Use SQL and Python to Advance the Effect Analysis of Financial Data Automation

Abstract

With the rapid acceleration of enterprise informatization, the volume and complexity of accounting data have increased dramatically, often growing at an exponential rate. Traditional manual data management methods are no longer sufficient to handle such large-scale datasets, as they are inefficient, time-consuming, and prone to errors, which can negatively impact financial accuracy and decision-making. This paper addresses the challenge of efficiently managing accounting data by exploring the intelligent and combined application of SQL and Python. Specifically, it proposes a method in which SQL is utilized for structured data extraction and querying, while Python is employed to automate data processing, analysis, and the generation of comprehensive accounting reports. The study conducts a systematic evaluation of this combined approach across four critical dimensions: operational efficiency, computational accuracy, cost-effectiveness, and decision-support capability. Through empirical analysis and case studies, the research demonstrates that integrating SQL and Python can significantly streamline the accounting workflow, reduce human error, optimize resource utilization, and provide timely, data-driven insights for managerial decision-making. The results indicate that this integrated approach not only enhances the speed and reliability of accounting processes but also strengthens enterprises' capacity for accurate financial reporting and strategic planning, making it a highly effective solution for modern financial data management.