Enterprises that want to launch big data initiatives — or even more ambitiously, seek to create an "analytics culture" — invariably should answer a handful of critical questions before spending money and allocating resources: What's the business case for analytics? Which big data tools should we use? Should we hire a data analytics vendor to handle everything? If we build an in-house team, where do we get the analytics talent?
The last question is rooted in the reported (and sometimes disputed) shortage of data scientists needed to meet growing demand as enterprise and consumer data continue to increase exponentially. But if an enterprise is fully committed to data analytics, it will find or develop the talent.
Beyond talent acquisition, the fundamental challenge facing enterprises trying to build an effective data analytics team is determining the optimum combination of skills, background, and personality.
Two senior data scientists who lead their own respective data science operations talked with CITEworld about what enterprises should consider when assembling a team.
"The first step is to define a clear business goal, or at least one the company is working toward," says Kevin Lyons, senior vice president of analytics for eXelate, a digital marketing data management platform vendor. "If you can't identify it, there's no way you'll be able to achieve it."
Data scientists for companies such as Google and Facebook, for example, must produce analytics for computers that "learn" about consumers and can predict behavior. These types of data scientists typically have strong mathematical and computational skills.
Conversely, data scientists charged with producing analytics for humans to make product or operational decisions usually need stronger "soft" skills.
"You need at least one person who can communicate," says Claudia Perlich, chief scientist at Dstillery, a marketing company that analyzes web browsing data to help brands target ads. "Somebody who can sit down with the CTO or CMO or CEO and have a good enough understanding of the business problems to help frame what role and what specific task data science should work on."
As essential as soft skills are to data scientists who must interact with colleagues in business units and executive suites, Perlich emphasizes that they need some fundamental technical chops.
"They don't need super coding skills, but they need to be able to access data," she says. "They need at least a scripting language, say Perl or Python, in order to manipulate data once it's out of wherever they found it. And they need a practical understanding of statistics. They don't need probability theory, but they need to understand empirical distributions of data and how the mean can be super misleading when you have a long-tail distribution."
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