Data warehouse for EIS: some issues and impacts

The data warehouse is one of the most rapidly growing areas in management information systems. With this approach, data for EIS and DSS applications is separated from operational data and stored in a separate database called a data warehouse. Some of the advantages of this approach are improved performance, better data quality, and the ability to consolidate and summarize data from heterogeneous legacy systems. A data warehouse is part of a larger infrastructure that includes legacy data sources, external data sources, a repository, data acquisition software, and user interface and related analytical tools. A powerful form of data analysis, called multidimensional data analysis, is often performed by users of a data warehouse. Data warehouses can be organized into two-tier or three-tier client/server systems. Despite the complexity of the data warehouse environment little academic research has been performed in this area. This paper identifies a number of issues that arise in the context of developing and using a data warehouse. It develops a proposed research model to determine the impact of factors such as organizational factors, warehouse infrastructure, and management support on user satisfaction and development characteristics of a data warehouse.