The Role Of Artificial Intelligence In Big Data Analytics For Business Intelligence Applications In Saas Products
Keywords:
Business Intelligence (BI), Business Analytics, Artificial Intelligence (AI), Big Data Analytics (BDA), Software-asa-Service (SaaS), Data-Driven Decision Making, BI Evolution, AI in Business Intelligence, BI SaaS Integration, BASOA (BigData Analytics Service-Oriented Architecture), AI Frameworks, Scalability in BI, Performance Optimization, Data Strategy, Sector-Specific Applications
Abstract
The growing complexity and significance of data-related challenges in modern businesses have brought Business Intelligence and Analytics (BI&A) to the forefront of both academic research and industry practice. A major paradigm shift is underway in how organizations make decisions and plan for the future, driven by the integration of Artificial Intelligence (AI) and advanced data analytics. This paper explores how these emerging technologies are transforming the landscape of BI. The study aims to provide a comprehensive overview of BI, with a particular focus on its evolution through the incorporation of AI and Big Data Analytics (BDA), and to assess the future direction of these technologies in the corporate environment. Specifically, the paper reviews the integration of BI, AI, and BDA within Software-as-a-Service (SaaS) platforms. It outlines the core components of big data analytics, explains their relationship to business intelligence, and highlights current AI trends in BI. Additionally, the review presents successful applications across various sectors, proposes a Big Data Analytics Service-Oriented Architecture (BASOA), and discusses the growing adoption of SaaS in BI solutions. It also identifies future research opportunities, including the development of AI frameworks and strategies to optimize the scalability and performance of BI applications deployed on SaaS platforms.
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2. S. Arora and S. R. Thota, “Using Artificial Intelligence with Big Data Analytics for Targeted MarketingCampaigns,” June 2024. doi:10.48175/IJARSCT-18967.
3. S. Arora and S. R. Thota, “Automated Data Quality Assessment and Enhancement for SaaS Based DataApplications,” J. Emerg. Technol. Innov. Res., vol. 11, pp. i207–i218, 2024.
doi:10.6084/m9.jetir.JETIR2406822.
4. S. R. Thota, “Ethical Considerations and Privacy in AI-Driven Big Data Analytics,” 2024.
5. S. Alghannam, “A Review of Big Data Analytics for Organizational Business Intelligence,” Int. J. Sci. Res., vol. 11, no. 4, pp. 25–32, 2022. doi:10.21275/SR22331213813.
6. Y. Niu, L. Ying, J. Yang, M. Bao, and C. B. Sivaparthipan, “Organizational Business Intelligence and DecisionMaking Using Big Data Analytics,” Inf. Process. Manag., 2021. doi:10.1016/j.ipm.2021.102725.
7. S. R. Thota and S. Arora, “Neurosymbolic AI for Explainable Recommendations in Frontend UI Design Bridging the Gap Between Data-Driven and Rule-Based Approaches,” May 2024, pp. 766–775.
8. G. Vicario and S. Coleman, “A Review of Data Science in Business and Industry and a Future View,” Appl. Stoch. Model. Bus. Ind., 2020. doi:10.1002/asmb.2488.
9. Y. Chen et al., “An Optimizing and Differentially Private Clustering Algorithm for Mixed Data in SDN-BasedSmart Grid,” IEEE Access, 2018.
10. K. Henrys, “Role of Predictive Analytics in Business,” SSRN Electron. J., 2021. doi:10.2139/ssrn.3829621.
11. S. Arora and P. Khare, “Optimizing Software Pricing: AI-driven Strategies for Independent Software Vendors,” May 2024, pp. 743–753.
12. Z. Sun and H. Z. Sun, “Big Data Analytics as a Service for Business Intelligence,” 14th IFIP Conf e-Business, eServices e-Society (I3E 2015), Delft, Netherlands. doi:10.1007/978-3-319-25013-7_16.
13. J. Thomas, “Optimizing Bio-energy Supply Chain to Achieve Alternative Energy Targets,” 2024, pp. 2260–2273.
14. H. Sinha, “Predicting Bitcoin Prices Using Machine Learning Techniques With Historical Data,” Int. J. Creat. Res. Thoughts, vol. 12, no. 8, 2024. doi:10.3390/e25050777.
