There is this wonderful algorithm for dimensionality reduction for presentation purposes called t-distributed stochastic neighbor embedding, t-SNE for short, introduced by van der Maaten and others (2008):
Now, it has even been said that if people could see in high dimensions machine learning would not be necessary. Therefore, if we were to apply t-SNE to datasets of interest to people, for example friends, locations, jobs, with dots color coded with class labels of importance to them (e.g., did you like that job?) from a 2D projection of the data, the users could figure out a boundary and make their own decisions *without the need of machine learning*.
The idea here is to make a simple tool that allow people to get t-SNE projections. For the most part that hinges is putting together distance metrics between their instances of interest that will work for t-SNE.