Optimizing e-Commerce Decision-Making Using Real-Time Business Intelligence with AWS Cloud Technologies
DOI:
https://doi.org/10.56472/ICCSAIML25-137Keywords:
e-commerce, business intelligence, AWS Cloud, real-time analytics, data-driven decision-making, Amazon Kinesis, Amazon Redshift, Amazon QuickSightAbstract
In the rapidly evolving e-commerce landscape, leveraging real-time business intelligence (BI) is essential for data-driven decision-making and maintaining a competitive edge. This paper explores the integration of AWS Cloud technologies to optimize e-commerce operations through real-time BI. We examine AWS services such as Amazon Kinesis for real-time data streaming, Amazon Redshift for scalable data warehousing, and Amazon QuickSight for interactive data visualization. By implementing these services, e-commerce businesses can enhance customer experiences, streamline operations, and drive profitability. The paper also discusses best practices for adopting cloud-based BI solutions, addressing challenges, and providing a roadmap for successful implementation
Downloads
References
[1] Amazon Web Services, Inc. (2023). Amazon Kinesis Data Streams Developer Guide. AWS Documentation.
[2] Amazon Web Services, Inc. (2023). Amazon Redshift Database Developer Guide. AWS Documentation.
[3] Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
[4] Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.
[5] Susmith Barigidad. “Edge-Optimized Facial Emotion Recognition: A High-Performance Hybrid Mobilenetv2-Vit Model". IJAIBDCMS [International JournalofAI,BigData,ComputationalandManagement Studies]. 2025 Apr. 3 [cited 2025 Jun. 4]; 6(2):PP. 1-10.
[6] R. Daruvuri, “Harnessing vector databases: A comprehensive analysis of their role across industries,” International Journal of Science and Research Archive, vol. 7, no. 2, pp. 703–705, Dec. 2022, doi: 10.30574/ijsra.2022.7.2.0334.
[7] Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
[8] Sharma, S., & Sharma, M. K. (2020). AI and Machine Learning in Business Intelligence: A Review. International Journal of Business Analytics, 7(2), 23-41.
[9] Zhang, Y., & Xie, B. (2019). Real-time business intelligence: Concept, architecture and applications. IEEE Access, 7, 169-180.
[10] Vasdev K. “Exploration and Production Optimization in Oil and Gas Using GIS”. J Artif Intell Mach Learn & Data Sci 2023, 1(1), 1903-1906. DOI: doi.org/10.51219/JAIMLD/kirti-vasdev/421
[11] Gupta, A., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
[12] Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of Winning. Harvard Business Review Press.
[13] Puneet Aggarwal,Amit Aggarwal. "Empowering Intelligent Enterprises: Leveraging SAP's SIEM Intelligence for Proactive Cybersecurity", International Journal of Computer Trends and Technology, 72 (10), 15-21, 2024.
[14] Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171-209.
[15] Oracle Corporation. (2023). Cloud Data Warehousing and Analytics Best Practices. Oracle White Paper.
[16] Holsapple, C. W., & Sena, M. P. (2005). A knowledge management ontology. Expert Systems with Applications, 29(1), 77-89.
[17] Gartner, Inc. (2024). Magic Quadrant for Analytics and Business Intelligence Platforms. Gartner Report.
[18] Russom, P. (2011). Big data analytics. TDWI Best Practices Report.
[19] Puvvada, R. K. "The Impact of SAP S/4HANA Finance on Modern Business Processes: A Comprehensive Analysis." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11.2 (2025): 817-825.
[20] Amazon Web Services, Inc. (2023). Amazon QuickSight User Guide. AWS Documentation.
[21] Kirti Vasdev (2022). “GeoLocation-Cell Tower Capacity Planning”. Journal of Engineering and Applied Sciences Technology. SRC/JEAST-161. 4(1), PP, 1-4. DOI: doi.org/10.47363/JEAST/2022(4)E161
[22] K. R. Kotte, L. Thammareddi, D. Kodi, V. R. Anumolu, A. K. K and S. Joshi, "Integration of Process Optimization and Automation: A Way to AI Powered Digital Transformation," 2025 First International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies (CE2CT), Bhimtal, Nainital, India, 2025, pp. 1133-1138, doi: 10.1109/CE2CT64011.2025.10939966.
[23] Venu Madhav Aragani, 2025, “Implementing Blockchain for Advanced Supply Chain Data Sharing with Practical Byzantine Fault Tolerance (PBFT) Alogorithem of Innovative Sytem for sharing Suppaly chain Data”, IEEE 3rd International Conference On Advances In Computing, Communication and Materials.
