Methodological Aspects of Data Management at the Enterprise
This article explores the formation of a unified methodological framework for data management within modern enterprises. The relevance of the topic stems from ongoing changes in economic and social structures, driven by digital transformation and the increasing importance of information as a critical resource. Data is now recognized as one of the most valuable assets in contemporary business. Companies striving not only to achieve their business objectives but also to ensure long-term, sustainable market performance face the urgent need to develop tools and workflows for managing data of varying quality and formats. The aim of this study is to examine both fundamental and forward-looking methodological aspects of data handling and to identify the key characteristics of this process. Data management — across its many forms, as discussed in the article — is conceptualized as a comprehensive system based on a digital model that supports the effective operation of businesses in the face of new technological implementations. The authors identify the structural elements of such a system and highlight the interdependence between the development of methodological approaches to data management and the broader digital transformation of companies. The research methodology includes analysis, deduction, and analogy. The findings may be of interest to both domestic and international researchers for further study in the field of enterprise data management methodology, to business professionals seeking to optimize their data management strategies, and to students and postgraduate scholars studying enterprise data management.
Authors
- Elena Nikolaevna Sheremetyeva (ORCID: https://orcid.org/0000-0002-1855-7291)
- Yu. G. Popov
- A. K. Adgizalova
Institutions
- Samara State University of Economics (RU)
Publication Details
- Journal
- Management Sciences
- Published
- 2025-06-24
- DOI
- https://doi.org/10.26794/2304-022x-2025-15-2-116-129
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00