ENTERPRISE BUSINESS ANALYTICS AS A SYSTEM OF METHODS AND TOOLS FOR SUPPORTING MANAGERIAL DECISION-MAKING
Abstract
This study examines enterprise business analytics as a system of methods and tools for supporting managerial decision-making, specifically the methods and tools for collecting, processing, analysing, and visualising data on enterprise operations in order to make optimal managerial decisions. The main objectives of business analytics in organisations are outlined. These include supporting decision-making, identifying problems and opportunities, forecasting future trends, optimising resources and processes, monitoring and control, and providing an information framework for strategic planning. The main functions of business analytics in organisations are examined, specifically data collection and integration, data processing and preparation, data analysis, visualisation of results, supporting decision-making, monitoring key performance indicators, forecasting trends, as well as risk assessment and scenario modelling. The study suggests analysing business operations using descriptive analytics, real-time analytics, diagnostic analytics, predictive analytics, and prescriptive analytics as stages of business analytics. Enterprise business analytics methods are examined in terms of the following aspects: objectives, tools, scope of application, level of use, types of methods, advantages, and dependence on data. The essence of business analytics methods is shown to lie in the systematic application of scientific, mathematical, statistical, and digital approaches to data collection, processing, analysis, and interpretation in order to support informed managerial decision-making. The study examines the methods of descriptive analytics, real-time analytics, diagnostic analytics, predictive analytics, and prescriptive analytics, which help businesses identify trends and patterns in data, forecast future events or consumer behaviour, optimise business processes, and make effective strategic and operational decisions. The means of data collection, storage, processing, and transformation, analytical tools, predictive analytics tools, machine learning methods, and big data analysis are analysed. An algorithm for business analysis is offered.
References
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Ahmed, F. F., Alrowaiei, K. H., Alsubaiei, S. H. and Wadi, R. A. (2023) Impact of business analytics and enterprise system on managerial accounting. In Emerging Trends and Innovation in Business and Finance. Singapore: Springer Nature Singapore, pp. 839-851.
Akter, S., Akhter, S. R., Rahman, M. B. and Naidu, P. (2025) AI-driven business analytics for competitive advantage in service-oriented enterprises: CUSTOMER experience and efficiency. International Journal of Business and Economics Insights, vol. 5(3), pp. 389-423.
Al-Debei, M. M. (2024) The era of business analytics: identifying and ranking the differences between business intelligence and data science from practitioners’ perspective using the Delphi method. Journal of Business Analytics, vol. 7(2), pp. 94-119.
Al-Okaily, A., Teoh, A. P. and Al-Okaily, M. (2023) Evaluation of data analytics-oriented business intelligence technology effectiveness: an enterprise-level analysis. Business Process Management Journal, vol. 29(3), pp. 777-800.
Andrushko, A. (2025) Enhancing Decision-Making in Service SMEs: Role of Business Analytics in Management Approach Transformation. IUP Journal of Knowledge Management, vol. 23(1), pp. 52-72.
Bauskar, S. (2024) Business analytics in enterprise system based on application of artificial intelligence. International Research Journal of Modernization in Engineering Technology and Science, vol. 4(1), pp. 1861-1870.
Bayraktar, E., Tatoglu, E., Aydiner, A. S. and Delen, D. (2024) Business analytics adoption and technological intensity: An efficiency analysis. Information Systems Frontiers, vol. 26(4), pp. 1509-1526.
Cao, G. and Duan, Y. (2024) Exploring the impact of business analytics on strategic decision-making in uncertain environments. Journal of Management Analytics, vol. 11(4), pp. 577-600.
Chatterjee, S., Rana, N. P. and Dwivedi, Y. K. (2024) How does business analytics contribute to organisational performance and business value? A resource-based view. Information Technology & People, vol. 37(2), pp. 874-894.
