REAL-TIME ANALYTICS FOR INTELLIGENT BUSINESS DECISION-MAKING: AN INTEGRATIVE FRAMEWORK AND EMPIRICAL SYNTHESIS
Abstract
The exponential growth of digital data within corporate ecosystems has fundamentally transformed business decision-making, creating the need for advanced analytical techniques capable of processing and interpreting large volumes of information in real time. This paper presents a comprehensive review of Real-Time Analytics (RTA) as an emerging approach in business intelligence and decision support systems by synthesizing evidence from thirty contemporary studies published between 2024 and 2026.
Drawing upon concepts from information systems theory, decision-making processes, and intelligent computation, the study examines the core architecture of real-time analytics, the integration of Artificial Intelligence (AI) techniques, and practical implementation strategies across diverse business domains. The paper introduces the Real-Time Decision Intelligence Model (RT-DIM), a conceptual framework illustrating how streaming data systems function as an intermediary layer between organizational data sources and strategic decision-making outcomes.
The findings demonstrate that organizations adopting real-time analytics achieve substantial improvements in decision-making speed, forecasting accuracy, and operational efficiency. The empirical synthesis highlights measurable benefits across industries, including an 18.3% reduction in fraud within financial services and a 67% decrease in cybersecurity incident response time. The study concludes that real-time analytics, when integrated with AI-driven decision intelligence, provides organizations with a significant competitive advantage and offers a robust framework for intelligent, data-driven business decision-making.
Authors
Amit Yadav, Shreya, Vickey Kumar, Vimal Singh, Vishal Kumar
Institution
Noida Institute of Engineering & Technology (MCA Institute), Greater Noida, India
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