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Marketing Analytics: How To Start Without Data Scientists

You don't need a team of highly paid math whizzes to get started with data analytics, says one marketing analytics expert.

 Big Data Talent War: 7 Ways To Win
Big Data Talent War: 7 Ways To Win
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The looming data scientist shortage, whether real or perceived, has become a hot topic recently. Some industry analysts predict companies will have a hard time finding qualified people to pull insights from their growing stockpiles of data. The McKinsey Global Institute, for instance, says the U.S. could face a shortage of up to 190,000 data scientists by 2018.

There's good news, however, particularly for businesses interested in marketing analytics. In a phone interview with InformationWeek, Anil Kaul, CEO of AbsolutData, an analytics and research firm based in Alameda, Calif., said the data scientist shortage is real, but added that companies shouldn't panic if they don't already have a squad of statistical whizzes on staff.

In addition, there's no reason to invest heavily in a sophisticated data science platform, at least not when you're getting the operation off the ground. "I've seen this happen at many organizations, where people say, 'I need Ph.D.'s to do this. Where am I going to find them?' Because there aren't too many of those people around," Kaul says.

Often, indecision leads companies to simply do nothing. Wrong move.

[ For more on what makes an effective data scientist, see Wanted: Qualified Data Scientists, People Skills A Plus. ]

"When you start in analytics, in your first six months you should not worry about investing tremendous amounts of money on technology, or worry about hiring a big team," says Kaul, who has a Ph.D. in marketing analytics from Cornell University and more than 17 years of experience in the analytics field.

AbsolutData provides analytics support for major Fortune 500 companies, Kaul says, either on a project basis or increasingly on an ongoing "team-support basis," where the firm develops analytics teams for businesses. "The real impact of analytics comes when you're doing simple, basic analytics. You don't need huge tools. You don't need a Ph.D. to do that," Kaul says.

More sophisticated analysis, however, requires people with serious skills in a variety of technical disciplines, such as computer science, analytics, math, modeling, and statistics. Furthermore, a data scientist must be a good communicator who is capable of understanding a business problem, transforming that problem into an analytics plan, executing the plan, and then devising a business solution. "It's a fairly skilled roll that crosses two different areas," says Kaul. "It takes a particular type of person who can do that very well."

This rare combination of skills is a major reason why there's a shortage of data scientists. But things may be improving. More business schools are starting to teach analytics courses, says Kaul, which may mean more university graduates will soon have a basic set of data science skills. "Traditionally, most people have shunned analytics as a career because it meant being good at math," he says.

The need for marketing analytics will grow significantly in the future, as the media landscape becomes increasingly fragmented. Thirty years ago, for instance, marketers had far fewer national outlets at their disposal, including three major television networks, fledging cable TV channels, radio, and a select number of magazines and newspapers. Today, of course, a marketer's options may seem endless; in addition to traditional media, there are search engines, social networks, and sundry other Internet options.

"The decision itself has become so complex that you need analytical support," Kaul says. "People who have analytical skills have suddenly become in demand, and that's part of what is driving the shortage."

And that development means that analytics as a profession will have a very bright future, Kaul believes. "This is something that will play a critical role in creating the next wave of companies," he says. "Companies will win because of the way they use data and analytics to get insights."

Predictive analysis is getting faster, more accurate and more accessible. Combined with big data, it's driving a new age of experiments. Also in the new, all-digital Advanced Analytics issue of InformationWeek: Are project management offices a waste of money? (Free registration required.)

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