Analysis of Approaches and Solutions for Developing a Customer Data Storage System for the Banking Ecosystem

The article is devoted to the analysis of approaches and solutions for the development of a single, scalable and highly loaded customer data warehouse within the bank’s digital ecosystem. In the context of digital transformation and the development of digital ecosystems, large organizations, including banks, are faced with the fact that the storage and processing of customer data is becoming a critical issue for ensuring company processes, personalization of services for customers, compliance with regulatory requirements and maintaining a competitive advantage. The purpose of the research is to determine the optimal set of architectural principles and technological solutions applicable to building a scalable, fault-tolerant and high-performance customer data storage system. The following methods were used to conduct the research: system analysis and synthesis to study the specifics of the subject area of the banking digital ecosystem, comparative analysis to evaluate data storage technologies based on criteria such as performance, scalability, data consistency, fault tolerance, and analysis of scientific literature and information sources to study the subject area, modern solutions and technologies in systems data storage. Based on the conducted research, optimal patterns and technologies for implementing a customer data storage platform have been selected and recommendations have been formulated for designing a single customer storage architecture capable of operating effectively in the dynamic IT landscape of the modern banking ecosystem. Based on the results of the study, it was concluded that other scientific papers and articles also confirm that the use of hybrid data warehouses using different technologies and solutions is most optimal for building highly loaded and scalable data storage and processing systems. The prospects of the research include further in-depth study of intelligent data management systems, data storage and processing used in machine learning and AI, Data Mesh and Data Fabric approaches and their application in the banking sector.

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