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3Vs (volume, variety and velocity)

This definition is part of our Essential Guide: Big data tutorial: Everything you need to know
Contributor(s): Ivy Wigmore

3Vs (volume, variety and velocity) are three defining properties or dimensions of big data. Volume refers to the amount of data, variety refers to the number of types of data and velocity refers to the speed of data processing. According to the 3Vs model, the challenges of big data management result from the expansion of all three properties, rather than just the volume alone -- the sheer amount of data to be managed.

Gartner analyst Doug Laney introduced the 3Vs concept in a 2001 MetaGroup research publication, 3D data management: Controlling data volume, variety and velocity. More recently, additional Vs have been proposed for addition to the model, including variability -- the increase in the range of values typical of a large data set -- and value, which addresses the need for valuation of enterprise data.

The infographic below (reproduced with permission from Diya Soubra's post, The 3Vs that define Big Data, on Data Science Central) illustrates the increasing expansion of the 3Vs. 

The 3Vs of big data

 

This was last updated in February 2013

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This is ridiculous. Coming up with abstract buzzwords to sell tools to distill garbage data into garbage results to CIOs reading about it in SkyMall.
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Great to see the industry finally adopting the "3Vs" of Big Data that Gartner first introduced over 12 years ago! Here's a link to the original piece I wrote on "The Three Dimensional Data Challenge" back in 2001 positing them: http://goo.gl/wH3qG. Interesting also to see others lop on additional "V"s that while interesting are decidedly no definitional characteristics of Big Data. --Doug Laney, VP Research, Gartner, @doug_laney
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I agree, as far as definitions go this is pretty useless. If one has never heard of the three V's before it is good to understand. What we really need is some kind of empirical definition that transcends time, sort of like Moore's Law. 

Here's my suggestion: "Data is Big Data when it is too big to work on any one commonly available computer, but rather requires a cluster of computers". "Commonly available" would then have to be defined somehow, for example "computers available in the majority of large and medium-sized businesses" so that mainframes would be eliminated.

The reason why a "cluster of computers" is important is because this requires a fundamental change in the underlying architecture of how mathematical functions are designed in order to perform acceptably when network communication is part of the system.

The amount of data that one computer can process has certainly changed over the years and will continue to do so. Therefore this kind of definition should be useful moving forward.
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