In the digital age, all roads lead to data. Yet, transforming the growing mountain of bits and bytes into information, knowledge and real-world insights is no simple task. In many cases, it requires a data scientist to unlock the value.
As Scott Schlesinger, principal for the IT Advisory Practice at consulting firm E&Y puts it: “Data scientists are an invaluable asset when it comes to advanced data analytics.” Yet, data scientists are expensive, and many organizations struggle to keep them busy.
“Data scientists are being hired and leveraged differently today than when they broke onto the scene a few years ago,” Schlesinger explains. “Fewer and fewer large organizations are hiring dedicated data scientists, and more are turning to contract labor from consulting firms that employ them in larger numbers and leverage them for multiple clients and across multiple engagements.”
How can a CIO or other enterprise leader determine when, where and how to put data scientists to work? How do executives approach the space? And how is the field evolving? While there are no simple answers and no one-size-fits-all approach, experts say there are ways to tap into the talents of data scientists and citizen data scientists while also keeping a budget in check.
The growing breed of citizen data scientists often don’t have a technology background and usually work in a line-of-business department. However, many work collaboratively with data scientists in the IT organization to create projects that have business value.
Promoting Data Scientists as Value Creators
CIOs should focus on several essential issues, notes Dorman Bazzell, vice president and Americas Practice Lead for Insights & Analytics at Hitachi Consulting. Ideally, these tech executives should provide data scientists with access to a broad range of data; create flexible repositories of pre-managed data; make sure data scientists have access to data with high integrity; ensure that data scientists understand the goals of an initiative; and fully leverage the value of data science by attempting to build an analytics-driven culture within an organization.
The upshot? “Organizations would do well to begin promoting data science and its practitioners as value creators [that] foster a data-driven culture,” Bazzell says. This, ultimately leads to “higher-value analytics that impact organizational effectiveness, including revenue optimization, customer satisfaction, product development and risk identification.”
Unfortunately, the task is easier said than done. “The output of a data science effort often goes unnoticed except by the business unit or function that paid for it,” he adds.