The Data Science Management Process

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It is increasingly clear that companies and government agencies do not know how to manage data science at the enterprise level. Many are still stuck doing pilots. Some take on projects that are beyond their capabilities. And too often, excellent work dies on the vine during implementation. Companies must take action to address the structural and process issues that hold them back. In an earlier article, we pointed out the major structural flaw hindering many data science programs — the inherent conflict between data science groups (which we termed the lab) and business operations (termed the factory). To resolve that conflict, we proposed a data science bridge: an intermediary group headed by a person with the title innovation marshal tasked with ensuring better communication and integration between the two groups and surfacing the best ways to make inventions by the lab fit into the needs of the factory.

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