So-called “Big Data” has never been more popular among organizations, and those with the ability to analyze vast amounts of data find themselves much in demand.
That demand, in turn, could drive more graduates to embrace analytics as a career. But for anyone contemplating such a move, be warned: Data scientists within even the most dynamic company face a whole lot of drudgery as part of their daily routine. A new study by The New York Times estimates that data scientists spend between 50 percent and 80 percent of their time “mired in [the] more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.”
That isn’t to denigrate data analytics as a field somehow unworthy of pursuit—drudgery is an element of nearly every job on the planet, and the increasingly lucrative salaries attached to data-analytics positions all but guarantee even the dullest-sounding opening will attract candidates. The “mundane” part of the data-analytics equation is worth mentioning because, over the past few quarters, a number of startups (and more than a few established companies) have issued software supposedly designed to “streamline” analytics and place sophisticated data tools in the hands of those without much training.
Yet despite the aggressive marketing behind those “magic bullets,” analytics remains a job largely defined by skill sets mastered only after years of work and study. “It’s an absolute myth that you can send an algorithm over raw data and have insights pop up,” Jeffrey Heer, a professor of computer science at the University of Washington, told the Times.
While those complexities could also end up protecting the jobs of many highly specialized data scientists for quite some time, it seems inevitable that new generations of tools could allow companies to surface finer insights from rougher data. The question is: How much “mundane labor” will those tools end up eliminating from the analytics process?
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