Why Data Science Teams Need Generalists, Not Specialists

Most businesses organize for efficient productivity. They do this through specialization. Workers that are highly skilled at narrow tasks can perfect their skill and, when put in an assembly line, produce goods with high efficiency. Data science teams have been built this way, too, with highly specialized tasks assigned to individuals that work together to create new business capabilities. But this is the wrong way to organize a data science team because data scientists often don’t know what they’re producing until they hit upon it. Their goal should be to learn, not be efficient. To maximize learning, teams should reorganize as groups of generalists who can perform many data science tasks. This will reduce many of the classic bottlenecks in data science that lead to poor outcomes. The team of generalists will learn more and contribute more to the business.