In the physical sciences there are plenty of domains where accurate measurements are sparse. In a case close to home for me, it's measurements of water depth off coasts (accurate to centimeters onna grid of size meters). The place where you have these measurements in the real world can be counted on one hand. But now you want to train a ML algorithm to be able to guess water depth in environments all over the world, so in this case you need your data to be representative of a bunch of possible cases that are outside real data. This differs slightly from the GP who I think is talking about creating data that isn't even represented in the real world at all, but that would help an algorithm predict real world data anyway. But they are fairly related topics.