Preparing data for analysis at any scale is a notoriously time consuming and error prone process. It is estimated that up to 90% of the time spent on data analysis projects is spent on data preparation. The problem is that data originates from an ever growing number of sources, comes in a wide variety of complex formats, and can span the range from structured, semi-structured, and more often unstructured content. All this content is vast, inconsistent, incomplete, and often off topic. In this environment each dataset takes weeks or months of effort to process, frequently requiring programmers writing custom scripts. Accelerating and automating data preparation is the key to unlocking the potential of all your data.