The fingerprints of data science are seemingly everywhere in business today. Even companies not traditionally grounded in advanced mathematics are realizing the value in tracking customers’ digital trails.
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Companies looking to build their analytics capabilities need data scientists who can corral data, make it useable, and help people across the company understand and use it, according to Eric Leininger, a clinical associate professor of executive education at the Kellogg School. That task can be difficult for data scientists in larger organizations that gained prominence before the digital revolution.
“One of the reasons many data scientists like to work in startups goes to the flatness of the organization, the speed of decision making, and the degree to which people are integrated with one another,” Leininger says. “Most big companies today want to be more agile. A data scientist plugged in the right spot is going to be able to contribute more directly to the business than one who is kept at a distance from decision makers.”
So what can companies do to get the most out of their data scientists? Leininger provides five tips for attracting and retaining analytics talent—and for motivating that talent to make a more robust contribution to the business.
Learn more about leading with big data and analytics at the Kellogg Executive Education program.
Let data scientists invent. “These are people who want to push the boundaries,” Leininger says. “They get bored easily if they’re asked to do the same thing over and over again.”
These scientists are scarce talents who want to work on the company’s most important functions. If they are asked to spend their time performing repetitive tasks such as data acquisition, data management, and extensive massaging of results forecasting, they often feel underutilized. Tasking data scientists with forward-looking projects gives them the opportunity to invent the way the company can benefit from big data.
Involve leaders at the right stage of projects. Without access to the C-suite, data scientists may focus on the wrong problems. So it is crucial for data scientists to be able to engage senior management at three stages in any project: early on to help define the problem the company wants to solve; once early results start rolling in; and when it comes time for the project to be implemented across the business.
The data scientist can develop a tremendous reputation for knowledge inside the organization.
Leininger points to IBM as an example of a company that has taken this idea to heart. “IBM is a strong leader of the marketing analytics community that works hard to make sure the right people with the right skill sets are in place so that the right work gets done,” Leininger says. “The company has reconfigured jobs in the spirit of looking through the eyes of the data scientist as well as through the eyes of the company.”
Ensuring that dialogue between data scientists and the C-suite occurs early and often also increases the likelihood that data scientists’ suggestions are actually implemented.
“There’s a ‘last-mile gap,’ where the work gets done, then at the last mile it doesn’t get applied to the business,” Leininger says. “But there’s also a first-mile gap, where leaders aren’t appropriately involved up front to help the data scientists think about issues or opportunities. That gap at the very beginning increases the likelihood of a last-mile gap.”
Let data scientists out of the data box. Data scientists are natural learners who are positioned to see all aspects of the business as informed by data, rather than through a reputational or marketing lens. Because of this perspective, they can bring unique connections to broader conversations through their observation of the overall business.
That said, be sure to clarify whether a data scientist’s opinions rely on data, intuition, or a bit of both. “The data scientist can develop a tremendous reputation for knowledge inside the organization,” Leininger says. “You want to hear what they have to offer beyond their data science work, but a great data scientist who has built up a tremendous reputation for uncovering great new truth can also be seen as a bit of an oracle whose opinions can be heard as facts.”
Cross-train data scientists. Whether or not data scientists have the sexiest job of the 21st century, as Harvard Business Review declared, is debatable. What is not in dispute is that they are hard to identify, hard to recruit, and in short supply. Once a company has their data scientists on board, Leininger recommends something rather unorthodox: cross-training them to assume other roles.
“Moving people from a data science organization into operations management, digital marketing, or customer relationship management”—all analytically grounded disciplines—“can be a nice half step away from the pure data science role,” says Leininger.
Combining this with clarity around career progression for data scientists in the organization—including C-suite roles for analytics leaders who, in addition to making sure the right work is being commissioned and applied to the business, can help guide the careers of its people—can act as a motivator for talent.
Cisco, not to be stymied by the tight market for external talent, has taken this idea a step further, training insiders in data science. “Cisco identifies people already in the organization who have the skill sets and the analytical orientation and are eager to expand their career horizons,” Leininger says. “They put these people through an internal program, where they come out able to contribute to data science tasks. They’ve had some very impressive results.”
Help data scientists adopt a broader business mindset. “Reports tell us what is happening,” Leininger says. “Great analysis tells us not only what is happening, but why it’s happening, what it’s likely to do in the future, and what we should do in any given situation. That ability to fit data analytics into a narrative context for the total company—and to see the financial implications of that work—is an essential management skill, and it’s a skill that can be learned by data scientists.”
When business leaders confuse data reporting for analysis, a company can have trouble addressing problems effectively. By the same token, data scientists need to learn how to address the C-suite on the C-suite’s terms. “Data scientists may have a tendency to want to explain everything they’ve done, how hard they’ve worked, and what a great task and accomplishment this was,” Leininger says. “The C-suite has three rules: Be clear. Be quick. And be gone.”
Developing the business acumen of data scientists helps them contribute more holistically to conversations within the company, allowing them to initiate analyses and experiments, rather than simply reacting to requests. That is a long-term benefit that costs companies little to implement.
Fred Schmalz is the business editor of Kellogg Insight.
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