Some BI applications include online analytical processing (OLAP), also referred to as multidimensional analysis capabilities.

OLAP involves “slicing and dicing” data stored in a dimensional format, drilling down in the data to greater detail, and aggregating the data.

Consider an example from below figure, showing the data cube. The product is on the x-axis, geography is on the y-axis, and time is on the z-axis.

Now, suppose you want to know how many nuts the company sold in the West region in 2009. You would slice and dice the cube, using nuts as the specific measure for product, West as the measure for geography, and 2009 as the measure for time.

The value or values that remain in the cell(s) after our slicing and dicing is (are) the answer to our question.

As an example of drilling down, you also might want to know how many nuts were sold in January 2009. Alternatively, you might want to know how many nuts were sold during 2008–2010, which is an example of aggregation, also called “rollup.”