MIT Sloan Management Review

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. 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.

GSEM Professor Diego Kuonen and his co-authors argue that the key to successful data science projects is to recognize the importance of clearly defining the problem and adhere to proven principles in so doing. They find that this problem is not relegated to technology teams; many business, political, management, and media projects, at all levels, also suffer from poor problem definition.

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