For a good working definition of big data, remember the Vs, NetNewsCheck reports.

Nitesh Chawla, a professor of computer science and engineering at the University of Notre Dame and founder of Aunalytics Inc., a big data analytics firm, says it’s easiest to understand big data in terms of volume, variety, veracity and velocity.

First the volume part. Think about the sheer magnitude of data handled by media companies — text, images, video, numbers — all of which adds up quickly as it accumulates each hour and day. Variety speaks to the different types of data coming in, living in different systems and databases, now mixed together as part of the bigness of big data.

Veracity? “How much do you trust the data?” Chawla says. “The data could be noisy. It could have human error when entering it. What’s the quality of the data you’re dealing with?”

… For Elizabeth Bruce, executive director of the Massachusetts Institute of Technology’s Big Data Initiative at the Computer Science Artificial Intelligence Lab, big data is a fire hose — “too big, too fast, too hard” for most computer systems and algorithms to competently manage.

“We’ve been able to collect large amounts of data for a really long time, but the kinds of analytics that people now want to be able to run on top of that data to find patterns and identify trends … is where the complexity is going way out from what people have done in the past,” she says.

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About the Author:

Denny Carter

Dennis has covered higher education technology since April 2008, having interviewed some of the most recognized IT pros in U.S. colleges and universities. He is always updating eCampus News with the latest in pressing ed-tech issues, such as the growing i