Real-Time Predictive Analytics: A Framework for Dynamic Decision Intelligence in Event-Driven Architectures
Year: 2025
Type: article
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.
Author: Sudhakar Reddy Vyza
Institution: Discover Financial Services (United States)
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4407595032