Using deep learning to enhance business intelligence in organizational management

Sina Gholami; Erfan Zarafshan; Reza Sheikh; Shib Sankar Sana; Sina Gholami; Erfan Zarafshan; Reza Sheikh; Shib Sankar Sana

doi:10.3934/DSFE.2023020

Data Science in Finance and Economics

2023, Volume 3, Issue 4: 337-353. doi: 10.3934/DSFE.2023020

Previous Article Next Article

Research article

Using deep learning to enhance business intelligence in organizational management

1.
Production Management, Shahrood University of Technology, Shahrood, Iran
2.
Department of Mathematics, Kishore Bharati Bhagini Nivedita College, Ramkrishna Sarani, Behala, Kolkata-700060, India

Received: 25 July 2023 Revised: 23 August 2023 Accepted: 30 August 2023 Published: 01 October 2023
JEL Codes: O16; C63; M21

Business intelligence (BI) is crucial in organizational management, providing insights that enable informed decision-making. Traditional BI approaches, however, are limited in handling the vast amounts of data generated by organizations today. Deep learning, a subfield of machine learning, has shown great potential in improving BI through automated analysis of complex and large data sets. In this study, we explore the effectiveness of deep learning in enhancing BI for organizational management. We evaluate the accuracy and F-score of our proposed deep learning model against traditional BI methods in a real-world scenario. Our dataset contains a large volume of unstructured text data from customer feedback forms, which presents significant challenges for traditional BI approaches. Our deep learning model is trained using a convolution neural network (CNN) architecture to classify customer feedback into positive and negative sentiment categories. The model achieved an accuracy of 88% and an F-score of 0.86, outperforming traditional BI methods such as rule-based systems and sentiment analysis algorithms. Furthermore, our model's ability to handle unstructured data highlights its potential for processing diverse data types beyond structured data, commonly used in traditional BI.
- artificial intelligence,
- decision-making,
- knowledge management,
- organizational quality,
- CNN
Citation: Sina Gholami, Erfan Zarafshan, Reza Sheikh, Shib Sankar Sana. Using deep learning to enhance business intelligence in organizational management[J]. Data Science in Finance and Economics, 2023, 3(4): 337-353. doi: 10.3934/DSFE.2023020

Related Papers:

Abstract

Business intelligence (BI) is crucial in organizational management, providing insights that enable informed decision-making. Traditional BI approaches, however, are limited in handling the vast amounts of data generated by organizations today. Deep learning, a subfield of machine learning, has shown great potential in improving BI through automated analysis of complex and large data sets. In this study, we explore the effectiveness of deep learning in enhancing BI for organizational management. We evaluate the accuracy and F-score of our proposed deep learning model against traditional BI methods in a real-world scenario. Our dataset contains a large volume of unstructured text data from customer feedback forms, which presents significant challenges for traditional BI approaches. Our deep learning model is trained using a convolution neural network (CNN) architecture to classify customer feedback into positive and negative sentiment categories. The model achieved an accuracy of 88% and an F-score of 0.86, outperforming traditional BI methods such as rule-based systems and sentiment analysis algorithms. Furthermore, our model's ability to handle unstructured data highlights its potential for processing diverse data types beyond structured data, commonly used in traditional BI.

