Big Data, and Volume, Velocity, and Variety

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The terms “volume”, “velocity”, and “variety” turn up often – very often – in discussions about “Big Data”.  Gartner’s Doug Laney has laid claim to the original collective use of these terms, in a then-META Group article in February 2001 (“3D Data Management: Controlling Data Volume, Veocity, and Variety“).

If Mr. Laney is indeed correct that he is the originator of these concepts in the context of Big Data, then his ideas have proved  amazingly resilient for an issue that is receiving so much hype recently.  Indeed, the use of volume, variety, and velocity have become ubiquitous in any discussion of Big Data.  Look up “Big Data” , and the “3 Vs” turn up in abundance (some  79,000 times.)

Although Mr. Laney phrased it as ‘volume, velocity, and variety’, we analysts, consultants, pundits, and technology writers have mixed it up a bit when using these terms.

Big Data’s 3Vs in use

To take a look at how the “3Vs” have been used, I did a Google search of the three terms + “Big Data” from January 1, 2001:

COUNT  PCT of Total
1 volume, variety, and velocity 3,980 57.6%
2 volume, velocity, and variety 1,970 28.5%
3 variety, velocity, and volume 504 7.3%
4 velocity, volume, and variety 236 3.4%
5 velocity, variety, and volume 130 1.9%
6 variety, volume, and velocity 93 1.3%
6,913

 

To the extent that these variations mean anything, it is interesting to note that while volume has maintained its position at the head of many lists, the terms “variety” and “velocity” are, by a 2:1 margin, inverted from their usage in Doug Laney’s 2001 article.

The interesting thing about “big data” is that this is one of the few technology areas defined by the problem rather than by the available solution sets. So it is not surprising, perhaps, to see a lot of focus on the volume part of the problem.

The challenge of “big data” (exploding data volumes, and the increasing complexity of what we want to do with the data) will require serious strategies and solutions.  ”Volume”, “velocity”, and “variety”  have been employed nearly to the point that they do more for taking up white space as they do for clarifying core issues.   It may be time to consider some fresh approaches to talking about “big data”, analytics, and  other relevant solutions sets. In the meantime, we owe Doug Laney his due for providing such a resilient vocabulary for Big Data.