METHODS OF DATA PROCESSING AND ANALYSIS IN A CORPORATE NETWORK

Relevance . The relevance of studying methods of data processing and analysis in corporate networks is driven by the rapid development of information technologies, the growing volume of corporate data, and the need for prompt and accurate analysis to support effective decision-making. The importance of such methods is also linked to the challenges of ensuring cybersecurity in corporate networks, particularly the protection of information at all stages of its processing and transmission. The quality of the technologies and methods applied to data handling affects a company’s performance, competitiveness, and ability to adapt to rapidly changing market conditions. Therefore, systematizing knowledge about the most effective methods of working with corporate data remains a relevant task for both modern science and practical applications. The object of research is data processing processes within corporate networks, including the collection, transmission, storage, filtering, analysis, and protection of information circulating within the information and communication infrastructure of a modern enterprise. These processes are considered in the context of their impact on the efficiency of the corporate information system, data security, support for managerial decision-making, and integration with analytical and cloud platforms. Purpose of the article is to investigate the main stages and methods of data processing and analysis in the corporate environment, including data collection, preprocessing, storage, analytical evaluation, and information security, as well as to analyze modern tools and platforms that ensure efficient and secure data handling at the scale of a large organization. Research results. During the study, a comprehensive analysis of the stages and methods of data processing in the corporate environment was conducted. A systemic model of the corporate data lifecycle was established, covering all key stages – collection, preprocessing, storage, analytics, and protection. Each of these stages requires the application of a specific class of technologies and presents its own implementation challenges. Tools for data collection and preprocessing were classified, with ETL processes, logging, aggregation technologies, sampling, and normalization playing an important role in ensuring data cleanliness and suitability for further analysis. Modern data storage platforms were analyzed, including cloud-based (Azure, AWS, GCP) and on-premises solutions. The effectiveness of using OLAP and machine learning in analytical processing was evaluated. The role of information security in corporate networks was addressed. The necessity of implementing cryptographic protection, access control, as well as anonymization and masking mechanisms was demonstrated as a response to the risks of data loss or compromise. Conclusions. Modern methods of data processing in corporate networks have been examined, covering the stages of data collection, preprocessing, storage, analytics, and information security. It has been established that effective data management is achievable only through a comprehensive approach that combines technical tools, software platforms, and security policies. Particular attention was given to the use of cloud technologies, ETL tools, OLAP analytics, and machine learning algorithms. The importance of cryptographic protection, anonymization, and access control was emphasized. The obtained results can be applied to improve the efficiency of enterprise information systems and to implement analytical solutions in business practice.