Big Data analysts are today's IT rock stars. Consider this advice on how to snag an increasingly coveted job.
Big Data Talent War: 10 Analytics Job Trends
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As companies and service providers collect ever growing amount of data and information from applications, Web interactions, social media, and clickstreams, the need for analysts who can mine gold from these mountains of data has grown ever more urgent. Among the IT skills in greatest demand is business analytics, according to the InformationWeek2012 U.S. IT Salary Survey.
Another factor may be Brad Pitt, who provided a touch of "coolness" to the job when Hollywood actor showed up in the movie Moneyball as Billy Beane, the Oakland A's general manager who brought the team to victory using predictive analytics. Still, it is unlikely that a hit movie has created an unprecedented demand for data scientists.
It must be the data. In fact, 83% of respondents foresee an increase in the next five years in the number of data scientists needed because of the expansion of new technologies and expectations with unstructured data, according to a new global survey from storage and data management vendor EMC. Moreover, 63% of respondents said the need for data scientists will somewhat or significantly outpace the current talent supply.
That's all the more reason, in today's tough IT job market, to jockey for a position as a data scientist. Mike Driscoll, CEO of Metamarkets, a data analytics company in San Francisco, shared his tips for landing one of the hottest gigs in high tech.
-- Domain expert vs. machine learner. It's a never-ending debate in Big Data circles--whether it's better to hire a domain expert or machine learner. Although both sets of skills are valuable, Driscoll said, "for framing questions, domain expertise is very important. After all, if you didn't know anything about baseball, it would be difficult to build a team of great hitters." At the same time, Driscoll added, "after the questions are framed properly, machine running is more important because at that point it becomes a question of building a model of historical observations"--a task made simpler by those with extensive machine-learning expertise.
-- Natural curiosity. No amount of schooling can teach an IT professional how to become a great storyteller. And it's precisely this talent that is a "critical piece" of mining data for valuable and fresh insights. "Ultimately, data science is about finding stories in that data, being curious, and asking the right questions," said Driscoll. "That's what will tease a great story out of the data." Besides, "It's always easier to teach someone domain expertise than it is to infuse them with natural curiosity and the brain power to do something about it."
-- A scholastic hybrid. These days, many aspiring data scientists are combining degrees in areas such as political science with a minor in math. That's an excellent plan of action, said Driscoll. "There are certain domains or areas of knowledge that are difficult to learn on your own and outside of a formal educational process--those domains are often the hard sciences."
-- Dirty hands. Although data science theory offers a tidy view of zettabytes, data is rarely mess-free. As a result, Driscoll said he prefers to hire candidates who aren't afraid of "the grimy work--the coal mining of the information age which is to extract, transform and load data." The practical experience of building databases and handling "messy" data in the real world is a sign of someone who's ready and willing to dive in and "learn the art" of data science, he said.
-- See Moneyball. "Moneyball is the touchpoint where we had an 'a-ha' moment that data can beat the rotund gut of the tobacco-chewing pitching coach," chuckled Driscoll. Because when it comes to getting inspired by the beauty of data analytics, "Brad Pitt being the patron saint of Big Data certainly doesn't hurt."
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InformationWeek Tech Digest, Nov. 10, 2014Just 30% of respondents to our new survey say their companies are very or extremely effective at identifying critical data and analyzing it to make decisions, down from 42% in 2013. What gives?