Digital Business Transformation. Ways to Use Spatial Analysis, Databases and Query Languages

This article explores a current issue in business digital transformation ‒ the implementation of information technologies, particularly geographic information systems (GIS), to address tasks in the field of geomarketing. The focus of analysis is on service organization challenges that require consideration of spatial characteristics. The primary objective of the research is to formalize the process of buffer analysis and demonstrate its practical application in a business context. The integration of spatial data into marketing research enhances the effectiveness of analysis and accelerates goal achievement. The study emphasizes the importance of using large volumes of descriptive data on research objects and processing tools such as databases and query languages. Special attention is given to the potential of GIS and big data analytics in solving economic and managerial problems. The key task of the article is to perform a geomarketing analysis using buffer operations, which involves identifying a specific area around a target object. To ensure reliable results, it is essential to account for the spatial dynamics of multiple indicators, requiring the use of advanced tools ‒ specialized GIS platforms and spatial databases. The methodological foundation of the study lies in systems analysis and geostatistical methods, which support the transformation of data from discrete to continuous representations during processing. The spatial analysis conducted is closely linked with relational algebra, which underpins query formulation in spatial databases. The results demonstrate the potential of buffer operations to increase the accuracy of analytics and reduce the time needed for marketing tasks. Particular emphasis is placed on combining buffer analysis with relational spatial data processing as a promising methodology in modern spatial analytics. Future research should focus on automating such processes and integrating them with machine learning and Big Data technologies to enhance decision-making efficiency.