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Designing Great Data Products by Jeremy Howard, Mike Loukides, Margit Zwemer

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Chapter 1. Designing Great Data Products

By Jeremy Howard, Margit Zwemer, and Mike Loukides

In the past few years, we’ve seen many data products based on predictive modeling. These products range from weather forecasting to recommendation engines to services that predict airline flight times more accurately than the airline itself. But these products are still just making predictions, rather than asking what action they want someone to take as a result of a prediction. Prediction technology can be interesting and mathematically elegant, but we need to take the next step. The technology exists to build data products that can revolutionize entire industries. So, why aren’t we building them?

To jump-start this process, we suggest a four-step approach that has already transformed the insurance industry. We call it the Drivetrain Approach, inspired by the emerging field of self-driving vehicles. Engineers start by defining a clear objective: They want a car to drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing. Someone using Google’s self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach. We don’t claim that the Drivetrain Approach ...

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