How to succeed in data science projects industrialization ? | Zendy

Hervé Potelle | Zendy; Laurent Leblond | Zendy

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How to succeed in data science projects industrialization ?

Author(s) -

Hervé Potelle,

Laurent Leblond

Publication year - 2018

Publication title -

management and data science

Language(s) - English

Resource type - Journals

ISSN - 2555-7033

DOI - 10.36863/mds.a.4717

Subject(s) - mandate , data quality , process management , weighting , transformation (genetics) , computer science , business , engineering management , knowledge management , engineering , operations management , political science , medicine , metric (unit) , law , radiology , biochemistry , chemistry , gene

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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