Visually, the cube consists of multiple dimensions along the different sides that can be sliced or diced along each of its dimensions. The goal of this model is to allow users to easily complete complex analysis by viewing data that has been aggregated based upon user requirements. Users still have the ability to drill into the detail. For example, you want to review your average length of stay. Since operational data is at a detail level, the information is often summarized and computed in the loading and transformation phase.
Performance is enhanced by precomputing values or aggregations.
Every time you want to review your average length of stay you will not be required to calculate this data. In this example, all of the ALOS will be calculated and stored in the data cubes, but the patient level detail will still be accessible. The dimensions of your cube will include date of admission and discharge, insurance plan, hospital and diagnosis group. The measures of the cube would include target ALOS, inpatient days, and average length of stay. Data within each dimension can be organized into levels of a hierarchy.