Управленец

2218-5003

2686-7923

10.29141/2218-5003-2023-14-6-1

YVQVIE

https://upravlenets.usue.ru/ru/-2023/1428

Мультимодальная бизнес-аналитика: концепция и перспективы использования в экономической науке и практике

Multimodal business analytics: The concept and its application prospects in economic science and practice

Михненко

Павел Александрович

Михненко

Павел Александрович

Mikhnenko

Pavel A.

pmihnenko@bmstu.ru

Bauman University (Moscow, Russia)Московский государственный технический университет им. Н.Э. Баумана (г. Москва, РФ)

12012024

1462181408202303102023

CC BY-NC 4.0

Одной из проблем бизнес-анализа являются получение и обработка все возрастающего объема данных экономического, финансового, организационного и политико-правового содержания. Мультимодальная бизнес-аналитика представляет новую методологию, сочетающую классический бизнес-анализ с технологиями больших данных, интеллектуальной бизнес-аналитики, мультимодального слияния данных, искусственных нейросетей и глубокого машинного обучения. Статья посвящена разработке концептуальных основ феномена мультимодальной бизнес-аналитики и обоснованию перспектив ее использования в экономической науке и практике. Методологической базой исследования выступает системный подход, позволяющий изучить это уникальное интегральное явление, включающее несколько компонентов и взаимосвязи между ними. Использовались методы сбора и оценки динамики количества релевантных публикаций и их сегментации по предметным областям. Информационную базу исследования составили научные статьи, включенные в базы данных Scopus и eLibrary за период 2000–2022 гг., посвященные проблеме мультимодальной бизнес-аналитики. Результатами исследования стали тезаурус и онтология ключевых понятий, составляющих рассматриваемый феномен. Сделан вывод, что использование этой концепции позволяет расширить спектр данных, выявить скрытые взаимосвязи организационно-экономических явлений и синтезировать принципиально новую информацию, необходимую для принятия эффективных бизнес-решений.

One of the problems of business analysis is obtaining and processing an ever-increasing volume of economic, financial, organizational, political and legal data. Multimodal business analytics is a new methodology combining the methods of classical business analysis with big data technologies, intelligent business analytics, multimodal data fusion, artificial neural networks and deep machine learning. The purpose of the study is to determine the conceptual foundations of the phenomenon of multimodal business analytics and substantiate the prospects for its use in economic science and practice. Methodologically, the study rests on the systems approach, i.e., multimodal business analytics is examined as a unique integrated phenomenon comprised of several interrelated components. The evidence base covers research studies of 2000–2022 on multimodal business analytics from Scopus and the Russian online database eLibrary.ru. Empirical methods were used to collect and evaluate the dynamics of the number of relevant publications and their segmentation by subject areas. We have proposed own thesaurus and ontology of the key terms that make up the phenomenon of multimodal business analytics. It is shown that the use of the concept allows expanding the range of data, exposing hidden interrelations of organizational and economic phenomena and synthesizing fundamentally new information needed for effective decision-making in business. Keywords: multimodal business analytics; business analysis; data mining; data fusion; neural networks; machine learning. Funding: The article was prepared as part of the state assignment of Bauman University in 2023 on the topic “Exploratory research in providing algorithmic, software and hardware solutions for high-performance hybrid intelligent systems for multimodal merging and analytical processing of heterogeneous data on geographically distributed industrial objects”.

интеллектуальный анализ данных;слияние данных;нейросети;машинное обучение.мультимодальная бизнес-аналитика;бизнес-анализ;

multimodal business analytics;business analysis;data mining;data fusion;neural networks;machine learning.

Статья подготовлена в рамках государственного задания МГТУ им. Н.Э. Баумана в 2023 г. на тему «Поисковые исследования в области создания алгоритмического, программного и аппаратного обеспечения высокопроизводительных гибридных интеллектуальных систем для мультимодального слияния и аналитической обработки разнородных данных о территориально распределенных объектах промышленной инфраструктуры».

The article was prepared as part of the state assignment of Bauman University in 2023 on the topic “Exploratory research in providing algorithmic, software and hardware solutions for high-performance hybrid intelligent systems for multimodal merging and analytical processing of heterogeneous data on geographically distributed industrial objects”.

