Data to AI is what oil has been to the world economy for the last century. Unless you have plenty of it, you will not be able to get far! It’s not enough to get a hold of sufficient data to train a model, you also need to have other data sets of the same type to validate your model on. Then, you need to have the right data flowing into your model once it is launched in the real-world. What does that mean? It means that if your model requires data that is not readily available in the setting that the model is used for, it will not be able to perform as intended. If it does not have access to the input data it needs to come up with predictions or do its pattern recognition, it will not be of much use. This is especially salient in the world of healthcare, where data is siloed and often not inter-operable.