Big Data Without Mathematical Thinking: A Dangerous Illusion in the Digital Economy

Abstract

This study explores the widespread misconception that big data alone is sufficient to support effective decision-making in the digital economy without the need for mathematical thinking. Using a qualitative library research approach, this study examines a wide range of primary and secondary sources from the fields of data science, economics, and decision theory. The analysis aims to understand the relationship between data availability and the role of mathematical reasoning in transforming data into meaningful insights. The findings reveal that although big data provides vast and diverse information, it does not automatically generate accurate or useful conclusions without proper mathematical interpretation. Mathematical thinking plays a critical role in organizing data, identifying patterns, and developing reliable predictive models. Without such analytical frameworks, data can easily be misinterpreted, leading to flawed conclusions and ineffective or even harmful strategic decisions. Furthermore, the absence of mathematical reasoning increases the likelihood of cognitive bias, overfitting, and erroneous analysis, particularly in complex and dynamic environments. These risks highlight the limitations of relying solely on data volume without adequate analytical rigor. Therefore, this study argues that treating big data as a standalone solution is a dangerous illusion. Integrating mathematical frameworks is essential to ensure valid, reliable, and evidence-based decision-making in the digital economy.