Semantic integrity in data warehousing: a framework for understanding

Explores the importance of semantic integrity during data warehouse design and its impact on the successful use of the implemented warehouse. This was achieved through a detailed case study. Consequently, a conceptual framework for describing inter-subjective meaning in data modelling has been developed. The purpose of the framework is to provide a theoretical basis for explaining how a data model is interpreted through the meaning levels of understanding, connotation and generation, and also how a data model is created from an existing meaning structure by intention, generation and action. This paper also describes inhibiting factors for understanding the physical data model based on the case study findings. Furthermore strategies are suggested to address the factors inhibiting the generation of meaning from a data model, in particular for improving understanding. The result of the exploration is the recognition that the implementation of a data warehouse may not assist with providing a detailed understanding of the semantic content of the data warehouse.