How To Fill Big Data Talent Gaps Fast

Consider training current employees or recruiting from our list of analytics master's degree programs.

Doug Henschen, Executive Editor, Enterprise Apps

January 9, 2013

5 Min Read

 Big Data Analytics Masters Degrees: 20 Top Programs

Big Data Analytics Masters Degrees: 20 Top Programs

Big Data Analytics Masters Degrees: 20 Top Programs (click image for larger view and for slideshow)

Stop sweating the much-hyped big data skills gap and start exploring training and recruiting options.

Experts are trained, not born. One question you need to answer is whether you're looking for technical expertise or data expertise. Technical expertise is what's needed to bring NoSQL databases or Hadoop clusters into production. Data expertise is what's needed to take advantage of data mining, text mining, forecasting and machine learning techniques. Either way, let's start with the assumption that nurturing talent from within your current staff is the first and best option.

Our 2012 Big Data and Analytics Staffing Survey shows that most companies plan to do a mix of retraining, hiring and outsourcing to fill their talent gaps. But getting new salaries approved, working with recruiters, and interviewing and onboarding candidates is a time-consuming process. What's more, professionals on staff presumably have a good handle on your business, your data sources, your current analyses and most likely, what you're hoping to get out of big data and advanced analyses.

If it's technical expertise you lack, trust that your existing DBAs, database developers and data-warehousing pros can learn new tricks. Moving from a conventional database to a massively parallel processing (MPP) database platform, for example, is not a huge leap for your talented DBA, and the right person will be energized by the new challenge.

[ Want more on big data analytics staffing options? Read Big Data And Analytics Expertise: Beg, Borrow Or Steal? ]

Vendors invariably have low-cost training and certification options. Hewlett-Packard, for example, just announced course modules and certification exams around its MPP-based HP Vertica Analytics Platform. The introductory course is delivered over three days in HP ExpertOne classrooms around the globe. There is also an option for a virtual training online, which is delivered over four days. "It's technical product training on how to set up the software and get the most out of it, and it's geared to everybody from DBAs to data scientists," says HP's Chris Selland, VP of marketing.

Big data that's marked by data variety and extreme volume is mostly being handled on new platforms such as Apache Hadoop and NoSQL databases. Lots of companies without prior experience are getting Hadoop clusters into production in as little as five months, according to John Schroeder, CEO of MapR, a Hadoop software, support and training provider. "There's a scramble for talent right now because there are more projects than people, but the number of people trained in Hadoop is growing dramatically versus what we saw in 2010 and 2011," Schroeder says.

MapR and its competitors, Cloudera and Hortonworks, offer plenty of training courses. In MapR's case, they're typically two- to three-day MapR Academy video, online and classroom courses on topics including fundamentals of cluster administration; hands-on Hive, Pig and MapReduce training; and HBase programming. Cloudera University offers online and classroom courses globally while Hortonworks University offers classroom courses in major cities and, with six or more participants, on-premises at company locations.

The assumption with these training options is that you have competent system admin and developer types who can pick up on new technologies that don't have the familiar feel of SQL and relational databases. In the case of more advanced data-analysis-oriented courses, such as "Introduction to Data Science -- Building Recommender Systems" (offered by Cloudera University), the target audience is "software engineers, data analysts and statisticians with basic Hadoop knowledge and proficiency."

If your data-management team isn't up on advanced analytics topics like data mining, text mining and forecasting, you might consider recruiting from one of these top analytics master's degree programs. The shortest of these programs can be completed in 10 months, but that's full-time, so it's probably not appropriate for a current employee, unless they can take a sabbatical. There are part-time and online options that take about two years at a minimum.

We developed our master's degree compendium mostly as a resource for would-be students, but if you're in a hurry to fill a talent gap, look to these programs as a recruiting resource. The advantage of dealing with masters programs is that most students will have three or more years of business experience. Most schools will be only too happy to welcome new employers into their recruiting programs, particularly if they're larger firms that have a steady demand for new employees.

Some schools even do joint research with sponsoring companies, turning professors and teams of grad students loose on real-world business challenges. That's the case at North Carolina State University's Institute for Advanced Analytics and at MIT's Center For Digital Research. Look for research centers or institutes affiliated with analytics or big data degree programs.

The bottom line is that there are plenty of options other than hand wringing and high-priced head hunters. If you're looking for very senior people, the task will undoubtedly be harder. But before you buy into the "I can't find talent" hysteria, determine your needs, size up the training options and strike up a relationship with a local university.

Tech spending is looking up, but IT must focus more on customers and less on internal systems. Also in the all-digital Outlook 2013 issue of InformationWeek: Five painless rules for encryption. (Free registration required.)

About the Author(s)

Doug Henschen

Executive Editor, Enterprise Apps

Doug Henschen is Executive Editor of InformationWeek, where he covers the intersection of enterprise applications with information management, business intelligence, big data and analytics. He previously served as editor in chief of Intelligent Enterprise, editor in chief of Transform Magazine, and Executive Editor at DM News. He has covered IT and data-driven marketing for more than 15 years.

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