Framing Data Science Problems the Right Way From the Start

The failure rate of data science initiatives — often estimated at over 80% — is way too high. We have spent years researching the reasons contributing to companies’ low success rates and have identified one underappreciated issue: Too often, teams skip right to analyzing the data before agreeing on the problem to be solved. This lack of initial understanding guarantees that many projects are doomed to fail from the very beginning. Of course, this issue is not a new one. Albert Einstein is often quoted as having said, “If I were given one hour to save the planet, I would spend 59 minutes defining the problem and one minute solving it.”

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