INFORMATION TECHNOLOGIES FOR THE INTEGRATION OF CUSTOMER AND CONSUMER DATA

Abstract

Using the example of a book enterprise that combines the functions of a publisher, distributor, and retailer, it is shown how multi-channel operational activities lead to the accumulation of vast arrays of information in databases that are fragmented, incomplete, unstructured, and contain duplicates. This situation makes it impossible to effectively analyze customer behavior, including the accurate calculation of key performance indicators. The relevance of the work lies in reducing this critical gap between the volume of accumulated information and the business's ability to make effective management decisions based on it. The purpose of this work is to develop a methodological approach to creating a data warehouse based on the star schema architecture and to implement an adaptive ETL chain with built-in quality control rules. An analysis of modern data warehouse design methods was conducted, including the transition from the entity-relationship model to the star schema. Based on the structure of the transactional database and business requirements for data analysis, an analytical warehouse using the star schema was designed, and key facts and dimensions necessary to support comprehensive customer analytics were identified. To transfer data from the transactional system to the warehouse, an extract, transform, and load (ETL) process was developed, and its logic was described: data extraction from sources, its cleaning and transformation in a staging area, and loading into the target warehouse tables. The effectiveness of the developed processes was evaluated based on event log data. The analysis results confirm the reliability and high performance of the proposed solution. The approach proposed in the article provides automated, reliable, and efficient updating of the data warehouse, creating a single source of truth for business analytics.