References

[1]	Abdelwhab AA, Panneerselvam DDD, Paris L, et al. (2019) Key factors influencing knowledge sharing practices and its relationship with organizational performance within the oil and gas industry. J Knowl Manag 23: 1806–1837. https://doi.org/10.1108/JKM-06-2018-0394 doi: 10.1108/JKM-06-2018-0394
[2]	Anisuzzaman BM, Siddique AR, Al Mamun T, et al. (2022) Deep learning in mining business intelligence. In 2022 IEEE Region 10 Symposium (TENSYMP), pp: 1–6. IEEE. https://doi.org/10.1109/TENSYMP54529.2022.9864444
[3]	Bharadiya JP (2023) Leveraging machine learning for enhanced business intelligence. Int J Comp Sci Tech 7: 1–19. https://www.ijcst.com.pk/IJCST/article/view/234
[4]	Çelebi Hİ (2021) Artificial intelligence applications in management information systems: a comprehensive systematic review with business analytics perspective. Artif Intel Theo Appl 1: 25–56. https://dergipark.org.tr/en/pub/aita/issue/77113/1290881
[5]	Doshi R, Hiran KK, Mijwil MM, et al. (2023) To That of Artificial Intelligence, Passing Through Business Intelligence. In Handbook of Research on AI and Knowledge Engineering for Real-Time Business Intelligence, 1–16. IGI Global. https://doi.org/10.4018/978-1-6684-6519-6.ch001
[6]	Fombellida J, Martín-Rubio I, Torres-Alegre S, et al. (2020) Tackling business intelligence with bioinspired deep learning. Neur Comp Appl 32: 13195–13202. https://doi.org/10.1007/s00521-018-3377-5 doi: 10.1007/s00521-018-3377-5
[7]	Hamzehi M, Hosseini S (2022) Business intelligence using machine learning algorithms. Multimed Tools Appl 81: 33233–33251. https://doi.org/10.1007/s11042-022-13132-3 doi: 10.1007/s11042-022-13132-3
[8]	Harter JK, Schmidt FL, Hayes TL (2002) Business-unit-level relationship between employee satisfaction, employee engagement, and business outcomes: a meta-analysis. J Appl Psychol 87: 268. https://doi.org/10.1037/0021-9010.87.2.268 doi: 10.1037/0021-9010.87.2.268
[9]	Hlavac J, Stefanovic J (2020) Machine Learning and Business Intelligence or from Descriptive Analytics to Predictive Analytics. Cybernetics & Informatics (K & I). https://doi.org/10.1109/KI48306.2020.9039874 doi: 10.1109/KI48306.2020.9039874
[10]	Kraus M, Feuerriege S, Oztekin A (2020) Deep learning in business analytics and operations research: Models, applications and managerial implications. Eur J Oper Res 281: 628–641. https://doi.org/10.1016/j.ejor.2019.09.018 doi: 10.1016/j.ejor.2019.09.018
[11]	Manesh MF, Pellegrini MM, Marzi G, et al. (2020) Knowledge management in the fourth industrial revolution: Mapping the literature and scoping future avenues. IEEE T Eng Manage 68: 289–300. https://doi.org/10.1109/TEM.2019.2963489 doi: 10.1109/TEM.2019.2963489
[12]	Mohaghar A, Lucas C, Hosseini F, et al. (2008) Application of business intelligence as a strategic information technology in banking: Inspection and detection of fraud. J Inf Tech Manag 1: 105.
[13]	Nosrati Sh (2015) The role of business intelligence on the productivity of the Iranian banking industry. Int Conf on Manag, Eco and Fin Sys.
[14]	Ostendorf S, Meier Y, Brand M (2022) Self-disclosure on social networks: More than a rational decision-making process. Cyberpsycholog 16: 27. https://doi.org/10.5817/CP2022-4-2 doi: 10.5817/CP2022-4-2
[15]	Phillips-Wren G, Daly M, Burstein F (2021) Reconciling business intelligence, analytics and decision support systems: More data, deeper insight. Dec Supp Sys 146: 113560. https://doi.org/10.1016/j.dss.2021.113560 doi: 10.1016/j.dss.2021.113560
[16]	Ranjan J, Foropon C (2021) Big data analytics in building the competitive intelligence of organizations. Int J of Infor Manag 56: 102231. https://doi.org/10.1016/j.ijinfomgt.2020.102231 doi: 10.1016/j.ijinfomgt.2020.102231
[17]	Rath M (2021) Realization of business intelligence using machine learning. In book: Internet of Things in Business Transformation, 169–184. https://doi.org/10.1002/9781119711148.ch10 doi: 10.1002/9781119711148.ch10
[18]	Safari G, Majidi B, Akhtarkavan E (2018) Designing a treatment decision management system with the help of machine learning and business intelligence for a smart hospital. fourth National Conference on new Technologies in Electrical Engineering, Telecommunications and Mechatronics.
[19]	Shi Z, Wang G (2018) Integration of big-data ERP and business analytics (BA). J High Tech Manag Res 29: 141–150. https://doi.org/10.1016/j.hitech.2018.09.004 doi: 10.1016/j.hitech.2018.09.004
[20]	Shrestha YR, Krishna V, Von Krogh G (2021) Augmenting organizational decision-making with deep learning algorithms: Principles, promises, and challenges. J Bus Res 123: 588–603. https://doi.org/10.1016/j.jbusres.2020.09.068 doi: 10.1016/j.jbusres.2020.09.068
[21]	Sreesurya I, Rathi H, Jain P, et al. (2020) Hypex: A tool for extracting business intelligence from sentiment analysis using enhanced LSTM. Multimed Tools Appl 79: 35641–35663. https://doi.org/10.1007/s11042-020-08930-6 doi: 10.1007/s11042-020-08930-6
[22]	Turkeli S, Ozaydin F (2022) A novel framework for extracting knowledge management from business intelligence log files in hospitals. Appl Sci 12: 5621. https://doi.org/10.3390/app12115621 doi: 10.3390/app12115621
[23]	Wani SUD, Khan NA, Thakur G, et al. (2022) Utilization of artificial intelligence in disease prevention: Diagnosis, treatment, and implications for the healthcare workforce. Healthcare 10: 608. https://doi.org/10.3390/healthcare10040608 doi: 10.3390/healthcare10040608
[24]	Whig P (2019) Artificial intelligence and machine learning in business. Int J Integr Educ 2: 64–68.
[25]	Zarei B, Zarei Z (2018) The effect of business intelligence on financial performance of Iranian banks. J Appl Econ Stud Iran 7: 111–130.

Reader Comments

Your name:*

Email:*
© 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)