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Businesses spend millions of dollars employing advanced analytics and machine learning to support decision-making across a wide range of business processes. Every project promises substantial value to the business, but also carries with it a cost and some risk of failure. Many predictive analytic projects have addressed well-articulated problems, using complex data resources and elegant algorithms to generate compelling results at pilot scale — but never achieved user adoption in production. How can one predict which advanced analytics projects will succeed, and is it feasible to salvage the ones that never get off the ground?