Identifying Quality Factors within Data Warehouse

Abstract

The popularity of data warehouses for data analysis has grown tremendously. Data warehouse systems have emerged as the core of management information systems. Data warehouse is part of a larger infrastructure that includes legacy data sources, external data sources, data acquisition software, a repository, analytical tools, and user interface. The difficulties of data warehouse implementations have been widely cited in the literature but research on the factors for initial and ongoing data warehouse implementation success is rare and fragmented. Through a comprehensive review of the literature, 10 factors were found to be critical to DW implementation success — Organizational, Technical, Project, Environmental, Infrastructure, Information Quality, System Quality, Service Quality, Relationship Quality, and Net Benefits. A proposed research model is developed in this paper to determine the impact of quality and success factors on the implementation of data warehouse by adapting the updated (2003) DeLone and McLean Information System Success Model, new dimensions are proposed to the model.

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