Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{187448,
        author = {Vedant Vaity},
        title = {Real-Time Business Intelligence: Enhancing Decision-Making with Stream Analytics in Cloud Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {5344-5348},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187448},
        abstract = {The exponential growth of big data, driven by the proliferation of the Internet of Things (IoT), mobile devices, and cloud computing, has transformed the landscape of business intelligence (BI). Traditional batch processing methods are increasingly insufficient for organizations requiring timely insights to make critical decisions. This research paper investigates the implementation of real-time business intelligence (RTBI) through stream analytics in cloud environments. Drawing upon contemporary literature and recent advancements, the paper provides a comprehensive examination of streaming analytical workflows, platforms, security and privacy challenges, and adaptive frameworks for dynamic data environments. The study also highlights gaps in existing research, particularly in integrating spatial-temporal data and ensuring security in multi-cloud deployments. The objective is to propose a holistic approach for designing and implementing robust, scalable, and secure RTBI systems leveraging cloud-based stream analytics. The findings suggest that a combination of cloud-based architectures, adaptive analytics algorithms, and robust security protocols is essential for effective real-time decision-making in modern business contexts. Recommendations for future research include the fusion of fog computing for latency-sensitive tasks and advanced privacy- preserving mechanisms for sensitive data streams.},
        keywords = {},
        month = {November},
        }