What is the term for data that is too sparse to represent the underlying data model?
Small sample size is probably the concept you're looking for. A common failure in statistics is trying to draw conclusions from data that isn't "big" enough to accurately represent the underlying distribution. 

It's worth noting that "small data" increases your chances of overfitting - which is another way of saying that your model is weak to outliers and noise. It's also worth noting that bootstrapping, simulation, and duplication might help (but not always).

There may be some other niche term for this, but if you relate it to basic stats there's a high probability everyone in the room will understand what you're talking about.