mgimobusinessМеждународный бизнесInternational Business2949-639XФонд поддержки образовательных инициатив «Новый взгляд»10.24833/2949-639X-2025-3-13-74-95mgimobusiness-130Research ArticleСтатьиПрименение методов интеллектуального анализа данных и машинного обучения для повышения эффективности управления в менеджментеApplication of Data Mining and Machine Learning Methods to Enhance the Effectiveness of Business Managementhttps://orcid.org/0000-0002-2598-1699ГутникС. А.GutnikS. A.

Гутник Сергей Александрович, доктор физико-математических наук, доцент, доцент кафедры математики, эконометрики и информационных технологий 

Москва

Sergey A. Gutnik, Doctor of Physical and Mathematical Sciences, Associate Professor, Associate Professor, Department of Mathematics, Econometrics and Information Technology

Moscow

s.gutnik@inno.mgimo.ruhttps://orcid.org/0009-0005-9153-0457ЗвягинцевМ. М.ZvyagintsevМ. М.

Звягинцев Михаил Михайлович, младший научный сотрудник Центра искусственного интеллекта 

Москва

Mikhail M. Zvyagintsev, Junior Researcher, Center for Artificial Intelligence

Moscow

m.zvyagintsev@inno.mgimo.ruМГИМО МИД РоссииРоссияMGIMO UniversityRussian Federation20250512202503(13)7495Copyright © Гутник С.А., Звягинцев М.М., 20252025Гутник С.А., Звягинцев М.М.Gutnik S.A., Zvyagintsev М.М.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ibj.mgimo.ru/jour/article/view/130

В статье раскрываются ключевые аспекты бизнес-аналитики, включая интеграцию данных и приложений, процессы трансформации информации в практические знания, а также разнообразные методы аналитической обработки. Определены отличительные характеристики концепции Big Data и технологий анализа больших данных. Проведена систематизация и сравнительная оценка современных подходов к интеллектуальному анализу информации с акцентом на их применение для решения управленческих задач и разработки бизнес-стратегий. Описана общая архитектура процессов цифровой обработки корпоративной информации. Особое внимание уделено функциональным возможностям предиктивной аналитики и систем прогнозирования. Представлен обзор лидирующих решений в области аналитики и машинного обучения на ИТ-рынке, а также перспективы их интеграции в цифровую экономику. Рассмотрены базовые подходы к обучению моделей в системах искусственного интеллекта и возможности их использования в различных сферах бизнеса. Исследуется специфика применения алгоритмов машинного обучения как составной части систем искусственного интеллекта, анализируются их преимущества и ограничения, а также оценивается потенциал использования данных технологий в современном менеджменте. Перечислены перспективные направления и функциональные возможности развития искусственного интеллекта для корпоративных решений, такие как агентные системы, автоматизированное машинное обучение и причинный искусственный интеллект.

This article elucidates the key aspects of business analytics, encompassing the integration of data and applications, the processes of transforming information into actionable insights, and a variety of analytical processing methods. It defines the distinctive characteristics of the Big Data concept and large-scale data analysis technologies. A systematic classification and comparative evaluation of contemporary approaches to data mining is conducted, with a focus on their application for solving managerial tasks and developing business strategies. The general architecture for corporate digital information processing is outlined. Particular attention is paid to the functional capabilities of predictive analytics and forecasting systems. An overview of leading solutions in the fields of analytics and machine learning within the IT market is presented, along with the prospects for their integration into the digital economy. Foundational approaches to model training in artificial intelligence systems and their potential applications across various business domains are considered. The specifics of implementing machine learning algorithms as integral components of artificial intelligence systems are examined, analysing their advantages and limitations, while also assessing the potential for leveraging these technologies in modern management. Promising directions and functional capabilities for the development of artificial intelligence in corporate solutions are enumerated, such as agent-based systems, automated machine learning, and causal artificial intelligence.

