Real-Time Predictive Analytics: A Framework for Dynamic Decision Intelligence in Event-Driven Architectures

Abstract

This article presents a comprehensive examination of real-time predictive analytics systems, focusing on their integration with modern data pipeline architectures for enhanced decision intelligence. The article proposes a novel framework that combines event-driven processing with adaptive machine learning models to enable dynamic decision-making in complex environments. The article investigates the architectural components necessary for processing high-velocity data streams while maintaining prediction accuracy and system reliability. Through multiple case studies across financial markets and supply chain operations, the article demonstrates the framework's effectiveness in supporting real-time decision-making processes. The article highlights the significance of optimized pipeline architectures in reducing latency while maintaining model accuracy, contributing to both theoretical understanding and practical implementation of real-time predictive systems. The article also addresses critical challenges in scalability, fault tolerance, and model adaptation, providing insights for future developments in the field of real-time analytics.