Data Warehouses as Tools in Supporting Decision-Making Processes in Management

Purpose: The purpose of this article is to analyse the role of data warehouses as a tool supporting decision-making processes in management, identify the key benefits resulting from their implementation, and indicate challenges and best practices in the effective use of data analysis systems. Design/Methodology/Approach: The study is based on a literature review and analysis of the technological and organizational aspects of data warehouse implementation. It also incorporates case studies of enterprises that have successfully leveraged data warehouses to improve management efficiency and strategic decision-making. The research question is formulated as: How do data warehouses influence decision-making processes within organizations, and which operational mechanisms are most effective in improving the quality of decisions made? On this basis the following research hypothesis has been stated: the implementation of data warehouses, including the integration of data from multiple sources, the use of advanced analytical tools, and the automation of reporting processes, significantly enhances the quality of managerial decisions, thereby increasing organizational efficiency. Findings: the findings of the analysis indicate that the effective implementation of a data warehouse significantly supports decision-making processes within an organization by consolidating and analysing data from various sources. This enables managers to make swift decisions based on comprehensive reports and forecasts derived from historical data. The main challenges associated with implementing a data warehouse include high implementation costs, the need to ensure data consistency and quality, and a lack of appropriate skills among employees. Best practices, such as integrating the data warehouse with Business Intelligence systems, using advanced analytical algorithms, and implementing mechanisms for automatic data updates, contribute to improving management efficiency and minimizing the risk of incorrect decisions. Practical implications: the study provides practical recommendations for organizations planning to implement or modernize data warehouses to improve the quality of decision-making processes. Utilizing modern analytical technologies such as big data and artificial intelligence enables more precise and dynamic decision-making. Regular employee training in data analysis and optimizing ETL processes can further enhance the efficiency of data warehouses. Originality/value: The article also emphasizes the need for further research on the role of modern technologies in data integration and their impact on innovative organizational management models.