1.) Most data scientists are not good enough at math to take an existing model and use it for a different purpose. Usually there needs to a model whos mathematical properties match almost exactly to their needs (this is usually the case).
Understanding of the statistical methods and machine learning models is imperative to have but why for example IBM’s Watson for Oncology Project Cancelled After Spending $62 Million. Wasn't they had proper skill set and data?
2.) The data is bad