Data Sherpas Needed
In the recent New York Times article Training to Climb an Everest of Digital Data, Ashlee Vance reported on the challenges associated with managing – and deriving value from – massive repositories of data.
“Researchers and workers in fields as diverse as bio-technology, astronomy and computer science,” reports Vance, “will soon find themselves overwhelmed with information. The next generation of computer scientists has to think in terms of what could be described as Internet scale. Facebook, for example, uses more than 1 petabyte of storage space to manage its users’ 40 billion photos. (A petabyte is about 1,000 times as large as a terabyte, and could store about 500 billion pages of text).”
According to Gartner Research, the volume of enterprise data is doubling every 18 months. This rapid data proliferation is causing day-to-day business challenges to evolve faster than the existing applications (or new applications under development) can react.
“Science these days has basically turned into a data-management problem,” said Jimmy Lin, an associate professor at the University of Maryland, at a recent technology conference.
From the beginning of civilization, mathematics (the language of science) has been central to our advancement. But our relatively new found ability to collect massive amounts of digital data has ushered in a new era for leveraging and benefiting from mathematics.
Advancements in machine learning technology using sophisticated mathematical algorithms are providing the capability to not only rapidly process large volumes of data, but more importantly, enable enterprises to make better data-driven business decisions.
According to Vance, companies large and small, as well as universities and government agencies, are “looking for big data experts” capable of scaling today’s digital data mountains.
Perhaps tomorrow we will even see a listing in the classifieds (or more likely in a Twitter status update) that simply reads:
Data Sherpas Needed
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