Батаева Б.С., Кокурина А.Д., Карпов Н.А. (2021). Влияние раскрытия ESG-показателей на финансовые результаты российских публичных компаний // Управленец. Т. 12, № 6. С. 20–32. https://doi.org/10.29141/2218-5003-2021-12-6-2

Калабихина И.Е., Крикунов А.С. (2018). Новая методика оценки качества нефинансовой отчетности (на примере энергетических компаний) // Вестник СПбГУ. Менеджмент. Т. 17, вып. 3. С. 297–328. https://doi.org/10.21638/11701/spbu08.2018.303

Кузубов С.А., Евдокимова М.С. (2017). Повышает ли стоимость компании публикация нефинансовых отчетов по стандартам GRI (на примере стран БРИКС)? // Учет. Анализ. Аудит. № 2. С. 28–36. https://doi.org/10.26794/2408-9303-2017-2-28-36

Митрович С. (2017). Специфика интеграции технологий бизнес-интеллекта и больших данных в процессы экономического анализа // Бизнес-информатика. № 4(42). С. 40–46. https://doi.org/10.17323/1998-0663.2017.4.40.46

Олейник А.Н. (2021). Применение контент-анализа в экономических науках: обзор текущего состояния дел и перспектив // Вопросы экономики. № 4. С. 79–95. https://doi.org/10.32609/0042-8736-2021-4-79-95

Понкин И.В. (2019). Понятие «аналитика» // International Journal of Open Information Technologies. Т. 7, № 10. С. 80–90.

Смирнов С.В., Смирнов С.С. (2022). Мониторинг российского делового цикла на основе ежедневных данных // Вопросы экономики. № 5. С. 26–50. https://doi.org/10.32609/0042-8736-2022-5-26-50

Федорова Е.А., Афанасьев Д.О., Нерсесян Р.Г., Ледяева С.В. (2020). Влияние нефинансовой информации на основные показатели российских компаний // Журнал Новой экономической ассоциации. № 2(46). С. 73–96. https://doi.org/10.31737/2221-2264-2020-46-2-4

Abouelmehdi K., Beni-Hssane A., Khaloufi H., Saadi M. (2017). Big data security and privacy in healthcare: A review. Procedia Computer Science, no. 113, pp. 73–80. https://doi.org/10.1016/j.procs.2017.08.292

Acciarini C., Cappa F., Boccardelli P., Oriani R. (2023). How can organizations leverage big data to innovate their business models? A systematic literature review. Technovation, vol. 123, 102713. https://doi.org/10.1016/j.technovation.2023.102713

Ahmad Z., Jindal R., Mukuntha N.S., Ekbal A., Bhattachharyya P. (2022). Multi-modality helps in crisis management: An attention-based deep learning approach of leveraging text for image classification. Expert Systems with Applications, vol. 195, 116626. https://doi.org/10.1016/j.eswa.2022.116626

Asif M., Searcy C., Santos P., Kensah D. (2013). A review of Dutch corporate sustainable development reports. Corporate Social Responsibility and Environmental Management, vol. 20, issue 6, pp. 321–339. https://doi.org/10.1002/csr.1284

Blazquez D., Domenech J. (2018). Big Data sources and methods for social and economic analyses. Technological Forecasting and Social Change, no. 130, pp. 99–113. https://doi.org/10.1016/j.techfore.2017.07.027

Brennan N., Merkl-Davies D. (2013). Accounting narratives and impression management. The Routledge Companion to Communication in Accounting (pp. 109–132). London, Routledge. https://doi.org/10.4324/9780203593493.CH8

Chen H., Chiang R.H.L., Storey V.C. (2012). Business intelligence and analytics: From Big Data to Big Impact. MIS Quarterly, vol. 36, no. 4, pp. 1165–1188. https://doi.org/10.2307/41703503

Dai Y., Yan Z., Cheng J., Duan X., Wang G. (2023). Analysis of multimodal data fusion from an information theory perspective. Information Sciences, vol. 623, pp. 164–183. https://doi.org/10.1016/j.ins.2022.12.014

Davis G., Searcy C. (2010). A review of Canadian corporate sustainable development reports. Journal of Global Responsibility, no. 1, pp. 316–329. https://doi.org/10.1108/20412561011079425

Doan A., Halevy A., Ives Z. (2012). Principles of data integration. Elsevier.

