УПРАВЛІННЯ ТА ІНТЕГРАЦІЯ ДАНИХ В УМОВАХ ЦИФРОВІЗАЦІЇ ЕКОНОМІЧНИХ ПРОЦЕСІВ: ВИКЛИКИ ТА ПЕРСПЕКТИВИ

Abstract

Objective. The purpose of the article is to clarify the theoretical and methodological aspects, analyze data management methods in the context of digitalization of economic processes, and choose the priority method of integrating corporate information systems depending on the tasks to be solved in each case. Methods. The paper uses a set of data integration methods: application integration method (EAI), method of extracting data from external sources, transforming them in the appropriate structure and forming data warehouses (ETL); method of real-time integration of incomparable data types from different sources (EI). Results. The paper proves that data management includes the formation and analysis of data architecture, integration of the database management system; data security, identification, segregation and storage of data sources. Data integration refers to the process of combining data from different sources into a single, holistic system and aims to provide access to a complete, updated and easy-to-analyze data set. Data integration is especially important in the areas of e-commerce, logistics and supply chains, where it is necessary to combine data from different sources to optimize processes, in the field of business intelligence, where processing large amounts of data and combining them allows you to identify useful information and certain patterns. Integration of enterprise information systems is the process of combining several IS and individual applications into a single, holistic system that works together to achieve a common goal, aimed at increasing the efficiency of the company, reducing duplication of efforts and streamlining processes. The main functional components of a corporate information system are identified: Business Process Automation IS, Financial Management IS, Customer Relationship Management IS, Supply Chain Management IS, Human Resources Management IS, Business Intelligence IS, Communication IS, and Data Security and Protection IS. Within a corporate information system, several narrowly focused software products operate simultaneously, capable of successfully solving a certain range of tasks. At the same time, some of them may not involve interaction with other information systems. The main approaches to data integration include universal access to data and data warehouses. Universal access technologies allow for equal access to data from different information systems, including on the basis of the concept of data warehouses - a database containing data collected from databases of different information subsystems for further analysis and use. It is proved that the most holistic approach to the integration of information systems is integration at the level of business processes. As part of the integration of business processes, there is an integration of applications, data integration, and integration of people involved in this business process. The article substantiates the feasibility of using three methods of big data management and integration: integration of corporate applications, integration of corporate information, and software for obtaining, transforming, and downloading data. As a result of comparing integration methods and building a generalized scheme for integrating heterogeneous IS, a number of situations have been identified in which the use of a specific integration method is preferable or the only possible one. The scientific novelty of the study is to identify the problems of integrating big data and corporate information systems. Approaches to choosing a method for integrating data and applications based on a generalized scheme for integrating heterogeneous information systems are proposed. Practical significance. The results of the analysis allow optimizing the methods of data integration within a corporate information system. The principles of integration inherent in the considered methods are used to solve a wide range of tasks: from real-time integration to batch integration and application integration. Implementation of the proposed methods of big data integration will make information more transparent; obtain additional detailed information about the efficiency of production and technological equipment, which stimulates innovation and improves the quality of the final product; use more efficient, accurate analytics to minimize risks and identify problems in advance before catastrophic consequences; more effectively manage supply chains, forecast demand, carry out comprehensive business planning, organize cooperation