The Impact of Data Volume and Analytical Complexity in Big Data Technology on Financial Performance Prediction in Financial Companies in Indonesia

Abstract

This quantitative research explores the intricate relationship between data volume, analytical complexity, and their combined impact on financial performance prediction across 25 Indonesian financial companies. This study utilizes multiple regression analysis and statistical techniques using IBM SPSS Statistics version 26 to uncover the dynamics at play. The findings show that data volume and analysis complexity have a significant and positive role in improving financial performance prediction. The symbiotic relationship between these factors underscores the importance of adopting a holistic approach to data management and analysis. The results of this study have profound implications for financial professionals, data scientists, and decision makers, as it provides a roadmap for utilizing Big Data technology for more accurate and informed decision making in the Indonesian financial sector.