Industrializing Data Science projects in business lines results from a transformation that takes place from strategic scoping to operational management. By using a learning base, it is possible to predict with performance the success or the failure of future projects in the pre-scoping phase. Industrializable projects can thus be correctly predicted by weighting the following six criteria: the business question, the business mandate, the business availability, the "Data" competences, the quality / Data quantity and the Data monitoring. On this core, the design of a Predictive Score Card evaluation tool allows an optimized projects pre-scoping.
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