Data science is a team sport

In 2012, Tom Davenport, well-known thinker on management and technology, and D.J. Patil, the computer scientist who would later serve as chief data scientist in the Obama Administration’s Office of Science and Technology Policy, penned a provocative Harvard Business Review essay in which they argued, as the title of the article stated, that data scientist was the “sexiest job of the 21st Century.”

But Joe DosSantos, chief data and analytics officer at data analytics software company Qlik, hates their conclusion. “The essay created unreasonable expectations of what a data scientist can do,” DosSantos says, adding these over-hyped expectations worsened the war for talent by encouraging companies to look for “unicorn” candidates who possess all of the skills Davenport and Patel described.

Instead of hunting for data scientists who are the masters-of-everything that Davenport and Patil value, DosSantos says, companies should be thinking about how to build teams in which data literate analysts work alongside subject-matter experts from various business units. “Data science is a team sport,” he says. “And data scientists are arguably not even the most important people on the team.”

Developing effective algorithms for companies requires many steps. The most important of which, DosSantos says, are “aligning the organization around an important problem to solve and then figuring out how an algorithm might help solve that problem.” At both of those things, he argues, most data scientists are “useless.”

Other important steps include thinking about what data is available and thinking through the ethics of marrying that data with a particular business case. Then a company has to obtain the data, clean it, build the algorithm, and train it on the data. And then it must test the algorithm, and figure out how to deploy it. Finally, it must monitor the algorithm’s performance to ensure it is continuing to work as designed. Only a small number of those tasks absolutely requires a data scientist, DosSantos says: building the algorithm and training it. Subject matter experts or people with different kinds of engineering and analytic backgrounds can handle most of the others…


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