Professionals in big data are big deals in today’s largely sluggish U.S. job market.
The demand for talent capable of gleaning useful information from businesses’ increasingly large and diverse data sets — generated by sensors, electronic payments, online sales, social media and more — is outpacing the supply of workers.
Take Enova International, which analyzes more than two dozen data sources to determine, in less than 10 minutes, whether an applicant will qualify for one of its three-year, $10,000 loans.
In the past three years, the growing Chicago online lender has doubled the size of its analytic team to 25 people, and next year, it would like to increase it by 50 percent, said Adam McElhinney, Enova’s head of business analytics. “There’s a shortage of talent that we’re looking to address,” McElhinney said.
By 2018, the United States might face a shortfall of about 35 percent in the number of people with advanced training in statistics and other disciplines who can help companies realize the potential of digital information generated from their own operations as well as from suppliers and customers, according to McKinsey & Co.
That deficit represents more than 140,000 workers, the consulting firm estimates.
Workers in big data are hard to come by in the short term. A recent survey by CareerBuilder — an affiliate of Tribune Co., which owns the Chicago Tribune and is a partner in McClatchy-Tribune News Service — found that “jobs tied to managing and interpreting big data” were among the “hot areas for hiring” in the second half of 2013.
“There aren’t enough of them. Period. End of story,” said Linda Burtch, founder of Burtch Works, an Evanston, Ill.-based executive recruitment firm. “The demand for quantitative professionals has grown so across industries that there aren’t enough kids coming out of school studying math and statistics.”
As a result, about half of Enova’s data analysts have visas or green cards.
Historically, Enova typically hired people with degrees in statistics, computer science and industrial engineering, but it has broadened its potential talent pool to include people with backgrounds in astrophysics and computational chemistry.
Enova, a unit of Texas-based Cash America International Inc., visits with Northwestern University and the University of Chicago several times a year, recommending that they adjust their curriculum to help turn out graduates with the skills for big data.
“Over the past five years, there has been a convergence of data analysis and computer science,” McElhinney said, noting that big data requires proficiency at both. “Five years ago, that was not the case.”
On Oct. 11 at the University of Chicago, Enova, which has more than 1,000 Chicago-area workers, is sponsoring a “data smackdown” in which it will provide students with a data set and business case, and the students have six hours to make recommendations. Meanwhile, IBM said Aug. 14 that it has now partnered with more than 1,000 colleges and universities, including those at Northwestern and DePaul, to try to narrow the skills gap on big data.
Thanks partly to advances in software and database systems, companies find it easier to capture, store, crunch and share the data in ways that help their business serve customers, predict their behavior, innovate, improve productivity and cut costs. The computing power of the average desktop computer, for example, has risen by 75 times from 2000 to 2013, McKinsey said.
Big data pays well. Median base salaries for nonmanagement workers is $90,000, according to a 47-page Burtch Works report published in July that surveyed 2,845 of the quantitative professionals in the firm’s database.
Nearly 9 of 10 big data professionals have at least a master’s in a quantitative discipline such as statistics, applied mathematics, operations research or economics, according to Burtch Works.
t companies, they work in such areas as analytical database marketing, analytics management and business intelligence. Nearly 40 percent are foreign citizens, Burtch Works found in its study.
Nine out of 10 quantitative professionals are recruited over LinkedIn at least once a month, Burtch said.
“Candidates with a strong breadth of knowledge in big data are challenging to find,” said Rona Borre, chief executive of Instant Technology, a Chicago-based talent management firm.
She said junior-level professionals in big data can start out earning $80,000, with senior technicians making as much as $140,000.
Bill Franks, chief analytics officer for Teradata Corp., said his Dayton, Ohio-based company has turned to external recruiters to help fill jobs in big data “because it’s so difficult to find people.”
Also, traditionally, Teradata’s analytics team considered people with at least 10 years of experience, but now it is looking more closely at applicants with less experience, said Franks, also author of “Taming the Big Data Tidal Wave.”
Joe DeCosmo, director of advanced analytics for West Monroe Partners’ technology solutions practice, said his Chicago-based consulting firm has 55 professionals specializing in data management warehousing and analyzing big data. Of those, 10 have been added this year, and West Monroe has plans to add at least 10 more by year’s end, he said.
