Generative AI-Driven Business Intelligence and Cloud Data Engineering for Real-Time Intelligent Decision-Making
DOI:
https://doi.org/10.15662/IJEETR.2024.0605028Keywords:
Generative AI, Automated Business Intelligence, Cloud Data Engineering, Real-Time Analytics, Decision Support, Data Pipelines, Enterprise AnalyticsAbstract
Automated Business Intelligence (BI) is evolving through the integration of Generative Artificial Intelligence (GenAI) and cloud-based data engineering, enabling organizations to transform large volumes of heterogeneous data into timely and actionable business insights. Traditional BI environments often depend on predefined dashboards, manual data preparation, and periodic reporting, which can limit responsiveness to rapidly changing business conditions. This research proposes an automated BI framework that combines cloud data ingestion, scalable data processing, machine learning, Generative AI, semantic analytics, and natural-language interaction to support real-time decision-making. The framework integrates structured and unstructured enterprise data through automated pipelines, performs data quality validation and transformation, and maintains continuously updated analytical repositories. GenAI components generate contextual summaries, explain analytical findings, identify emerging patterns, and translate business questions into data-driven responses. Cloud-native orchestration enables elastic resource allocation, fault tolerance, and continuous processing across distributed data sources. The proposed approach emphasizes data governance, security, explainability, and controlled AI generation to improve the reliability of automated insights. The framework is intended to reduce analytical latency, minimize manual reporting effort, and enhance decision responsiveness. By integrating intelligent analytics with modern cloud data engineering, organizations can establish an adaptive BI environment capable of delivering continuous, contextual, and business-oriented decision support.
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