Optimization of Internal Control of Budgetary Operations in Public Utilities Based on Big Data Intelligence

Abstract

The optimization of internal control of budgetary operations in public utilities, leveraging big data intelligence, is crucial for enhancing efficiency, transparency, and accountability in financial management. This abstract explores the integration of big data technologies to streamline budgetary processes and mitigate risks in public utility operations. In this context, big data analytics offer the capability to analyze vast volumes of data generated from various sources within public utilities, including financial transactions, operational activities, and regulatory compliance. By harnessing advanced analytical techniques such as predictive modeling, machine learning, and data visualization, organizations can gain valuable insights into budget utilization patterns, identify potential fraud or irregularities, and optimize resource allocation strategies. Furthermore, the implementation of robust internal control mechanisms supported by big data intelligence enables real-time monitoring of budgetary operations, detection of anomalies, and timely intervention to prevent financial losses or mismanagement. This proactive approach enhances the reliability and accuracy of financial reporting, strengthens compliance with regulatory requirements, and fosters public trust in the management of public funds. Overall, the optimization of internal control of budgetary operations through big data intelligence empowers public utilities to achieve greater operational efficiency, cost-effectiveness, and governance effectiveness in managing their financial resources.