducting student surveys every semester to evaluate lecturer performance. However, the data processing in the case study remains manual in Microsoft Excel. Data processing results in 3 to 4 days of reporting delay per cycle. There are issues with data inconsistency, duplication, and non-automated mapping of questions into variables. This study aims to develop a business intelligence (BI) dashboard using Power BI that supports data-driven academic QA. The system was developed using human-centered design thinking and the four-step Kimball data warehousing methodology. The resulting dashboard comprises six main pages incorporating Pythonbased sentiment analysis. This dashboard includes interactive filtering and automated mapping of question variables. Evaluation through task-based usability testing yielded a 100% task completion rate, an average Single Ease Question score of 6.08 out of 7, and 100% data validity within a ±0.01 tolerance. In addition, all 43 visuals load in under 500 milliseconds. Theoretically, this study contributes a validated socio-technical BI framework demonstrating that user-participatory development has implications for organizational culture and decision-making in vocational HEIs. Practically, this dashboard eliminates manual reporting bottlenecks and enables stakeholders to monitor lecturer performance and generate reports more efficiently.]]>
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