Data consolidation into the database of the end-to-end analytics system

Abstract

The relevance of the study is driven by the need to improve data collection, processing, and consolidation processes in databases for end-to-end analytics, which is a crucial component of modern company management. The conditions of increasing competition and the growing volume of generated data require companies to implement effective tools for integrating data from heterogeneous sources to ensure comprehensive analysis of customer behavior and optimization of marketing decisions.

The aim of this study is to formulate approaches to consolidating data from various sources into a unified database of an end-to-end analytics system, taking into account technological specifics.

The research methods include constructing a generalized data collection scheme with subsequent detailing of specific processes for integrating information from systems such as Google Analytics, advertising web resources, and call-tracking systems. Based on the proposed models, ETL processes, including data cleansing, normalization, aggregation, and structuring, are examined.

The research results demonstrate that employing a database as the central element of end-to-end analytics ensures the possibility of data consolidation, tracking customer behavior, and optimizing managerial decisions. Models for integrating data from key sources, such as Google Analytics, advertising web resources, and call-tracking systems, have been developed. It has been shown that structured data in a database facilitates the creation of reports, visualizations, in-depth business analysis, and the generation of recommendations.

The conclusions emphasize that the proposed approaches to data consolidation in databases of end-to-end analytics systems are effective in meeting managerial needs. Future research prospects include integrating additional data sources and expanding the functionality of the end-to-end analytics system by automating the analysis processes and building recommendation models.