Real-Time Business Intelligence Systems for Enterprises
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DOI:
https://doi.org/10.67228/3071642X/IJCFDE-2024PI3W6JPublished 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
ArticlesAbstract
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.
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