by Paul Clark, Solution Marketing, SAP
Cutting through the hype
At the Gartner BI Summit last week attendees heard, predictably, a lot of talk about big data. And given where the topic is in the “hype cycle” there was a huge amount of hype too!
In amongst the hype, there was some useful, educational content on big data, like Merv Adrian’s session “Introducing “Big Data” Map/Reduce and Parallel Distribution Processing to the Warehouse.” Also good to hear were the multiple reminders that big data does not just mean large volumes. That’s especially important if you’re a small or midsized enterprise (SME), because you don’t need to be big to be impacted by the big data wave.
What’s a good definition for big data?
The industry came tantalizingly close to agreement on its words to describe big data, but in the end opted for partial agreement. Here are three versions heard at conferences in the last month or two:
- SAP: volume, velocity, variety, validity
- IDC: volume, velocity, variety, value
- Gartner: volume, velocity, variety, complexity
At least they agree on the first three Vs! Let’s take them in turn.
Volume
It’s sometimes argued that this one simply doesn’t apply to SMEs. Maybe, but then isn’t “big” a subjective term? Who’s to say that my 500GB of customer data is not big? It might be to me
Velocity
One other good comment at the Gartner event was on velocity. Don’t think of it as simply meaning “fast” data, but think instead of data coming in at varying speeds. Some updates happen weekly, some daily, some multiple times a day, and some as a non-stop stream like event data or Twitter feeds. One of the challenges of big data is to combine these different streams with different velocities. That’s maybe not a big volume, but it’s still a big headache if you don’t plan your approach, however big or small you are.
Variety
The third “agreed” V is also an important one to bear in mind. Again those social media feeds come to mind, and the need to combine those with the relevant in-house records on customer data or product data. Yet again, that’s something that is important to companies of all sizes.
Should SMEs care?
I think they should! Like large enterprises, SMEs need to be wary of the hype, but there could be value in big data, even for the smallest companies. In the meantime, check out hype-free analytics solutions for SMEs here!
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April 14th, 2012 at 9:07 am
Great to see the industry finally adopting the “3V”s of big data over 11 years after Gartner first published them. For future reference, and a copy of the original article I wrote in 2001, see: http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/. –Doug Laney, VP Research, Gartner, @doug_laney
April 14th, 2012 at 9:11 am
Note also that Gartner developed an “extreme data” model a couple years ago that expresses a dozen different characteristics/challenges: http://www.gartner.com/resId=1622715 – Doug Laney, VP Research, Gartner, @doug_laney
April 17th, 2012 at 10:58 am
Hi Doug
Thanks for the links and the historical reminder! It looks like the concept was built to last.
Paul
September 20th, 2012 at 1:14 pm
Here’s another twist from Forrester: volume, velocity, variety, variability
“The Big Deal About Big Data For Customer Engagement,” Sanchit Gogia, June 1, 2012
September 26th, 2012 at 9:17 am
Yet another, this time from IBM: volume, velocity, variety, veracity
http://tdwi.org/articles/2012/09/25/Big-Data-Hadoop-Confusion.aspx