A data model is a discrete structured data representation of a real-world set of entities related to one another. Over time, our understanding of the best way to represent our perceived model has changed to reflect the ways we understand information along with the ways that we want to process that information. There is a significant difference between how we use data in an operational/tactical manner (i.e., to run the business) and the ways we use data in a strategic manner (i.e., to improve the business).

The traditional modeling technique for operational systems revolves around the entity-relationship model. Unfortunately, analytical applications that are relevant to BI are less able to take advantage of data when it is structured in the entity-relational form; alternatively, casting the same information into a dimensional structure greatly simplifies the use of data for strategic purposes.

In the early 1980s, a number of practitioners and researchers (most notably Chris Date) explored the concept of a relational database, in which the way that information was modeled was viewed in the context of representing entities within separate tables and relating those entities within a business process context between tables using some form of cross-table linkage. This is referred to as entity-relationship modeling.

The apparent failure of the relational model to efficiently provide data to knowledge workers is due mostly to the complexity of the data models and the difficulty in reconstructing a natural view of information that can be used in an analytical context. In...