Duong T., Eduard O., Teuteberg A.F. (2022). What translates big data into business value? A meta-analysis of the impacts of business analytics on firm performance. Information & Management, vol. 59, issue 6, 103685. https://doi.org/10.1016/j.im.2022.103685

Duque J., Godinho A., Vasconcelos J. (2022). Knowledge data extraction for business intelligence: A design science research approach. Procedia Computer Sci-ence, no. 204, pp. 131–139. https://doi.org/10.1016/j.procs.2022.08.016

Fernandez-Vazquez E., Moreno B. (2017). Entropy econometrics for combining regional economic forecasts: A data-weighted prior estimator. Journal of Geo-graphical Systems, vol. 19, no. 4, pp. 349–370. https://doi.org/10.1007/s10109-017-0259-9

Foley É., Guillemette M.G. (2010). What is business intelligence? International Journal of Business Intelligence Research, vol. 1, no. 4, pp. 1–28. https://doi.org/10.1007/978-1-4302-3325-1_1

Gao Q., Cheng Ch., Sun G. (2023). Big data application, factor allocation, and green innovation in Chinese manufacturing enterprises. Technological Fore-casting and Social Change, vol. 192, 122567. https://doi.org/10.1016/j.techfore.2023.122567

Guo Y., Wang N., Xu Z., Wu K. (2020). The internet of things-based decision support system for information processing in intelligent manufacturing using data mining technology. Mechanical Systems and Signal Processing, no. 142, 106630. https://doi.org/10.1016/j.ymssp.2020.106630

Kara M.E., Firat S., Ghadge A. (2020). A data mining-based framework for supply chain risk management. Computers & Industrial Engineering, no. 139, 105570. https://doi.org/10.1016/j.cie.2018.12.017

Keshta I., Odeh A. (2021). Security and privacy of electronic health records: Concerns and challenges. Egyptian Informatics Journal, vol. 22, no. 2, pp. 177–183. https://doi.org/10.1016/j.eij.2020.07.003

Kounta C.A., Kamsu-Foguem B., Noureddine F., Tangara F. (2022). Multimodal deep learning for predicting the choice of cut parameters in the milling process. Intelligent Systems with Applications, no. 16, 200112. https://doi.org/10.1016/j.iswa.2022.200112

Lahat D., Adali T., Jutten C. (2015). Multimodal data fusion: An overview of methods, challenges, and prospects. Proceedings of the IEEE, vol. 103, no. 9, pp. 1449–1477. https://doi.org/10.1109/JPROC.2015.2460697

Li C., Chen Y., Shang Y. (2022). A review of industrial big data for decision making in intelligent manufacturing. Engineering Science and Technology, an International Journal, no. 29, 101021. https://doi.org/10.1016/j.jestch.2021.06.001

Li M., Wang F., Jia X., Li W., Li T., Rui G. (2021). Multi-source data fusion for economic data analysis. Neural Computing & Applications, no. 33, pp. 4729–4739. https://doi.org/10.1007/s00521-020-05531-0

Liu L., Wan X., Gao Z., Zhang X. (2023). An improved MPGA-ACO-BP algorithm and comprehensive evaluation system for intelligence workshop multi-modal data fusion. Advanced Engineering Informatics, vol. 56, 101980. https://doi.org/10.1016/j.aei.2023.101980

Liu S., Gao P., Li Y., Fu W., Ding W. (2023). Multi-modal fusion network with complementarity and importance for emotion recognition. Information Sciences, vol. 619, pp. 679–694. https://doi.org/10.1016/j.ins.2022.11.076

Menges F., Latzo T., Vielberth M., Sobola S., Pöhls H.C., Taubmann B., Köstler J., Puchta A., Freiling F., Reiser H.P., Pernul G. (2021). Towards GDPR-compliant data processing in modern SIEM systems. Computers & Security, no. 103, 102165. https://doi.org/10.1016/j.cose.2020.102165

Nalić J., Martinović G., Žagar D. (2020). New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers. Advanced Engineering Informatics, vol. 45, pp. 101130. https://doi.org/10.1016/j.aei.2020.101130

Nathan G., Safoora Y., Mostafa R. (2022). Multimodal data fusion for systems improvement: A review. IISE Transactions, vol. 54, no. 11, pp. 1098–1116. https://doi.org/10.1080/24725854.2021.1987593

Pedota M. (2023). Big data and dynamic capabilities in the digital revolution: The hidden role of source variety. Research Policy, vol. 52, issue 7, 104812. https://doi.org/10.1016/j.respol.2023.104812

