Why You're Not Getting Value from Your Data Science

Businesses today are constantly generating enormous amounts of data, but that doesn't always translate to actionable information. Over the past several years, my research group at MIT and I have sought answers to a fundamental question: What would it take for businesses to realize the full potential of their data repositories with machine learning? As we worked to design machine learning–based solutions with a variety of industry partners, we were surprised to find that the existing answers to this question often didn't apply. First, whenever we spoke with machine learning experts (data scientists focused on training and testing predictive models) about the most difficult part of their job, they said again and again, "the data is a mess." Initially, taking that statement literally, we imagined it referred to well-known issues with data -- missing values or a lack of coherence across databases.