Optimizing Decision Making in MSMEs through Business Intelligence Dashboards using Python and Power BI
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Azka Raisa Subagio
*
UIN Sunan Kalijaga, Yogyakarta,
Indonesia
(*) Corresponding Author
Abstract
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.
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References
Affairs, M. of C. for E. (2025). Pemerintah Dorong UMKM Naik Kelas, Tingkatkan Kontribusi terhadap Ekspor Indonesia. Ekon.Go.Id. https://www.ekon.go.id/publikasi/detail/6152/pemerintah-dorong-umkm-naik-kelas-tingkatkan-kontribusi-terhadap-ekspor-indonesia
Anjaningrum, W. D., Azizah, N., & Suryadi, N. (2024). Spurring SMEs’ performance through business intelligence, organizational and network learning, customer value anticipation, and innovation - Empirical evidence of the creative economy sector in East Java, Indonesia. Heliyon, 10(7), e27998. https://doi.org/10.1016/j.heliyon.2024.e27998
Baig Mirza, J., Hasan, M., Hassan, A., Paul, R., Rakibul Hasan, M., Khan, N., & Islam Asha, A. (2025). AI-Driven Business Intelligence in Retail: Transforming Customer Data into Strategic Decision-Making Tools. Advanced International Journal of Multidisciplinary Research, 3(1), 1–22. https://doi.org/10.62127/aijmr.2025.v03i01.1123
Chen, H., & Storey, V. C. (2026). B Usiness I Ntelligence And A Nalytics : F Rom B Ig D Ata To B Ig I Mpact. 36(4), 1165–1188.
Fabian, A. A., Uchechukwu, E. S., & Blessing, E. O. (2024). Business Intelligence and Decision-Making in Micro Small and Medium Enterprises in Africa. Scholars Journal of Economics, Business and Management, 11(04), 124–133. https://doi.org/10.36347/sjebm.2024.v11i04.003
Gonçalves, C. T., Gonçalves, M. J. A., & Campante, M. I. (2023). Developing Integrated Performance Dashboards Visualisations Using Power BI as a Platform. Information (Switzerland), 14(11). https://doi.org/10.3390/info14110614
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., Black, W. C., & Anderson, R. E. (2019). Multivariate Data Analysis Multivariate Data Analysis (8th ed.). CENGAGE.
Kenneth C. Laudon, J. P. L. (2022). Management Information Systems: Managing the Digital Firm. https://books.google.co.id/books/about/Management_Information_Systems.html?hl=id&id=SZSpxAEACAAJ&redir_esc=y
Mahmud, D., & Ikbal, M. Z. (2024). Power BI And Data Analytics In Financial Reporting_ A Review Of Real-Time Dashboarding And Predictive Business Intelligence Tools. International Journal of Scientific Interdisciplinary Research, 125–127.
Nguyen, G. T., Liaw, S. Y., & Duong, X. L. (2022). Readiness of SMEs for Adopt Big Data: An Empirical Study in Vietnam. International Journal of Computing and Digital Systems, 12(1), 509–521. https://doi.org/10.12785/ijcds/120141
Ogunmokun, A. S., Balogun, E. D., & Ogunsola, K. O. (2024). Business intelligence dashboard optimization model for real-time performance tracking and forecasting accuracy. International Journal of Social Science Exceptional Research, 3(1), 201–208. https://doi.org/10.54660/ijsser.2024.3.1.201-208
Ragazou, K., Passas, I., Garefalakis, A., & Zopounidis, C. (2023). Business intelligence model empowering SMEs to make better decisions and enhance their competitive advantage. Discover Analytics, 1(1). https://doi.org/10.1007/s44257-022-00002-3
Raguseo, E., & Vitari, C. (2020). Enabling innovation in the face of uncertainty through IT ambidexterity: A fuzzy set qualitative comparative analysis of industrial service SMEs. International Journal of Information Management, 50, 363–376. https://doi.org/10.1016/j.ijinfomgt.2019.05.007
Rizi, Y. A., Dharma, F., Amelia, Y., & Prasetyo, T. J. (2023). Factors Affecting Trust and Interest in Transactions By Indonesian Msme Sellers in E-Commerce. Journal of Indonesian Economy and Business, 38(1), 19–42. https://doi.org/10.22146/jieb.v38i1.4394
Ruansyah, M. A., Hadi, M. Z., Iqbal, M., Juniwati, J., & Aulia, L. (2024). Designing an analytics dashboard for knowledge extraction in the retail industry using descriptive and predictive analytics. Journal Industrial Servicess, 10(2), 242. https://doi.org/10.62870/jiss.v10i2.27900
Sanabia-Lizarraga, K. G., Carballo-Mendívil, B., Arellano-González, A., & Bueno-Solano, A. (2024). Business Intelligence for Agricultural Foreign Trade: Design and Application of Power BI Dashboard. Sustainability (Switzerland), 16(21). https://doi.org/10.3390/su16219576
Siljanoska, T., Savoska, S., & Jolevski, I. (n.d.). Integrating Python Into Power BI for Analyzing and Predicting Digital Development : Case Study – Balkan Countries. 4523, 55–69.
Square, D. (2025). The Ultimate Microsoft Power BI Guide for SMEs (2025). Dynamic Square. https://www.dynamicssquare.com/guides/power-bi-guide-for-sme/?utm_source=chatgpt.com
Truong, N. X. (2022). Factors Affecting Big Data Adoption: An Empirical Study in Small and Medium Enterprises in Vietnam. International Journal of Asian Business and Information Management, 13(1), 1–21. https://doi.org/10.4018/IJABIM.315825
Yusuf Iskandar, Heliani, Jaman, U. B., & Ardhiyansyah, A. (2023). Analyzing the Relationship Between Technology Adoption and Business Performance the Digital Age in SMEs in Indonesia. Eastasouth Proceeding of Nature, Science, and Technology (EPNST). https://doi.org/https://doi.org/10.58812/asst.v1i01.6
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