By user741803 on Mar 28, 2011
One of the primary business benefits of the Common Enterprise Information Model (CEIM) is enabling goal-based visibility and performance management across the enterprise. Business managers need to compare actual performance side-by-side with targets, such as actual sales compared to the sales forecast. The ratio of these two metrics– actual sales as a percent of forecast– can be compared to a threshold to create automatic stoplights and alerts. To measure and improve effectiveness, managers also need to compare measures across processes, such as a ratio of product shipments to inventory levels (inventory turns). This post lays out the basic concept for tying data from these far-flung sources together so they can be interactively analyzed as a whole, automatically manufacturing queries and handling the exception cases for the business users.
In Part 1 of this series, I briefly introduced that basic concept: conformed logical dimensions in the business model layer. In Part 2, I reviewed general dimensional concepts as applied to a single measure. In this post, I will describe the dimensionality of the business model as a whole, and how the conformed dimensions enable business performance visibility.
The concept of grain is a key to understanding conformed dimensions. This post shows how individual measures have differing grain within the overall conformed dimensions of the business model, and how grain is presented in the end user’s analytical experience. Later, this concept will be essential to understanding all the posts covering the logical-physical mapping patterns.[Read More]