INTELLECTUAL ANALYTICAL SYSTEMS: MATHEMATICAL FOUNDATIONS AND PRACTICAL ASPECTS OF APPLICATION IN BUSINESS

DOI: 10.31673/2415-8089.2026.013809

Abstract

The rapid development of digital technologies and the continuous
growth of data volumes significantly transform the functioning of modern enterprises and economic
systems. In such conditions, the ability to effectively process, analyze and interpret large datasets
becomes a key factor in ensuring competitiveness and sustainable business development. Intelligent
analytical systems play a crucial role in this process by integrating mathematical models, artificial
intelligence algorithms and advanced data processing technologies.
The relevance of this research is determined by the increasing demand for analytical tools capable
of supporting decision-making processes in complex and dynamic business environments. Modern
organizations generate vast amounts of information through digital platforms, financial transactions, customer interactions and operational processes. The effective use of this information requires the
development of intelligent analytical solutions based on mathematical modeling and machine
learning approaches.
The article focuses on the conceptual foundations of intelligent analytical systems and their role
in business management. Particular attention is given to the mathematical methods and algorithms
that form the basis of data analysis, including statistical modeling, predictive analytics and machine
learning techniques. The study also highlights the growing importance of big data technologies and
digital platforms that enable companies to analyze complex datasets and extract valuable insights for
strategic planning.
The development of intelligent analytical systems contributes to improving the quality of
managerial decisions, optimizing business processes and increasing the efficiency of enterprise
management. Such systems are widely applied in various areas of business activity, including
marketing analytics, financial risk assessment, logistics management and customer behavior
analysis. By combining mathematical modeling with modern digital technologies, organizations are
able to gain a deeper understanding of market trends and develop more effective business strategies.
The research emphasizes the importance of further development of analytical technologies and
their integration into enterprise management systems. The implementation of intelligent analytical
solutions allows companies to enhance their adaptability to changing economic conditions and create
new opportunities for innovation-driven business growth.
Keywords: intelligent analytical systems, data analytics, business intelligence, artificial
intelligence, big data, mathematical modeling.

References
1. Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics. MIS
Quarterly, 36(4), 1165-1188. https://doi.org/10.2307/41703503
2. Provost, F., & Fawcett, T. (2013). Data Science for Business. O’Reilly Media.
https://datapot.vn/wp-content/uploads/2023/12/datapot.vn-Data-Science-forBusiness.pdf?srsltid=AfmBOorFhsJNIdq3SYAIfCVmmGQaPnNYAmNccxJ03GrHjIOUHb79inD6
3. Russell, S., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach (4th ed.).
Pearson. http://lib.ysu.am/disciplines_bk/efdd4d1d4c2087fe1cbe03d9ced67f34.pdf.
4. Shmueli, G., Bruce, P., Yahav, I., Patel, N., & Lichtendahl, K. (2017). Data mining for
business analytics: concepts, techniques, and applications in R. Wiley.
5. Dotsenko, S. I. & Kharchenko, V. S. (Eds.). (2023). Intelligent cybernetic systems: evolution
of principles, theories and security technologies: monograph. Retrieved from: https://
dspace.library.khai.edu/xmlui/handle/123456789/5051 [In Ukrainian].
6. Tretiak, V., Holubnychyi, D., Kolomiitsev, O., Mehelbei, H., Voznyi, O., & Filipenkov, O.
(2020). Matematychna model ranhovoho pidkhodu. Collection of Scientific Papers ΛΌГOΣ, 116-
122. DOI: https://doi.org/10.36074/25.12.2020.v1.40 [In Ukrainian]