Big Data Analytics Frameworks for Decision Support in Modern Information Systems

Abstract

The exponential growth of data in modern information systems has created both opportunities and challenges for organizational decision-making. Big Data Analytics (BDA) frameworks provide scalable, high-performance solutions to extract meaningful insights from heterogeneous and voluminous datasets. These frameworks integrate data processing, storage, and advanced analytical tools, enabling predictive, prescriptive, and real-time decision support. This scholarly review explores contemporary BDA architectures, distributed computing models, machine learning integration, and deployment strategies in enterprise environments. It also discusses performance evaluation, security, and scalability considerations, offering insights into future research directions for enhancing decision-making capabilities through big data analytics. [1–5]