These specialists, who can supervise the integration of many types of data, find themselves in great demand compared to traditional data analysts.
12 Top Big Data Analytics Players
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Data scientists "can take a data set and model it mathematically and understand the math required to build those models," according to Hilary Mason, chief scientist for the URL shortening service bit.ly., as quoted in the Journal. "That means asking the right questions, and that is usually the hardest piece."
Traditional data analysts look at data from a single source--a CRM application, for example. Data scientists examine data from multiple sources, rationalize differences among the data types, and create a data set usable by less specialized analysts that also accurately reflect trends within the data itself and how to apply them, according to IBM.
The requirements described by data specialists themselves are much more mundane, and much less likely to contain the term "data scientist," at least in their own job descriptions.
Data scientists may simply be the most senior among a group of data-analysis specialists. More likely those given or choosing to use the title are better educated, better paid, more experienced, and have a wider set of skills than others with big-data skills, according to a survey of data-analytic specialists (PDF, free registration required) recently published by BI vendor SiSense.
Only 5% of most data professionals hold a Ph.D. in a relevant specialty, for example, while 35% of data scientists hold one, the survey showed.
Data scientists also make more money than other data professionals. Those without management titles averaged between $70,000 and $90,000 per year, compared to $65,000 to $70,000 for more traditional data specialists.
On-the-job experience counts for as much as education, however. Those with 10 years of experience or more get salaries 80% higher than those with equivalent training but with three years experience or less, the survey showed.
Data specialists of all varieties, but especially those claiming the title scientist, have been doing better financially during the past two years as well. Forty seven percent reported earning between 1% and 10% more this year than last; 7% reported increases of between 10% and 20%.
Another 7% percent reported raises of more than 20% in 2012 compared to 2011.
Seventy-eight percent expect to make more during 2013 as well; 11% expect raises of 20% or more, 14% expect raises of 10% to 20%.
The upshot is that data scientists have more education, experience, and breadth of understanding than other big-data specialists and are increasingly expecting to be paid a premium for that knowledge.
While some technical skills are particularly valuable--especially BI and data warehouse skills, math and statistical abilities, and data visualization--project management, business-function expertise, and general business skills are rated even more important.
Despite advantages in career development, compensation and prestige, very few data specialists actually call themselves data scientists.
Only 15% of respondents listed their job title as containing the word "scientist," compared to 34% listing themselves as "business analysts," 27% with the title "data analyst," and 19% with management titles such as director of analytics or VP of analytics.
The reason so few who could claim the title actually do use that title isn't answered in the SiSense data or anyone else's, but the accompanying analysis does suggest a reason.
Despite the buzz around big data, "there is no clear definition of what a "data scientist" really is."
Whatever the job description really is or may eventually be defined to be, the one thing about data scientists that nearly every survey and analyst agrees on is that there are not enough of them now and that the demand for them will continue to grow during at least the next five years. Even after the first rush of demand for big-data skills passes, the requirements for becoming a data scientist ensures they will remain a rare breed.
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