BUSINESS INTELLIGENCE AS A CATALYST FOR BETTER DECISION-MAKING IN MSMEs

Author

Nikhil Kiran Patil


Abstract

 The increasing availability of digital information has changed the way organizations understand their business environment and make managerial decisions. Micro, Small and Medium Enterprises (MSMEs), despite operating with comparatively limited financial, technological and human resources, generate substantial amounts of information through sales, purchasing, inventory, finance, customers, suppliers, employees and digital channels. The challenge for many MSMEs is not the absence of information but the ability to transform dispersed information into meaningful insights that can support timely managerial action. Business Intelligence (BI) provides an analytical framework through which business data can be collected, integrated, interpreted and presented to decision-makers. This paper theoretically examines the role of Business Intelligence in strengthening everyday and strategic decision-making in MSMEs. Drawing on established and recent literature, the paper discusses the evolution of BI from conventional reporting toward dashboards, cloud analytics, predictive analytics and intelligent decision-support environments. Particular attention is given to the application of BI in sales, inventory, purchasing, finance, customer management, marketing, supplier management and strategic planning. The paper argues that the contribution of BI to MSME decision-making is not limited to technological automation. Rather, BI can improve managerial visibility, information accessibility, analytical understanding, responsiveness and organizational learning. At the same time, data quality, analytical capability, technological readiness, organizational culture and managerial interpretation remain important conditions for realizing BI value. The paper proposes a conceptual perspective in which BI functions as a bridge between business data, managerial understanding and organizational action. The discussion contributes to the theoretical understanding of how accessible and appropriately used BI can support more informed decision-making in MSMEs.



Keywords

Business Intelligence; MSMEs; Decision-Making; Business Analytics; Data-Driven Decision-Making; Decision Support; Digital Transformation



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References


  1. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188. https://doi.org/10.2307/41703503
  2. Işık, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13–23. https://doi.org/10.1016/j.im.2012.12.001
  3. Llave, M. R. (2017). Business intelligence and analytics in small and medium-sized enterprises: A systematic literature review. Procedia Computer Science, 121, 194–205. https://doi.org/10.1016/j.procs.2017.11.027
  4. Llave, M. R. (2019). A review of business intelligence and analytics in small and medium-sized enterprises. International Journal of Business Intelligence Research, 10(1), 19–41. https://doi.org/10.4018/IJBIR.2019010102
  5. Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729–739. https://doi.org/10.1016/j.dss.2012.08.017
  6. Tsiu, S. V., Mathabela, L., Ngobeni, M., & Thango-Mabizela, B. (2025). Applications and competitive advantages of data mining and business intelligence in SMEs performance: A systematic review. Businesses, 5(2), 22. https://doi.org/10.3390/businesses5020022
  7. Bianchini, M., & Michalkova, V. (2019). Data analytics in SMEs: Trends and policies. OECD SME and Entrepreneurship Papers, No. 15. OECD Publishing. https://doi.org/10.1787/1de6c6a7-en
  8. Eggert, M., & Alberts, J. (2020). Frontiers of business intelligence and analytics 3.0: A taxonomy-based literature review and research agenda. Business Research, 13, 685–739.
  9. Mekimah, S., Zighed, R., Mili, K., & Bengana, I. (2024). Business intelligence in organizational decision-making: A bibliometric analysis of research trends and gaps. Discover Sustainability, 5, 532.

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