цифровая экономикабизнес-аналитикабольшие данныемашинное обучениеинтеллектуальные системыуправление бизнесомdigital economybusiness analyticsBig Datamachine learningintelligent systemsbusiness managementReferencesБасергян А.А. Куприянов М.С., Холод И.И. и др. 2009. Анализ данных и процессов. Учебное пособие. 3-е издание. Санкт-Петербург: БХВ-Петербург. 512 с.Abbott D. 2014. Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst. New Jersey: Wiley. 464 p.Кацов И.В. 2024. Искусственный интеллект на предприятии. Теория и практика для маркетинга, управления поставками и производством. Mосква: ДМК Пресс. 710 с.Baesens B. 2014. Analytics in a Big Data World. The Essential Guide to Data Science and Its Applications. New Jersey: Wiley. 256 p.Кацов И.В. 2019. Машинное обучение для бизнеса и маркетинга. Санкт-Петербург: Питер. 512 с.Balef A.R. & Eggensperger K. 2025. In-Context Decision Making for Optimizing Complex AutoML Pipelines. Preprint Arxiv.org (Cornell University Library). https://doi.org/10.48550/arXiv.2508.13657Маслова Л.А., Гутник С.А. и Чеповский П.В. 2017. Бизнес-архитектура решений SAP для автоматизации деятельности финансово-кредитных организаций. Учебное пособие. Mосква: ИНТУИТ. 255 с.Basergyan A.A., Kupriyanov M.S., Kholod I.I. et al. 2009. Analiz dannykh i protsessov [Data and Process Analysis]. 3rd edition. Saint Petersburg: BKhV-Peterburg. 512 p. (In Russian)Сигель Э. 2014. Просчитать будущее. Кто кликнет, купит, соврет или умрет. Mосква: Альпина Бизнес Букс. 374 с.Brunk J., Stottmeister J.,Weinzierl S. et al. 2020.Exploring theEffect ofContextInformation on Deep Learning Business Process Predictions. Journal of Decision Systems. Vol. 29. P. 328–343. https://doi.org/10.1080/12460125.2020.1790183Старостин В.С. 2018. Трансформация маркетинговых технологий в эпоху машинного интеллекта. Вестник университета. 1 (1). С. 28–34.Brynjolfsson E., Rock D. & Syverson C. 2017. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. National Bureau of Economic Research. No. 24001. P. 1–44. https://doi.org/10.3386/w24001Abbott D. 2014. Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst. New Jersey: Wiley. 464 p.Challapally A., Pease C., Raskar R and Chari P. 2025. The GenAI Divide: State of AI in Business 2025. Cambridge: MIT Vedia Lab. 26 p.Baesens B. 2014. Analytics in a Big Data World. The Essential Guide to Data Science and Its Applications. New Jersey: Wiley. 256 p.Fayyad U., Piatetsky-Shapiro G., Smyth P. & Uthurusamy R. 1996. Advances in Knowledge Discovery in Databases. Cambridge: MIT Press. 625 p.Balef A.R. and Eggensperger K. 2025. In-Context Decision Making for Optimizing Complex AutoML Pipelines. Preprint Arxiv.org (Cornell University Library). https://doi.org/10.48550/arXiv.2508.13657Gandhi A., Adhvaryu K., Poria S. et al. 2023. Multimodal Sentiment Analysis: A Systematic Review of History, Datasets, Multimodal Fusion Methods, Applications, Challenges and Future Directions. Information Fusion. Vol. 91. P. 424–444. https://doi.org/10.1016/j.inffus.2022.09.025Brunk J., Stottmeister J.,Weinzierl S. et al. 2020.Exploring the Effect of Context Information on Deep Learning Business Process Predictions. Journal of Decision Systems. Vol. 29. P. 328–343. https://doi.org/10.1080/12460125.2020.1790183Gutnik S. 2021. Application of Data Mining and Machine Learning Methods to Enhance the Effectiveness ofDigital Marketing Strategies.Digital Strategies in a Global Market. Navigating the Fourth Industrial Revolution. Konina N.Yu. (ed.). Cham: Palgrave Macmillan. P. 131–144. https://doi.org/10.1007/978-3-030-58267-8Brynjolfsson E., Rock D. and Syverson C. 2017. Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics. National Bureau of Economic Research. No. 24001. P. 1–44. https://doi.org/10.3386/w24001Katsov I.V. 2019. Mashinnoye obucheniye dlya biznesa i marketinga [Machine Learning for Business and Marketing]. Saint Petersburg: Piter. 512 p. (In Russian)Challapally A., Pease C., Raskar R and Chari P. 2025. The GenAI Divide: State of AI in Business 2025. Cambridge: MIT Vedia Lab. 26 p.Katsov I.V. 2024. Iskusstvennyy intellekt na predpriyatii. Teoriya i praktika dlya marketinga, upravleniya postavkami i proizvodstvom [Artificial Intelligence in the Enterprise. Theory and Practice for Marketing, Supply Chain and Production Management]. Moscow: DMK Press. 