DATA PROCESSING METHODS IN A CORPORATE NETWORK

Abstract

Relevance. In the context of modern digital business transformation, corporate networks have become critically important components of enterprise information infrastructure, integrating numerous systems, devices, and services into a unified information environment. Every day, vast volumes of data are generated, transmitted, stored, and processed within these networks, encompassing all aspects of organizational activity – from financial operations and logistics to customer communications and internal management processes. The successful functioning of a corporate network is impossible without effective data processing, which enables the extraction of valuable insights from raw data, supports strategic decision-making, ensures information security, and optimizes resources. Amid intense competition, the rapid growth of information flows, and increasing demands for swift decision-making, data processing methods play a crucial role in enabling the analytical capabilities of enterprises. Data processing methods in corporate environments find applications across diverse business sectors. In the financial domain, they are used for transaction analysis, risk management, market fluctuation forecasting, and fraud detection. In logistics, they support route optimization, warehouse stock control, and real-time supply coordination. In marketing, they facilitate customer behavior analysis, audience segmentation, and personalized communication. In HR departments, they assist in employee performance evaluation and candidate selection. In healthcare, they support patient data analysis, service process optimization, and clinical decision-making. Furthermore, data processing forms the foundation for the development of artificial intelligence systems, digital twins, cybersecurity solutions, and automated enterprise management. Traditional approaches to data processing are gradually being replaced by advanced technologies based on Big Data, real-time stream processing, cloud computing, intelligent data analytics, and machine learning. In this context, issues related to data protection, integrity, availability, and compliance with legal and regulatory requirements for confidentiality gain particular importance. The purpose is to analyze modern methods and technologies for data processing in corporate networks, identify their advantages, disadvantages, and application areas, and classify approaches to data collection, cleansing, storage, and analysis. The object of research is enterprise corporate networks. The subject of research includes methods and technologies for data processing, storage, analysis, and protection. The results. As a result of the conducted research, a comprehensive understanding of modern data processing methods in corporate environments was formed. The architectural models of enterprise information systems were analyzed, key stages of data preparation identified, and approaches to their processing – from classical to intelligent – were characterized. The review revealed a wide range of data processing techniques, from traditional SQL queries and multidimensional analysis to real-time processing and machine learning applications. The choice of a particular approach depends on data volume, data inflow speed, latency sensitivity, and the specific goals of processing.