Why managing data scientists is different — Roger Stein

MIT Sloan Senior Lecturer Roger Stein

MIT Sloan Senior Lecturer Roger Stein

From the MIT Sloan Management Review

While businesses are hiring more data scientists than ever, many struggle to realize the full organizational and financial benefits from investing in data analytics. This is forcing some managers to think carefully about how units with analytics talents are structured and managed.

How can organizations realize the promise of the evolving disciplines that we broadly call analytics?

Although financial firms were among the first to recruit “quants” to use sophisticated mathematical models and high-powered computing hardware, analytics groups have now taken hold in areas ranging from health care to political campaigns to retailing to sports. Organizations like these can benefit from the insights gained by financial service firms on how best to manage teams doing advanced analytics. It requires skills and philosophies that are different from those that arise in managing other groups of smart professionals.

Rather than just involving oversight and planning, managing a data science research effort tends to be a dynamic and self-correcting process; it is difficult to plan precisely either a project’s timing or final outcomes. For those unused to this type of work, this process can seem quite messy — an unexpected contrast to a field that, from the outside, seems to epitomize the rule of reason and the preeminence of data.

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