TESTING HYPOTHESES IN IOT BUSINESS INTELLIGENCE: LEVERAGING BIG DATA ANALYTICS AND ADVANCED TECHNIQUES

Abstract

In an effort to improve operational effectiveness and strategic decision-making, this study explores how Big Data Analytics (BDA) and the Internet of Things (IoT) can be integrated inside the Business Intelligence (BI) framework. In order to handle the enormous datasets produced by IoT devices, the research investigates cutting-edge analytical methods including machine learning and predictive analytics. The suggested framework is evaluated using eight critical performance measures, such as data processing speed, integration efficiency, prediction accuracy, and accuracy of hypothesis testing, versus more established analytical techniques (SmartPLS, SPSS, and PLS-SEM). The IoT-BDA integrated BI framework performs noticeably better than conventional approaches, according to the results, especially in terms of real-time processing and system scalability. Enabling IoT and BDA in BI can result in more accurate, efficient, and scalable data-driven decision-making systems, giving businesses a competitive edge. An ablation study confirms the significance of each component within the framework, emphasizing that eliminating components such as machine learning algorithms or real-time data processing significantly degrades performance.