In situations where every item in a data set must be compared with every other item in the set, it may be desirable to store the data across a number of machines in such a way that any two data items are stored together on at least one machine. One way to evaluate the efficiency of such a distribution is by the largest fraction of the data it requires to be allocated to any one machine. The all-to-all comparison (ATAC) data limit for m machines is a measure of the minimum of this value across all possible such distributions using m machines. I'll discuss some recent work on this data limit by Joanne Hall, Doug Stinson and myself.