Anja Keß

In a dimensional model, the context of each measurement is defined by dimensions. A rich set of dimensions enables diverse and powerful analytic possibilities. All too often, well-meaning designers cripple this potential in a misguided effort to save space. In operational systems, it is common practice to divide data elements into constituent parts.

A dimensional model must have its own unique identifier for each dimension. This allows flexibility in responding to changes in source data. When a customer record is updated, for example, the dimensional model may need to commodate current and historic versions. The source system’s unique identifier will not be enough to distinguish between them.

Dimensional structures are populated with information that flows from operational systems. The data in operational systems undergoes changes. One customer’s date of birth is updated, for example, while another’s address is changed. Several standard responses are possible; the correct solution is dictated by analytic requirements.

In a dimensional model, measurements of a business process are called facts; the level of detail at which they are recorded is referred to as grain. Facts are stored at a detailed grain, but can be aggregated when queried, allowing the dimensional model to support a wide variety of business questions.

The requirements for dimensional designs are often identified through interviews with business users, many of whom are high-level executives. Conducting interviews should not be seen as an opportunity to impress. Instead, it is a time to humbly set aside what you know and start from scratch.

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