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Big Data: Where The Needs (and Jobs) Are In 2013

2013 is shaping up as pivotal in the race for Big Data experts.

Clear trends are emerging in the analytics roles that are most needed, as well as the steps that employers and employees can take to position themselves to capitalize on the Big Data wave.

56% of companies plan to hire new employees for data collection or analysis, according to a recent survey conducted by Infogroup. An additional 5% plan to hire an executive to hire their data projects including analysis.

These and other hiring-oriented findings can be found in the UBM Tech e-book, Big Data 2013.

Analysis, rather than just gathering “Big Data,” is top of mind for decision makers: 45% say analyzing or applying information will be their biggest challenge in 2013.

That need for analysis — in BI dashboards, spreadsheets, and even verbally — is emerging as the most important need. It clearly trumps gathering, normalizing and crunching data as the skillsets companies will prioritize.

IBM sees the need, and is teaming with several blue-chip universities to train data “scientists” (a term almost as in vogue as Big Data) who can deliver analytical insights to top business decision makers. An IBM exec says the key is knowing how to interpret the data first and foremost, so a business can take action.

For those companies waiting to fill analytics positions, there’s no magic bullet as degree programs come online.

In a separate analysis, the State of IT Staffing, InformationWeek Reports finds that companies expect to do a strong measure of retraining existing staff to perform Big Data analytics. 11% of respondents will use retraining, 28% will do mostly retraining with some hiring, and 33% predict an even mix of retraining and new hires to fill their needs.

The top reasons for this conservative viewpoint on Big Data analytics hiring: an anticipated limit on available talent, and the high salary demands that the supply-demand imbalance is expected to create.

The strong demand for people, and the nascent move to train individuals at the university level for big data analytics work – also points to an opportunity for consulting firms to fill a void, if not permanently, at least for the next several years until knowledge – and supply – catches up with market demand.

Rajeev Nayar highlights the service opportunity eloquently in this Big Data Republic blog:

“Predictive analytics will get a huge boost in a services climate as enterprises can offload their scalability challenges to the analytics partner,” Nayar writes. “With yesterday’s data being obsolete today, enterprises can worry less about data storage challenges, about complex algorithms required to analyse the data, about the speed with which data becomes redundant, and focus on their core business drivers and their customers.”

Still, the notion that a full-time, master’s- or Ph.D-toting scientist will be required to perform the analysis most companies need may be one of the biggest myths surrounding Big Data. (It falls into line after the notion that the collection of information on a ‘Big Data’ scale is a recent phenomenon). This analysis sheds light on what should actually be required to manage the analytics workload in most companies:

Another myth: The prevailing idea that these data questions can’t be answered without a data scientist on the scene, and that their small number will be a huge constraint on businesses wanting to conduct big data projects. Analytical tools will become more powerful, putting these abilities “into the hands of regular people,” says Tibco Software executive Chris Taylor.

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