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The search for vital analytics talent has often focused on data scientists. In this article for Harvard Business Review, we describe the overlooked analytics role that’s even more critical to fill.

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—largely 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 also 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).

What exactly is an analytics translator?

Source: First published in HBR, February 2018