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Over the past two years, we’ve worked extensively with leaders in the world of professional sports, a field known for its use of analytics. An emergent theme of our work has been the persistent cultural divide between the decision makers on the field and the data analysts who crunch numbers off of it.
Our work has included a series of research workshops to discuss trans-Atlantic and cross-sector issues around performance management in professional sports. A key issue that emerged from these meetings was the recognition of this consistent disconnect within performance management practice between “big data” analysts and the decision makers they support. This is evidenced by the predominantly dismissive attitude of many executive decision makers (general managers, head coaches, CEOs, COOs, etc.) to both the data itself and those responsible for delivering it — an attitude often born largely out of ignorance or fear. The research group believed that bridging this cultural gap would provide considerable competitive advantage to any organization concerned with high performance.
What’s more, this issue transcends the world of professional sports. Whatever your industry, it’s likely that misunderstandings between quants and frontline decision makers are a challenge your business is confronting, too. As Jeanne G. Harris and Vijay Mehrotra noted in a 2014 article in MIT Sloan Management Review, the problem is one of communication. “A common complaint is that data scientists are aloof and seem uninterested in the professional lives and business problems of less-technical coworkers,” they wrote. “They don’t see a need to explain or talk about the implications of their insights, which makes it difficult for them to partner effectively with professionals whose business expertise lies outside of the technical realm.”
What is to be done? From our work with successful sports leaders, we accept that there is a significant gap between the quants and the decision makers, a gap that we call the “interpretation gap.” We believe that those who are needed to fill that gap are what we call “data translators.” While some have argued that data scientists can bridge the gap, we think that, in many cases, the data translator role can best be filled by domain experts. To date, many businesses have been trying to bridge the gap by teaching the quants (often recent graduates) about the business in which they operate.
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