[24] L. N. R. Mudunuri, V. M. Aragani, and P. K. Maroju, "Enhancing Cybersecurity in Banking: Best Practices and Solutions for Securing the Digital Supply Chain," Journal of Computational Analysis and Applications, vol. 33, no. 8, pp. 929-936, Sep. 2024.
[25] Praveen Kumar Maroju, Venu Madhav Aragani (2025). Predictive Analytics in Education: Early Intervention and Proactive Support With Gen AI Cloud. Igi Global Scientific Publishing 1 (1):317-332.
[26] Mohanarajesh, Kommineni (2024). Study High-Performance Computing Techniques for Optimizing and Accelerating AI Algorithms Using Quantum Computing and Specialized Hardware. International Journal of Innovations in Applied Sciences and Engineering 9 (`1):48-59.
[27] Animesh Kumar, “AI-Driven Innovations in Modern Cloud Computing”, Computer Science and Engineering, 14(6), 129-134, 2024.
[28] Innovative Design Of Refining Muscular Interfaces For Implantable Power Systems, Sree Lakshmi Vineetha Bitragunta ,International Journal of Core Engineering & Management, Volume-6, Issue-12, 2021,PP-436-445.
[29] Sahil Bucha, “Integrating Cloud-Based E-Commerce Logistics Platforms While Ensuring Data Privacy: A Technical Review,” Journal Of Critical Reviews, Vol 09, Issue 05 2022, Pages1256-1263.
[30] Barigidad, S. (2025). Edge-Optimized Facial Emotion Recognition: A High-Performance Hybrid Mobilenetv2-Vit Model. International Journal of AI, BigData, Computational and Management Studies, 6(2), 1-10. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I2P101
[31] Optimizing Boost Converter and Cascaded Inverter Performance in PV Systems with Hybrid PI-Fuzzy Logic Control - Sree Lakshmi Vineetha. B, Muthukumar. P - IJSAT Volume 11, Issue 1, January-March 2020,PP-1-9,DOI 10.5281/zenodo.14473918
[32] Bhagath Chandra Chowdari Marella, “Driving Business Success: Harnessing Data Normalization and Aggregation for Strategic Decision-Making”, International Journal of Intelligent Systems And Applications In Engineering, vol. 10, no.2, pp. 308 – 317, 2022. https://ijisae.org/index.php/IJISAE/issue/view/87
[33] Kiran Nittur, Srinivas Chippagiri, Mikhail Zhidko, “Evolving Web Application Development Frameworks: A Survey of Ruby on Rails, Python, and Cloud-Based Architectures”, International Journal of New Media Studies (IJNMS), 7 (1), 28-34, 2020.
[34] Jagadeesan Pugazhenthi, V., Singh, J., & Pandy, G. (2025). Revolutionizing IVR Systems with Generative AI for Smarter Customer Interactions. International Journal of Innovative Research in Computer and Communication Engineering, 13(1).
[35] Sumaiya Noor, Salman A. AlQahtani, Salman Khan, “ XGBoost-Liver: An Intelligent Integrated Features Approach for Classifying Liver Diseases Using Ensemble XGBoost Training Model”, Computers, Materials and Continua, Volume 83, Issue 1, 2025, Pages 1435-1450, ISSN 1546-2218, https://doi.org/10.32604/cmc.2025.061700.(https://www.sciencedirect.com/science/article/pii/S1546221825002632).
[36] Venkata Krishna Reddy Kovvuri. (2024). Next-Generation Cloud Technologies: Emerging Trends In Automation And Data Engineering. International Journal Of Research In Computer Applications And Information Technology (Ijrcait),7(2),1499-1507.
[37] Settibathini, V. S., Virmani, A., Kuppam, M., S., N., Manikandan, S., & C., E. (2024). Shedding Light on Dataset Influence for More Transparent Machine Learning. In P. Paramasivan, S. Rajest, K. Chinnusamy, R. Regin, & F. John Joseph (Eds.), Explainable AI Applications for Human Behavior Analysis (pp. 33-48). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-1355-8.ch003
[38] Vootkuri, C. (2025). Multi-Cloud Data Strategy & Security for Generative AI.
[39] Priscila, S. S., Celin Pappa, D., Banu, M. S., Soji, E. S., Christus, A. T., & Kumar, V. S. (2024). Technological Frontier on Hybrid Deep Learning Paradigm for Global Air Quality Intelligence. In P. Paramasivan, S. Rajest, K. Chinnusamy, R. Regin, & F. John Joseph (Eds.), Cross-Industry AI Applications (pp. 144-162). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-5951-8.ch010