Analysis Patterns in Dimensional Data Modeling

Abstract

The construction of conceptual dimensional data models is one of the most important, fundamental and challenging tasks during the analysis phase in the systems development life cycle of a data warehouse system. Such data models are representing operational as well as strategic business requirements. Dimensional data models are used for implementing dimensional databases within the data warehouse system, which itself will be used for generating crucial information for decision-making. Although the enormous importance of conceptual dimensional data models is well known, the use of approved analysis patterns is not common practice. The non-consideration of analysis patterns can yield to poorly planned and therefore qualitative unproven dimensional data models respectively databases, which similarly yields to qualitative unproven generated decision-relevant information. Up to now the use of analysis patterns in dimensional data modeling is given no attention to in literature and in practice. This paper will overcome this gap in building data warehouse systems by introducing analysis patterns for dimensional data models which address well known and recurring problems in specific contexts.