UBER's Head of Machine Learning Thinks You Might be Doing it Wrong

Lange argues that most companies don't need to collect months of data after implementing a machine learning system before they derive value. Instead, look at the historical information that you already have and feed it to a supervised machine learning system (an algorithm that takes a known set of inputs and a matching known set of outputs and trains a model to generate predictions for responses to new data). Companies often have reams of saved customer service data that can yield lots of valuable insights, like how lead sources correlate to refunds, or how service packages are related to the amount of customer support a particular customer requires. The key is to choose existing data that is related to your main problem or question so that you drive ROI with purpose.