West Monroe has stepped up its recruiting and networking events, and it encourages current workers to get involved in the local chapter of the American Statistical Association to meet potential job candidates and to do more speaking at local colleges.
Big data professionals are also becoming more important to insurance companies, which need help sorting through and learning from data to provide better services and savings for their policyholders.
For example, Progressive Corp.’s Snapshot device, which is installed in the car and collects driving data, has pulled in more than 8 billion miles of driving data, and that number increases by the second.
As such, Progressive has “become much more proactive” about finding big data talent, including having a staff of “sourcing specialists” who home in on finding people with big data skills, said Adam Kornick, Progressive’s big data and analytics business leader. “As part of our recruiting efforts, we spend a lot of time highlighting Cleveland as a great place to live and work.”
Chicago-based Datascope Analytics, which calls itself a “data-driven design firm,” had three full-time “data scientists” in 2012. It has grown to eight and plans to expand to 20 to 25 over the next two years.
Dean Malmgren, co-founder of Datascope, said his hiring challenge has less to do with technical abilities — “almost anyone with minimal programming experience can teach themselves all the necessary tools, like many of us have” — and much more to do with a shortage of soft skills and creative potential.
“On the one hand, I disagree with the McKinsey numbers because many people have the potential to retool their existing skill set,” Malmgren said. “But on the other hand, I think the McKinsey numbers vastly underestimate the magnitude of the problem, which is that there are far fewer ‘creative thinkers’ than there are ‘nerds in the back room.’”
Another company is trying to grow its own big data talent.
Northbrook, Ill.-based Mu Sigma has more than 2,500 “decision scientists” worldwide who provide big data analytics to Fortune 500 companies in 10 industries.
“There are few ready-made ‘decision sciences’ professionals in the market, and so we focus on getting the right raw talent and then training them to become decision science professionals via our Mu Sigma University,” said Deepinder Dhingra, head of products and strategy.
“Over the past 12 months we have hired close to 850 decision scientist trainees that will be going through our Mu Sigma University and Decision Scientist Certification program,” Dhingra said. “We plan to hire more than 1,000 in the following year.”
Dhingra pointed out that the McKinsey report, in addition to citing a shortage of 140,000 to 190,000 qualified data scientists in coming years, also said there will be a need for 1.5 million executives and support staff who understand data.
Mu Sigma’s entry-level trainee professionals go through “an intense recruitment program” that includes aptitude tests to determine who has a “quantitative bent of mind”; group discussion, to spot individuals who can present and back their views and listen to feedback; and a “synthesis” test in which a candidate is shown a video and then asked to identify the key message. If they make it through those rounds, they undergo several personal interviews, a process that includes “props and interesting puzzles and case studies.”
Once a decision scientist trainee is recruited, they go through Mu Sigma University, where they learn such skills as the basics of consulting, the “art of problem solving” and the “art of insight generation.” They also take advanced statistics and are taught about machine learning, natural language processing and visualization, along with behavioral sciences and such big data technologies as Hadoop, Mahout and Cassandra.
Kristopher Kubicki — chief technology officer of Chicago-based Market Track, which just acquired the company he co-founded, Dynamite Data — said hiring is a challenge because advanced math skills are “desirable in basically every single knowledge profession right now.”
“Candidates with big data skill sets have lucrative options, and most companies looking to hire them have to compete with deep-pocketed financial institutions,” said Kubicki, whose company provides pricing and promotional intelligence to the retail industry.
GLOSSARY FOR BIG DATA:
—Big data professionals: Individuals who can apply sophisticated quantitative skills to data transactions, interactions or other behaviors to draw conclusions and recommend actions. They’re distinguished by the sheer quantity of data on which they operate, due to new ways to measure behavior and technological advances in the storage and retrieval of data.
—Internet of Things: The ubiquitous network of sensors, cameras and transmitters embedded in devices around the world.
—Units of measure: Gigabytes eventually become terabytes, which then become petabytes, which then become exabytes. Then it’s on to, respectively, zettabytes and yottabytes.
—Unstructured data: Not as easily searched as the highly structured and clean data sets of, say, customer purchase histories or inventory levels. It includes blog posts, social-media feeds, GPS tracking data, online chat rooms, and most audio and video content.
Source: MCT Information Services