Automatically Detect Valid/Interesting pivot/drill downs of a dataset
The automated generation of pivot tables is a well-studied topic. In fact, Google has a patent on the technology.

The general approach is:


Identify low-cardinality, categorical data features that have many repetitions. Those features become the row and column candidates for the pivot table.  
Identify numerical data that are the candidates for the data (or fact) cells in the pivot table.
Select an aggregation function (e.g., count, sum, average, …).
Select an evaluation method. The most common evaluation methods are human rating or look-a-like modeling with historical data.


Generating useful pivot tables typically requires domain knowledge.