5 V's of Big Data

As CIOs struggle to understand Big Data, many wrap their arms around the 5 Vs to give meaning to heavily-hyped term.

The five Vs, originally a mere three Vs, describes the characteristics of Big Data. Do they help us make Big Data productive and meaningful? Not necessarily.

The Vs just remind about what big data looks like, according to blogger Edd Dumbill. Edd writes very well even though his name, ahem, might suggest otherwise.

The first three Vs - volume, velocity and variety - are largely self-defining:

Data volumes are huge: Huge volumes are being created at ever-increasing velocities. And we know ther's more variety of data formats than Heinz has ketchups. The challenge is how IT deals with those volumes, velocities and varities of data.

The hardest Vs are four and five: validity and value. How do we validate data that's useful, reliable and accurate and once we've done that, how do we extract value from it? Validity is sometimes interchangeable with veracity or verification. Same idea.

As Dumbill points out, that is being left to the emerging area of data science and data scientists.

I wondered who came up with the 5 Vs. Database consultant Dave Beukle acknowledges in a 2011 post that others were writing about the first three, but he may have added four and five.

In any event, we still at the beginning so humor is essential. Let's hope we're not still at the beginning a year from now.

Like the Vs, there's a lot of terminology and clever descriptions that seem to stick to Big Data. I like oneblogger Charlie Wang just recycled:

“Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it.”

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