I'm no expert, but it occurs to me that a mountain of data coming in from disparate sources needs the concept of discoverability to be adequately used. That is, there has to be a way for a machine to find occurrences of data that it cares about without specifically knowing the exact attributes of the data.
A simple example off the top of my head. Let's say there's a popular app that collects your pulse from your cellphone somehow, and that data is stored in your "Personal API" in some format. Some scientists write a routine that scans people's pulse patterns (with their permission, say) and warns them of possible risk factors. Later on hospitals start feeding their pulse data into the same personal API, but in a different format and using different units (bpm instead of bph, say), because they've never heard of the cellphone app.
I take "discoverability" as a general concept addressing how the scientists' routine might discover that the hospital data exists in a person's "Personal API", discover that the thing being measured by the hospital is the same as the thing being measured by the app, and discover how to map both formats into a common workable format and units.