Demand for big-data skills rises even higher with growth of analytics in security, self-service BI and analytics processing in the cloud and on-premises
Fueled by the need for faster, better and more competitive business analytics, the demand for Big Data solutions – and the staff to deliver them – continues to skyrocket. This blog by Slashdot contributing editor John O’Donnell looks at some recent research studies that demonstrate the demand for data scientists and analysts, and how it is impacting hiring and pay scales.
The insight promised by sophisticated analytics has become so important to businesses that, by 2014, it had become “the air that we breathe — and the ocean in which we swim,” according to the Deloitte Consulting report Analytics Trends 2015.
Demand was even higher during 2015, driving sales growth of 23 percent, putting high-end analytics on track to generate almost $49 billion per year by 2019, according to IDC.
Demand for the special analytic, mathematic and data-architecting skills of data scientists has also skyrocketed – rising much faster than the number of data scientists and leading some organizations to loosen their definition of “data scientist” just to fill their open positions, according to the Forecast 2016 survey published by Computerworld in December, 2015.
Data Analytics Job Openings Continue to Soar
The number of jobs posted for big-data specialists and data scientists rose 27 percent in the US and 22 percent in the UK during 2015, compared to a national (US) average of 11 percent.
The increase drove big-data skills to No. 4 on the list of most-desired IT skills on the, up from No. 10 the previous year. That increase may understate the reality, however; 36 percent of senior-level IT managers responding to the Computerworld survey said they would hire people with big-data skills during 2016. Another 34 percent said they’d hire from the closely-aligned business intelligence/analytics category.
Big data, especially analytics housed by increasing numbers of HPC-friendly cloud- and SaaS platform providers, hit No. 1 on the list of technologies companies are testing, according to Computerworld. Confirming the impact of the big-data trend on those who make it happen, Robert Half Technology released a report in December showing that Big Data had risen from the No. 10 hiring priority during 2015 to the No. 4 priority now. That increase in demand has pushed salaries for data scientists up about five percent, according to Robert Half.
It has also made IT managers nervous about finding the right talent. When Computerworld asked IT professionals to name the most difficult skills to hire, 33 percent said that two consecutive years of record data breaches had pushed security to the top. Thirty percent said big data/analytical skills would be the hardest to find during 2016. Salaries for data scientists have already risen 8.9 percent this year, according to the 2016 edition of Robert Half Technology’s Salary Guide, which estimated other database/analytics salaries had risen between six and nine percent.
A shortage of potential new hires with the right skills is the biggest issue holding back companies wanting to move past plain old big-data analysis and get into predictive analytics, according to a study on best practices in predictive analytics by TDWI. Predictive analytics is more aggressive than typical big-data analysis because it extrapolates results into the future, using rigorous statistical modeling and analysis to identify and validate trends likely to have an impact, according to TDWI.
Changing Job Requirements for Big Data
Faced with a shortage of talent and software that gets easier to use all the time, however, TDWI also found that even companies with extensive analytics programs have shifted their hiring priorities away from data science, even when hiring data scientists. Of the 330 data warehousing and business-intelligence executives TDWI polled, only 34 percent said a degree or other extensive background in statistics and math or any training in predictive analytics was an important indicator of success for data scientists – even those using advanced statistical modeling for predictive analysis. Instead, the most desired characteristic was “knowledge of the business” (74 percent) followed by “Critical thinking” (67 percent), followed by a solid understanding of how to properly source data and integrate it so the resulting data sets remain valid.
Actual training in predictive analytics was considered critical by only 41 percent of respondents; only 34 percent said a background in statistics or math would be important.
When it comes to high-powered analysis, it is the result that matters, not the sophistication of the process, according to John Reed, senior executive director of Robert Half Technology, who was quoted in the Computerworld survey story.
“Companies are typically looking for someone who can help them manage data and package that data,” Reed said. The most successful data scientists are those “who can interpret and then help bring that data to life visually — building dashboards and things of that nature, so the executive suite can push through techspeak and understand what the data is telling them,” Reed said.
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