Data-Driven Decision Making: Advanced Database Systems for Business Intelligence

Data-driven decision-making is integral to modern businesses, relying heavily on Business Intelligence (BI) systems to analyze vast and complex datasets for actionable insights. Traditional BI systems often struggle with high query latency, limited scalability, insufficient predictive capabilities, and inadequate security measures, hindering their effectiveness in dynamic data environments. This paper presents an AI augmented database system designed to address these challenges within BI applications. The proposed system integrates AI driven query optimization, real-time data processing, advanced predictive analytics, and enhanced security protocols. The methodology involves implementing machine learning models for dynamic query prediction and caching, utilizing recurrent neural networks for improved time-series forecasting, and employing isolation forests for robust anomaly detection. Security is reinforced through role-based access control and AES-256 encryption. Experimental evaluations demonstrate a significant reduction in query latency by 77% for high-frequency queries, achieving an average response time of 15 ms compared to 65 ms in traditional systems. The predictive analytics model reduces the Mean Absolute Error to 0.54, outperforming conventional ARIMA models. Anomaly detection achieves a precision of 0.89 and recall of 0.92, indicating high reliability.