There is a big step from machine learning to Deep Learning (DL) in that DL requires much more data than ML.

Those who have worked on Machine Learning (ML) projects know that ML requires a large amount of data to train the resulting algorithms. Some would say you can never have too much data. There is usually a correlation between the amount of data and the sophistication of the resulting ML model. This data hunger is only going to get more intense as AI progresses towards new benefit pools while leveraging more sophisticated AI capabilities. Since there are other contributing trends bedsides the sophistication of AI, the question looms for organizations is, "do they have the right data to fuel successful AI efforts?"