15. Y. Chen, C. Li, and H. Wang, “Big Data and Predictive Analytics for Business Intelligence: A BibliographicStudy (2000–2021),” Forecasting, 2022. doi:10.3390/forecast4040042.
16. “Retraction: Artificial Intelligence-based Blockchain Technology for Skin Cancer Investigation Complementedwith Dietary Assessment and Recommendation Using Correlation Analysis in Elder Individuals,” Journal of Food Quality, 2024. doi:10.1155/2024/9851920.
17. S. Arora and P. Khare, “AI/ML-Enabled Optimization of Edge Infrastructure: Enhancing Performance andSecurity,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 4, pp. 230–242, 2024.
18. C. S. Lee, P. Y. S. Cheang, and M. Moslehpour, “Predictive Analytics in Business Analytics: Decision Tree,” Adv.Decis. Sci., 2022. doi:10.47654/V26Y2022I1P1-30.
19. C. A. Collier, “Teaching Case: Learning Skills of the Data Analytics Lifecycle with Microsoft Power BI andNational Parks Data,” Commun. Assoc. Inf. Syst., 2023. doi:10.17705/1CAIS.05210.
20. T. Semerádová and P. Weinlich, “Using Google Analytics to Examine the Website Traffic,” 2020. doi:10.1007/978-3-030-44440-2_5.
21. R. P. Deb Nath, K. Hose, T. B. Pedersen, O. Romero, and A. Bhattacharjee, “SETLBI: An Integrated Platformfor Semantic Business Intelligence,” in The Web Conference 2020 - Companion of the World Wide Web Conference, 2020. doi:10.1145/3366424.3383533.
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23. I. S. Nasir, A. H. Mousa, and I. L. Hussein Alsammak, “SMUPI-BIS: A Synthesis Model for Users’ PerceivedImpact of Business Intelligence Systems,” Indones. J. Electr. Eng. Comput. Sci., 2021. doi:10.11591/ijeecs.v21.i3.pp1856-1867.
24. C. Holsapple, A. Lee-Post, and R. Pakath, “A Unified Foundation for Business Analytics,” Decis. SupportSyst., 2014. doi:10.1016/j.dss.2014.05.013.
25. Z. Sun, K. D. Strang, and J. Yearwood, “Analytics Service Oriented Architecture for Enterprise InformationSystems,” ACM International Conference Proceeding Series, 2014. doi:10.1145/2684200.2684358.
26. Z. Sun and J. Yearwood, “A Theoretical Foundation of Demand Driven Web Services,” in Handbook ofResearch on Demand-Driven Web Services: Theory, Technologies, and Applications, 2014. doi:10.4018/978-1-46665884-4.ch001.
27. J. Bharadiya and J. P. Bharadiya, “Machine Learning and AI in Business Intelligence: Trends and Opportunities,” Int. J. Comput., 2023.
28. J. Thomas, “Enhancing Supply Chain Resilience Through Cloud-Based SCM and Advanced Machine Learning: A Case Study of Logistics,” J. Emerg. Technol. Innov. Res., vol. 8, no. 9, 2021.
29. V. Rohilla, M. Kaur, and S. Chakraborty, “An Empirical Framework for Recommendation-based Location Services Using Deep Learning,” Eng. Technol. Appl. Sci. Res., 2022. doi:10.48084/etasr.5126.
30. S. Mathur, “Supervised Machine Learning-Based Classification and Prediction of Breast Cancer,” Int. J. Intell. Syst. Appl. Eng., vol. 12(3), 2024. S. Arora and P. Khare, “AI/ML-Enabled Optimization of Edge Infrastructure: Enhancing Performance andSecurity,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 4, pp. 230–242, 2024.
Published
2024-09-18
How to Cite
Gayathri Devraj Naidu, Apeksha Niraj Gandhi, Darshan Rajendra Ahire, Dharmesh Shamji Vania, Rahul Pareek, & Nitin Kulshrestha. (2024). The Role Of Artificial Intelligence In Big Data Analytics For Business Intelligence Applications In Saas Products. Revista Electronica De Veterinaria, 25(2), 1612-1628. https://doi.org/10.69980/redvet.v25i2.1915
Section
Articles
