The Skill & Training Mandates of Big Data

Big Data Mandates

For some years a dearth of data scientists and analysts has caused concern, with McKinsey expecting a demand for analytical expertise 60% greater than available supply by 2017. Additionally, Gartner predicts a shortage of 100,000 data scientists in the US by 2020. In more recent findings, Accenture reported the vast majority of its clients intended to hire employees with data science capabilities but over a third cited lack of talent an impediment. It should come as no surprise that this relatively new, but booming, arena is undergoing significant shifts; big data has suddenly been recognized as a valuable commodity, organizations are investing in big data analytics to improve strategies and operations, and universities and colleges are creating and streamlining data analytics training programs to meet these new skill requirements.

Big Data Analytics

In businesses around the world, the role of big data analytics has grown. Though big data is readily identified as a golden ticket to business success, most organizations are only just coming to terms with the need for suitable data science skills to turn this barrage of information into valuable and constructive insight. Some of the benefits gleaned from analyzed data and predictive analytics include targeted marketing campaigns that provide well-timed offers to the right people, exceptional customer service through better anticipation of needs, and progressive business models able to leverage connectivity for greater success.

As valuable as the data science tools we currently utilize are, industry experts believe the future holds much more. The International Institute for Analytics expects automated data curation and management to free up data scientists and analysts to do more of the work that interests them in future years while the IDC predicts that through 2020 spending on data preparation and self-service visual discovery tools will grow two and a half times faster than traditional IT-controlled tools for comparable functions. Unfortunately, the skills shortage is likely to persist, and Forrester believes the demand won’t be met in the short term, “even as more degree programs launch globally.”

Big Data Training

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No matter the pessimism of some of our top analysts, educational programs aimed at bridging the big data skills gap are increasing and advancing. For those commencing training, a shortage of skills is a distinct advantage as they prepare for a market in which they have the upper hand. At the end of last year, RJMetrics reported that only 11,400 professionals classified themselves as data scientists and 52% of all data scientists had achieved their titles in the last four years. Many companies simply can’t wait for the next batch of graduates to meet their needs and some of the more progressive are implementing their own training schemes which encourage skill development within the organization. Along with STEM schools, online training platforms, and university partnerships, in-house training may help enlarge our skills pool.

It’s interesting also to note that Gartner expects an increase of citizen data scientists in the coming years; states Rita Sallam, VP of Research at Gartner, “Through 2017, the number of citizen data scientists will grow five times faster than the number of highly skilled data scientists.” As businesses and educational institutions provide their own investments into big data skills, it’s important to remember the opportunities technology has offered and continues to offer the self-made individual; opportunities abound too for the unconventionally trained but astute data scientist and as we navigate the current skills gap many have as much sway as their graduate colleagues.

Taking Advantage of the Talent Gap

According to SAS, a provider of industry-leading analytics software and solutions, it’s possible to turn the “analytical talent gap into an analytical talent dividend.” Developing key insights from MIT Sloan Management Review Research Report, “The Talent Dividend,” SAS highlights the value of finding talent that already exists in the business, integrating new hires with existing specialists, and pairing data scientists with business domain experts for better results. Practical business leaders are quickly learning that an innovative approach to talent cultivation often affords greater rewards.

Article sponsored by SAS Software and Big Data Forum

By Jennifer Klostermann

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