Pivoting data is a way of reporting on two major dimensions at once, by turning a list of results into a table of results.
For example, if you're looking at two dimensions of sales from the past four months, the first dimension being Top Level Category, and the second dimension being Completed Month.

When you add two dimensions, it creates a list of data.

If you click the Completed Month heading, click Pivot, and run it again, the results display with identical data, but in a more organized format.

On the Data tab, you can also select checkboxes for Totals (Column totals) and Row Totals to add space to your table for totals.

When you run it again, you'll get summary results to measure categories, months, and the overall total. You can use this to view sales or inventory activity in Multi-Store environments.
 

Like these examples, the most common pivot types are reading results over time, day-over-day, month-over-month, year-over-year, and so on.

Like dimensions, you can add two layers of dimensions to pivots.

Like non-pivoted dimensions, calculations can respond to pivoted dimensions. For example, if you want to highlight results from just one store, you can prepare it the same way as any other calculation.

For example, if you have a calculation that looks like:

if(${shops.name}="name82373","We Love Disco",null)

and you save it correctly, you can prepare compound IF statements looking at either pivoted or non-pivoted dimensions.

So in this instance, the calculation looks like:

if(${shops.name}="name82373","We Love Disco",if(${cl_category_tops.top_level_category}="CLOTHING","Time for Cookies",null))

and if you save it correctly, just like regular dimensions and measures, Pivoted measures have a special set of functions too.