Chowdhury, R. H. (2021) Cloud-based data engineering for scalable business analytics solutions: designing scalable cloud architectures to enhance the efficiency of big data analytics in enterprise settings. Journal of Technological Science & Engineering (JTSE), vol. 2(1), pp. 21-33.
Dereli, S. E. (2023) The role of business analytics in enabling competitive advantage for small and medium enterprises (Doctoral dissertation, Swinburne).
Iqbal, M. and Sakib, M. (2024) Integrating cloud computing, big data, and business analytics to determination digital transformation and competitive advantage in modern enterprises. Journal of Primeasia, vol. 5(1), pp. 1-8.
Ikbal, M. Z. (2025) A meta-analysis of AI-driven business analytics: Enhancing strategic decision-making in SMEs. Review of Applied Science and Technology, vol. 4(02), pp. 33-58.
Kalishina, D. (2023) Artificial Intelligence As An Enabler Of Growth: Advancing Business Analytics In Small And Medium Enterprises. International Journal of Core Engineering & Management, vol. 7(5), pp. 289-300.
Kumar, S. and Aithal, P. S. (2023) Tech-Business Analytics–a Review based New Model to Improve the Performances of Various Industry Sectors. International Journal of Applied Engineering and Management Letters (IJAEML), vol. 7(1), pp. 67-91.
Kumar, S. and Aithal, P. S. (2023) Tech-business analytics in primary industry sector. International Journal of Case Studies in Business, IT, and Education (IJCSBE), vol. 7(2), pp. 381-413.
Lee, C. S., Cheang, P. Y. S., and Moslehpour, M. (2022) Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, vol. 26(1), pp. 1-29.
Liu, Q. and Li, J. (2022) The progress of business analytics and knowledge management for enterprise performance using artificial intelligence and man-machine coordination. Journal of Global Information Management (JGIM), vol. 30(11), pp. 1-21.
Liu, S., Liu, O. and Chen, J. (2023) A review on business analytics: Definitions, techniques, applications and challenges. Mathematics, vol. 11(4), pp. 899.
Lösser, B. (2025) State of the art of business analytics for enterprise Transformation steering. Technology-Driven Transformation: The Future of Work and Organizations, pp. 289-305.
Marjanovic, O. (2022) A novel mechanism for business analytics value creation: improvement of knowledge-intensive business processes. Journal of Knowledge Management, vol. 26(1), pp. 17-44.
Riepina, I., Budiaiev, M., Nychyporuk, O., Yakusheva, N., Andriushchenko, A., Blyznyuk, A. and Mazur, Y. (2026) Assessment of the effectiveness of implementing AI tools in business analytics of enterprises in the conditions of digital change. Technology audit and production reserves, vol. 2(4 (88)), pp. 43-54.
Shiwakoti, S. (2025) Leveraging SAP FICO and Business Analytics for Financial Transparency and Decision Intelligence in Modern Enterprises. Journal of Artificial Intelligence General science (JAIGS), vol. 8(02), pp. 293-318.
Solano, M. C. and Cruz, J. C. (2024) Integrating analytics in enterprise systems: A systematic literature review of impacts and innovations. Administrative Sciences, vol. 14(7), pp. 138.
Valmohammadi, C., Sadeghi, M., Taraz, R. and Mehdikhani, R. (2024) Business analytics, corporate entrepreneurship, and open innovation. Management Decision, vol. 62(6), pp. 1977-2001.
Wee, M., Scheepers, H. and Tian, X. (2022) Understanding the processes of how small and medium enterprises derive value from business intelligence and analytics. Australasian Journal of Information Systems, vol. 26, pp. 1-26.
Wolniak, R. and Grebski, W. (2023) The five stages of business analytics. Silesian University of Technology Scientific Papers. Organization and Management Series, vol. 178, pp. 735-752.
Zamani, E. D. Griva, A., and Conboy, K. (2022) Using business analytics for SME business model transformation under pandemic time pressure. Information Systems Frontiers, vol. 24(4), pp. 1145-1166.
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