I want to say that anybody that does analytics work should be able to translate the results and methods used to a business leader. The best translator is someone who knows the business well, and ideally is known by management involved to understand the business. But overall its a polished engineering view you want, not a scientific one. You should have scientists available for the hard stuff, but its rare that you have to newly invent something.
You Don’t Have to Be a Data Scientist to Fill This Must-Have Analytics Role By Nicolaus Henke, Jordan Levine, Paul McInerney in the HBR.
It’s no secret that organizations have been increasingly turning to advanced analytics and artificial intelligence (AI) to improve decision making across business processes—from research and design to supply chain and risk management.
Along the way, there’s been plenty of literature and executive hand-wringing over hiring and deploying ever-scarce data scientists to make this happen. Certainly, data scientists are required to build the analytics models—including machine learning and, increasingly, deep learning—capable of turning vast amounts of data into insights.
More recently, however, companies have widened their aperture, recognizing that success with AI and analytics requires not just data scientists but entire cross-functional, agile teams that include data engineers, data architects, data-visualization experts, and—perhaps most important—translators.
Why are translators so important? They help ensure that organizations achieve real impact from their analytics initiatives (which has the added benefit of keeping data scientists fulfilled and more likely to stay on, easing executives’ stress over sourcing that talent). ... "
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