Saber M., Weber A. (2019). Sustainable grocery retailing: Myth or reality? – A content analysis. Business and Society Review, vol. 124, issue 4, pp. 479–496. https://doi.org/10.1111/basr.12187

Shi Y., Cui T., Liu F. (2022). Disciplined autonomy: How business analytics complements customer involvement for digital innovation. The Journal of Strategic Information Systems, vol. 31, issue 1, 101706. https://doi.org/10.1016/j.jsis.2022.101706

Sivarajah U., Kamal M.M., Irani Z., Weerakkody V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, vol. 70, pp. 263–286. https://doi.org/10.1016/j.jbusres.2016.08.001

Skouloudis A., Evangelinos K.I., Kourmousis F. (2010). Assessing non-financial reports according to the Global Reporting Initiative guidelines: Evidence from Greece. Journal of Cleaner Production, no. 18, pp. 426–438. https://doi.org/10.1016/J.JCLEPRO.2009.11.015

Woodall P., Giannikas V., Lu W., McFarlane D. (2019). Potential problem data tagging: Augmenting information systems with the capability to deal with inaccuracies. Decision Support Systems, no. 121, pp. 72–83. https://doi.org/10.1016/j.dss.2019.04.007

Yager R. (2004). A framework for multi-source data fusion. Information Sciences, vol. 163, issues 1-3, pp. 75–200. https://doi.org/10.1016/j.ins.2003.03.018

Ze D., Yuchao P., Sichao M. (2018). Understanding the economic shifting ‘‘from real to virtual’’ from the micro perspective: A literature review of corporate financialization. Foreign Economics & Management, vol. 40, no. 11, pp. 31–43.

Zhang P., Li T., Yuan Z., Luo C., Wang G., Liu J., Du S. (2022). A data-level fusion model for unsupervised attribute selection in multi-source homogeneous data. Information Fusion, vol. 80, pp. 87–103. https://doi.org/10.1016/j.inffus.2021.10.017

Bataeva B.S., Kokurina A.D., Karpov N.A. (2021). The impact of ESG reporting on the financial performance of Russian public companies. Upravlenets / The Manager, vol. 12, no. 6, pp. 20–32. https://doi.org/10.29141/2218-5003-2021-12-6-2. (in Russ.)

Kalabikhina I.E., Krikunov A.S. (2018). A new method of assessing the quality of non-financial reporting (on the example of energy companies). Vestnik SPbGU. Menedzhment / Vestnik of St Petersburg University. Management, vol. 17, issue 3, pp. 297–328. https://doi.org/10.21638/11701/spbu08.2018.303. (in Russ.)

Kuzubov S.А., Evdokimova M.S. (2017). Does the company value increase through the publication of non-financial reports under GRI guidelines? (On the example of BRICS countries). Uchet. Analiz. Audit / Accounting. Analysis. Auditing, no. 2, pp. 28–36. https://doi.org/10.26794/2408-9303-2017--2-28-36 . (in Russ.)

Mitrovich S. (2017). Specifics of the integration of Business Intelligence and Big Data technologies in the processes of economic analysis. Biznes-informatika / Business Informatics, no. 4(42), pp. 40–46. https://doi.org/10.17323/1998-0663.2017.4.40.46. (in Russ.)

Oleinik A.N. (2021). Uses of content analysis in economic sciences: An overview of the current situation and prospects. Voprosy Ekonomiki, no. 4, pp. 79–95. https://doi.org/10.32609/0042-8736-2021-4-79-95. (in Russ.)

Ponkin I.V. (2019). The concept of analytics. International Journal of Open Information Technologies, vol. 7, no. 10, pp. 80–90. (in Russ.)

Smirnov S.V., Smirnov S.S. (2022). Monitoring Russian business cycle with daily indicators. Voprosy Ekonomiki, no. 5, pp. 26–50. https://doi.org/10.32609/0042-8736-2022-5-26-50. (in Russ.)

Fedorova E.A., Afanasev D.O., Nersesyan R.G., Ledyaeva S.V. (2020). Impact of non-financial information on key financial indicators of Russian companies. Zhurnal Novoy ekonomicheskoy assotsiatsii / The Journal of the New Economic Association, no. 2(46), pp. 73–96. https://doi.org/10.31737/2221-2264-2020-46-2-4. (in Russ.)