Hiring Top Analytics Talent: How Leading Companies Do It

People with analytics modeling skills, business domain knowledge, and technical skills are in high demand among digital data-driven businesses. But these professionals are a rare breed. Here's how leading companies are attracting top talent.

Jessica Davis, Senior Editor

December 11, 2015

4 Min Read
<p align="left">(Image: James Brey/iStockphoto)</p>

Data Science Skills To Boost Your Salary

Data Science Skills To Boost Your Salary

Data Science Skills To Boost Your Salary (Click image for larger view and slideshow.)

The race to recruit top analytics talent is getting more intense as companies compete against each other to get the best of three worlds -- analytics modeling skills, technology experience, and business domain knowledge.

That combination of skills is hard to find in a single candidate.

"It's called a data science unicorn, because there aren't people out there that embody all these skills," Intel Chief Data Scientist Bob Rogers told InformationWeek an interview last month. Yet that's what organizations want -- so how do you find this rare professional?

A new survey from global management consulting firm A.T. Kearney addresses that question, looking at the most common approaches of industry-leading companies in analytics and digital business. How do these companies approach recruiting this top talent, or "trilinguals" as the report's authors call them?

Companies that A.T. Kearney identified in its 2015 Leadership Excellence in Analytic Practices (LEAP) study as "leaders" in employing analytics for digital business were more likely than their "laggard" counterparts to create their own data scientists by filling those employees' skills gaps after making the hire, instead of spending time and money pursuing the elusive unicorn. The study is based on a survey of 430 senior-level executives at companies in 10 countries and 30 industries.

The survey defined leader companies (8% of those surveyed) as those in which "analytics generates foresight on future business trends, playing a role in decision making and driving innovation." Laggards (24% of those surveyed) are defined as those in which "analytics is limited to reporting on past performance." Two other levels of analytics organizations identified are "Explorers" (27% of companies surveyed), which use analytics to "predict new trends to optimize business performance," and "Followers" (41% of respondents), which use "analytics [...] to understand and manage the drivers of cost and revenue."

"Leading firms are much less likely than laggards to hire experienced professionals, opting instead to build from within or grab talent straight out of college," wrote the study authors from A.T. Kearney. They are partner Christian Hagen, partner Khalid Khan, and director Bharath Thota. "Given enough time, these junior hires can be taught the specific skills necessary for their company's business and industry and be just as valuable over the long term."

Companies employ some similar approaches to achieve this kind of on-the-job training. Rotational programs move talent through different silos within the business. This approach provides "an organic way to get analytics talent an understanding of every function," which helps spread analytics across the whole organization.

That's how Charles Whittaker got started at Avant. The online loan company offers the rotation as part of its management program, Whittaker told InformationWeek in an interview earlier this year. Once you find your niche, you stop rotating, he said. He is the director of business intelligence at the company.

Another tactic leading companies use is cross-disciplinary teams to embed analytics throughout the organization.

"If having analytics talent at every part of the company is not possible, a strong solution is creating something akin to SWAT teams of analytics talent," the study authors wrote. "These teams can work shoulder to shoulder with functional teams in something of an incubator model, embedding analytics knowledge across an organization while giving the analytics talent key company knowledge."

[Universities aren't the only source of big data training. Read Bridging the Big Data Skills Gap With Online Training.]

That's the approach that Penn Medicine has taken, according to chief data scientist Michael Draugelis, who leads the effort there. Draugelis told InformationWeek about the approach during a recent interview.

A.T. Kearney also recommends building industry-university partnerships, which is something the firm has done itself through summer internships aimed at college students, and other initiatives.

"The [leading companies] see partnerships with universities as a way to get the talent they need for analytics positions -- they are four times more likely to consider this path than laggards, according to the study," the authors wrote. "Creating these partnerships could be as simple as putting in an internship program, or even helping colleges shape their curricula so they are more relevant for the business world of both today and tomorrow."

**Elite 100 2016: DEADLINE EXTENDED TO JAN. 18, 2016** There's still time to be a part of the prestigious InformationWeek Elite 100! Submit your company's application by Jan. 18, 2016. You'll find instructions and a submission form here: InformationWeek's Elite 100 2016.

About the Author(s)

Jessica Davis

Senior Editor

Jessica Davis is a Senior Editor at InformationWeek. She covers enterprise IT leadership, careers, artificial intelligence, data and analytics, and enterprise software. She has spent a career covering the intersection of business and technology. Follow her on twitter: @jessicadavis.

Never Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.

You May Also Like

More Insights