Data scientists are in demand. Why?
Digital enterprises seeking to outperform their competition (by leveraging their Big Data assets in order to uncover new business opportunities) are clamoring for this new class of employee.
However, many organizations are grappling with bridging a gap that exists between management and data scientists, primarily because the latter are more than adept at uncovering new business insights, but typically struggle to explain the impact of their work to leadership.
That’s according to a recent MIT Sloan School of Management article, where in companies are advised to take specific steps to gain the most value from data scientists.
“The organizations that create the most value from data science are those that seek game-changing insights into the open-ended questions that matter most to the business,” the article notes. “For a retailer, it might be, how can we identify previously undiscovered products for cross-selling opportunities? For a mobile phone company, it might be, where can we find new revenue streams to offset the decline in revenues from calls and texts?”
To drive greater business value from the deep and broad expertise/skills of data scientists, managers should consider these recommendations, the article suggests:
1. Add a data and analytics leader.
“This executive serves as the champion and passionate advocate for the adoption of Big Data analytics in the organization,” according to the article. “The chief data and analytics officer will also give data scientists direction and keep them focused on important business objectives while clearing bureaucratic obstacles and establishing partnerships with business customers.”
2. Map the outcomes for data scientists that would most benefit the business.
MIT Sloan surveyed more than 300 analytics professionals and found that almost all the data scientists surveyed—94 percent—said analytical abilities are a key element of their companies’ business models and strategies. Furthermore, 96 percent of data scientists said their analyses are used to make key decisions, and 89 percent reported that they find surprising and valuable business insights outside of formal projects.
3. Invest in training and other support that data scientists need to effectively communicate business outcomes to leadership.
“A. Charles Thomas, chief data officer for Wells Fargo & Co., put it this way: ‘Data scientists are brilliant, but they sometimes struggle to cast their findings in terms relevant to the business: revenue, profit, cost savings, delighted customers or customers retained. Given that data scientists seldom own the outcome but merely influence it, it is critical that these analysts learn how to speak the language of business-people—and vice versa.’”
4. Locate data scientists near the teams they support. This makes it easier for them to get faster, more targeted feedback on their work.
“Many large organizations create a core hub that is responsible for ensuring that data scientists have ample opportunities to learn, share and grow,” the article notes. “At Monster Worldwide, the parent company of the Monster.com global employment website, this approach gives data scientists a consistent context and way of working.”
5. Provide data scientists with direct exposure to business processes and customers.
New York City’s analytics department, for example, had data indicating that rodent problems, the number of ambulance visits, and delinquency in paying property taxes were all associated with higher incidences of fire in buildings.
“But it wasn’t until a data scientist was out with building inspectors that he became aware of how much some landlords did to maintain and develop their properties,” the article points out. “This, in turn, led to an interesting discovery: owners who had applied for permits to do exterior brickwork on their properties were significantly less likely to have fires.”
6. Build teams of business analysts, data scientists, visualization experts and modelers from different disciplines and functional areas.
“Leaders should focus on building teams of people with different skills who can work together to solve difficult problems and exploit opportunities.”
7. Identify the rewards that motivate data scientists.
Data scientists are often most motivated by intellectual challenge and peer recognition. “To achieve significant value, executives will have to deliberately and thoughtfully create conditions for data scientists’ success. Executives can begin by recognizing that data scientists aren’t miracle workers. They are, however, highly skilled professionals who, working collaboratively with others in the organization, will play a vital part in realizing big value from Big Data.”
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