Owl intentionally solves the problem using a machine learning first, rules second based approach. Owl automatically puts all columns under quality control. This includes nullchecks, emptychecks, statistical profiles, sketches. Owl creates snapshots and baselines in order to benchmark past data and discover drift. Owl automatically creates a ML labeling system for users to collaborate and down-train items with a click of a button. The reason for this approach is obviously to maximize coverage while reducing the dependency of manual rule building. The greater technical benefit is that all of Owl's generated checks and rules are adaptive. Owl is constantly learning from new data and will make predictions in many cases for: typos, formatting issues, outliers and relationships. This is a paradigm shift from, risk being a measure of how many rules one can dream up and write, to simply click the Owl [RUN] button.