710 p. (In Russian)Fayyad U., Piatetsky-Shapiro G., Smyth P. and Uthurusamy R. Advances in Knowledge Discovery in Databases. Cambridge: MIT Press. 1996. 625 p.Kumar V. & Reinartz W. 2012. Customer Relationship Management: Concept, Strategy, and Tools. Berlin: Springer-Verlag. 411 p. https://doi.org/10.1007/978-3-662-55381-7Gandhi A., Adhvaryu K., Poria S. et al. 2023. Multimodal Sentiment Analysis: A Aystematic Review of History, Datasets, Multimodal Fusion Methods, Applications, Challenges and Future Directions. Information Fusion. Vol. 91. P. 424–444. https://doi.org/10.1016/j.inffus.2022.09.025Maslova L.A., Gutnik S.A. & Chepovsky P.V. 2017. Biznes-arkhitektura resheniy SAP dlya avtomatizatsii deyatel’nosti finansovo-kreditnykh organizatsiy [SAP Solution Business Architecture for Automating the Activities of Financial and Credit Institutions]. Moscow: INTUIT. 255 p. (In Russian)Gutnik S. 2021. Application of Data Mining and Machine Learning Methods to Enhance the Effectiveness of Digital Marketing Strategies. Digital Strategies in a Global Market. Navigating the Fourth Industrial Revolution. Konina N.Yu. (ed.). Cham: Palgrave Macmillan. P. 131–144. https://doi.org/10.1007/978-3-030-58267-8Rawal A., Raglin A., RawatD.B. et al. 2025. Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives. ACM Computing Surveys. 57 (6). P. 1–30. https://doi.org/10.1145/3665494Kumar V., Reinartz W. 2012. Customer Relationship Management: Concept, Strategy, and Tools. Berlin: Springer-Verlag. 411 p. https://doi.org/10.1007/978-3-662-55381-7Riad M., Naimi M. & Okar C. 2024. Enhancing Supply Chain Resilience Through Artificial Intelligence: Developing a Comprehensive Conceptual Framework for AI Implementation and Supply Chain Optimization. Logistics. 8 (4). P. 1–26. https://doi.org/10.3390/logistics8040111Rawal A., Raglin A., Rawat D.B. et al. 2025.Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives. ACM Computing Surveys. 57 (6). P. 1–30. https://doi.org/10.1145/3665494Rogers D.L. 2016. The Digital Transformation Playbook. New York: Columbia Business School Publishing. 278 p.Riad M., Naimi M. and Okar C. 2024. Enhancing Supply Chain Resilience Through Artificial Intelligence: Developing a Comprehensive Conceptual Framework for AI Implementation and Supply Chain Optimization. Logistics. 8 (4). P. 1–26. https://doi.org/10.3390/logistics8040111Russell S.J. & Norvig P. 2022. Artificial Intelligence. A Modern Approach: Fourth Edition. London: Pearson Education Limited. 1167 p.Rogers D.L. 2016. The Digital Transformation Playbook. New York: Columbia Business School Publishing. 278 p.Shlash A., Khanfar I., Al-Oraini B. et al. 2024. Predictive Analytics on Artificial Intelligence in Supply Chain Optimization. Data & Metadata. Vol. 3. P. 1–395. https://doi.org/10.56294/dm2024395Russell S.J., Norvig P. 2022. Artificial Intelligence. A Modern Approach: Fourth Edition. London: Pearson Education Limited. 1167 p.Sigel E. 2014. Proschitat’ budushcheye. Kto kliknet, kupit, sovret ili umret [Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die]. Moscow: Al’pina Biznes Buks. 374 p. (In Russian)Shlash A., Khanfar I., Al-Oraini B. et al. 2024. Predictive Analytics on Artificial Intelligence in Supply Chain Optimization. Data & Metadata. Vol. 3. P. 1–395. https://doi.org/10.56294/dm2024395Starostin V.S. 2018. Transformatsiya marketingovykh tekhnologiy v epokhu mashinnogo intellekta [Transformation of Marketing Technologies in the Era of Machine Intelligence]. Vestnik Universiteta. 1 (1). P. 28–34. (In Russian)Wang Z., Zhang J. 2025. From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence. Preprint Arxiv.org (Cornell University Library). https://doi.org/10.48550/arXiv.2508.15447Wang Z. & Zhang J. 2025. From Bits to Boardrooms: A Cutting-Edge Multi-Agent LLM Framework for Business Excellence. Preprint Arxiv.org (Cornell University Library). https://doi.org/10.48550/arXiv.2508.15447Yang Q., Wu X., Domingos P. et al. 2006. 10 Challenging Problems in Data Mining Research. International Journal of Information Technology & Decision Making. 5 (4). P. 597–604. https://doi.org/10.1142/S0219622006002258Yang Q., Wu X., Domingos P. et al. 2026. 10 Challenging Problems in Data Mining Research. International Journal of Information Technology & Decision Making. 5 (4). P. 597–604. https://doi.org/10.1142/S0219622006002258

The authors declare that there are no conflicts of interest present.