Slicing means taking out the slice of a cube, given certain set of select dimension (customer segment), and value (home furnishings..) and measures (sales revenue, sales units..) or KPIs (Sales Productivity). Dicing means viewing the slices from different angles. For example -Revenue for different products within a given state OR revenue for different states for a given product.
Slicing and Dicing leads to what you can call Pivot. Pivot is known in Excel context. Pivot is the standard and basic look and feel of the views you create on the OLAP cubes. A pivot creates an ability for you to create the width and depth in your view of the data. A pivot is a two dimensional lay-out of the summary data. The x and y axis are the dimensions and the intersection cells for any two dimension values contain the value of the measures.
Here is an example of how you can slice and dice through pivot:
Step1: Starting layout- You can have product list on y axis (say 10 products), the quarters (say four quarters) on the X-axis. You can have sales value as the measure shown in the table against intersection of a given product and a quarter. You will have 10 X 4 matrix.
Step 2: Adding depth Cross-Dimensionally-Taking a step further, you can add a dimension of locations under the product to give it more depth. Therefore now you can have different locations (say 3 locations) for each row of product. You will not have a 30 (3 locations for each of the 10 products) X 4 (quarters) matrix.
Step 3: Adding depth within a single dimension: You can also add another dimension like months under quarters. Now you will have 30 X 12 (3 months for each quarter). You can also specify, if you want to have sub-totals for every dimension. For example, you can have the sub-totals for locations, productions, month and quarters.
Step 4: Pivoting on an axis: You can also pivot your view and transpose the product+ location combination on X axis and quarter + month combination on Y axis.
Step 5: Adding Width: Referring to starting layout-You can also add dimensions in 'width' instead of 'depth'. For example- instead of having location dimension under the product, you can add location dimension adjacent to the product dimension. Therefore, you will have a matrix, which on Y axis will have 10 rows (for 10 products) and 3 rows (for 3 locations), with a 13X4 matrix.
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