Real-Time Business Intelligence Systems for Enterprises

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India. Author

    DOI:

    https://doi.org/10.67228/3071642X/IJCFDE-2024PI3W6J

    Published 04-05-2024

  • Real-Time Business Intelligence, Enterprise Analytics, Business Intelligence Systems, Streaming Analytics, Artificial Intelligence, Machine Learning, Cloud Computing, Event-Driven Architecture, Big Data Analytics, Decision Support Systems, Data Visualization, Predictive Analytics

    Issue

    Section

    Articles

    How to Cite

    N, S. (2024). Real-Time Business Intelligence Systems for Enterprises. International Journal of Commerce, Finance and Digital Economy, 7(1), 01-15. https://doi.org/10.67228/3071642X/IJCFDE-2024PI3W6J
  • Abstract

    Real-Time Business Intelligence (RTBI) enables enterprises to analyze and act on data instantly, unlike traditional BI systems that rely on historical batch processing. By combining streaming analytics, cloud computing, AI/ML, and distributed data platforms, RTBI supports rapid decision-making, operational optimization, customer engagement, and risk management across industries such as finance, healthcare, manufacturing, retail, and logistics. Modern RTBI systems integrate data from ERP, CRM, IoT devices, cloud applications, and digital transactions using technologies such as Apache Kafka, Apache Flink, and Spark Structured Streaming. The framework emphasizes real-time data ingestion, predictive analytics, automated decision support, and interactive dashboards while evaluating performance through metrics such as latency, throughput, data freshness, and decision accuracy. RTBI offers major benefits including faster decisions, greater operational visibility, improved resource utilization, and competitive advantage. Key challenges include scalability, data integration, cybersecurity, governance, privacy, infrastructure cost, and AI interpretability. Future directions include Explainable AI, Edge Intelligence, Federated Learning, Digital Twins, and Generative AI-based decision systems, making RTBI a foundational technology for next-generation intelligent enterprises.

  • References

    [1] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 4th ed. Cambridge, MA, USA: Morgan Kaufmann, 2023.

    [2] A. Gandomi and M. Haider, "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, vol. 62, pp. 102433, 2022.

    [3] M. Zaharia et al., "Apache Spark: A unified engine for big data processing," Communications of the ACM, vol. 63, no. 11, pp. 82–91, Nov. 2020.

    [4] J. Kreps, N. Narkhede, and J. Rao, "Kafka: A distributed messaging system for log processing and stream analytics," IEEE Software, vol. 38, no. 3, pp. 92–99, 2021.

    [5] P. Carbone et al., "Apache Flink: Stream and batch processing in a single engine," IEEE Data Engineering Bulletin, vol. 44, no. 2, pp. 28–39, 2021.

    [6] S. Newman, Building Microservices, 2nd ed. Sebastopol, CA, USA: O'Reilly Media, 2021.

    [7] B. Burns, B. Grant, D. Oppenheimer, E. Brewer, and J. Wilkes, Kubernetes: Up and Running, 3rd ed. Sebastopol, CA, USA: O'Reilly Media, 2022.

    [8] T. Erl, R. Puttini, and Z. Mahmood, Cloud Computing: Concepts, Technology and Architecture, Updated ed. Pearson, 2021.

    [9] D. G. Harkut and M. Kasat, "Survey on machine learning algorithms for smart business analytics," Journal of Big Data, vol. 8, no. 1, pp. 1–23, 2021.

    [10] A. Adadi and M. Berrada, "Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI," Information Fusion, vol. 79, pp. 1–20, 2022.

    [11] T. Chen and C. Guestrin, "XGBoost: Scalable tree boosting system for predictive analytics," IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 2, pp. 1132–1145, 2023.

    [12] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2021.

    [13] A. Raschka, Y. Liu, V. Mirjalili, and J. Dzhulgakov, Machine Learning with PyTorch and Scikit-Learn. Birmingham, U.K.: Packt Publishing, 2022.

    [14] C. C. Aggarwal, Artificial Intelligence: A Textbook, Cham, Switzerland: Springer, 2023.

    [15] H. V. Jagadish, A. Labrinidis, and Y. Papakonstantinou, "Data engineering for real-time analytics: Challenges and future directions," IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 4, pp. 1450–1465, 2024.

  • Downloads

Similar Articles

1-10 of 89

You may also start an advanced similarity search for this article.