How to get the most value from your data scientists

They're not miracle workers but they play a vital role in getting big value from big data

By Jeanne G. Harris and Vijay Mehrotra

Simply hiring data scientists is not enough. To create real business value, data scientists must be managed effectively. The first step to getting value from data scientists is recognizing that they are not miracle workers. They are, however, highly skilled professionals who, working collaboratively with others in the organization, will play a vital part in helping you realize big value from big data.

To avoid repeating mistakes that have undermined the success of previous generations of analytical talent, consider these seven recommendations from the MIT Sloan Management Review report, Getting Value from Your Data Scientists.

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1. Appoint and empower a data and analytics leader. In the past, efforts to get business value from data were often isolated and lacked executive support. A mandate from the company’s senior leadership is essential to elevate analytics into a transformational possibility.
2. Point data scientists to your biggest problems and then get out of the way. Data scientists are most effective when they are supporting a strategy they believe in. But you can’t just hand them a mountain of data and tell them to make something of it. You need to frame the outcomes that would be valuable for the business. Data scientists want to make a big impact, but our research shows they also require a high degree of autonomy. Run interference for them as needed, and let them go after it.
3. Cultivate support for your data scientists among decision makers. To build a trusted working relationship with decision makers, data scientists must learn to communicate with business people about what matters to them.
4. Connect data scientists within the organization but locate them near decision makers. Data scientists engage in highly complex and knowledge-intensive work. For many reasons – including knowledge sharing, intellectual stimulation and resource sharing – it makes sense for data scientists to be grouped together organizationally but physically to be close to decision makers.
5. Encourage data scientists to get their hands dirty. There is no substitute for direct exposure to business processes and customers. Don’t keep your data scientists penned up in the office; get them out into the world where they can see problems firsthand.
6. Build analyst teams with diverse skills. Combining business analysts, data scientists, visualization experts and modelers from different disciplines and functional areas enables teams to tackle problems that would be too difficult for individuals to solve alone.
7. Reward data scientists in ways they care about. Data scientists aren’t indifferent to money – they expect to receive competitive wages. But often they are most motivated by intellectual challenge and recognition. Data scientists share their enthusiasm and ideas with colleagues at conferences such as the O’Reilly Strata Conference and through Kaggle.com data science competitions.

The MIT Sloan Management Review report, Getting Value From Your Data Scientists, is based on a survey of more than 300 analytics professionals (of which about one-third were self-described data scientists) working in different types of US companies. In addition to tips for managing data scientists for the best business value, the report also examines how data scientists differ from other types of analysts, and how both analysts and data scientists view their work and place in their organizations.

Jeanne G. Harris is managing director of information technology research at the Accenture Institute for High Performance and a coauthor, with Thomas Davenport and Robert Morison, of Analytics at Work (Harvard Business Press, 2010).

Vijay Mehrotra is a professor of business analytics and information systems at the University of San Francisco in San Francisco, California.