Optimizing Decision Making in MSMEs through Business Intelligence Dashboards using Python and Power BI - Azka Raisa Subagio (TIN: Terapan Informatika Nusantara, 2026)
Micro, Small, and Medium Enterprises in Indonesia play a vital role in national economic growth; however, many continue to rely on manual spreadsheet-based reporting and intuitive judgment, limiting the effectiveness and timeliness of data-driven decision making. This study aims to examine how Business Intelligence dashboards integrating Python and Power BI can enhance operational decision-making performance in Indonesian retail-sector micro, small, and medium enterprises. Using a quantitative descriptive approach, the study analyzes secondary data from the Grocery Store Sales Dataset (2025) obtained from the Kaggle open-source platform. A total of 1,980 transaction records were processed to simulate typical operational decision-making scenarios commonly faced by retail enterprises. In the baseline condition, decision making was conducted using conventional spreadsheet summaries without automated analytics or real-time visualization. Python was employed for data preprocessing, transformation, and key performance indicator computation, while Power BI was used to develop an interactive Business Intelligence dashboard. Descriptive statistical analysis and scenario-based simulations were conducted to compare decision-making efficiency and accuracy before and after dashboard implementation. The results indicate that the proposed Business Intelligence approach reduced average decision-making time by 36.36 percent, improved information accuracy by 41.18 percent, and accelerated strategic planning speed by 40 percent. These findings demonstrate that integrating Python-based analytics with Business Intelligence dashboards offers a low-cost, scalable, and effective solution to support data-driven managerial practices and strengthen the digital readiness of Indonesian micro, small, and medium enterprises.