Real-Time Analytics for Intelligent Business Decision-Making: An Integrative Framework and Empirical Synthesis

Real-Time Analytics for Intelligent Business Decision-Making: An Integrative Framework and Empirical Synthesis

Abstract

The exponential rise in digital data within corporate ecosystems has dramatically transformed the nature of business decision-making, which now requires advanced analytical techniques to handle voluminous amounts of information in real time. This paper provides a comprehensive review of real-time analytics as an innovative approach within business intelligence and decision-support systems, based on evidence from 30 studies published between 2024 and 2026. The Real-Time Decision Intelligence Model is presented to demonstrate how streaming-data systems serve as an intermediary layer between organizational data and decision-making outcomes. The study finds that enterprises using real-time analytics gain significant advantages in decision-making speed, forecasting accuracy, and operational efficiency, including an 18.3% reduction in fraud in financial services and a 67% decrease in cybersecurity threat-detection latency. The article also identifies important research gaps in real-time analytics.

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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