About Social Media Today
Comments by Doug Laney Subscribe 
On Driving Analytic Value From New Data
Great post Bill. Note that the 3Vs as I first defined them over 13 years ago (ref: http://goo.gl/wH3qG) were meant only to define the challenges and opportunities of Big Data. Value is important of course (along with a dozen other dimensions Gartner has identified), but it is not a defining characteristic of Big Data. That is, you can have Big Data but not be generating value from it. "Value" also is a vague, slipery word that's thrown around too casually: enterprise assets that are unutilized have probable value recorded on balance sheets, and deployed assets have realized value recorded in income statements. And "benefits" are often an unfortunate, insufficient proxy for actual value. Notwithstanding the fact that accounting principles *still* do not allow for information assets to be recognized, they meet all the criteria. Recognizing this and information's growing economic importance, I developed and have been teaching information economics (infonomics), including information valuation methods for some years (ref: http://en.wikipedia.org/wiki/Infonomics). Happy to connect on this w you.
Also, it's great that you point out the importance of "new data". This is one of the fallacies/limitations of Moneyball that people don't realize: new statistics were developed using old measurements. New ways of measuring player performance (e.g. Sportsvision's Field/fx system of capturing 2M datapoints per game) and similarly corporate/individual/process/machine performance are critical.
Cheers,
Doug Laney, VP Research, Gartner, @doug_laney
On Big Data Defined for 2013: A Definition That Can Help in Your Interaction with the IT Community
Great report, but unfortunate that TechAmerica authors failed to cite either Gartner's original "3Vs" framework that I conceived and authored over 12 years ago (ref: http://goo.gl/wH3qG), Gartner's expanded Big Data definition that incorporates use types and recognition of new forms of processing, or Gartner's "Art of the Possible" research note from 2011 -- each which they seemed to posit as their own ideas. Novel ideas may be hard to come by, but professional courtesy shouldn't be. --Doug Laney, VP Research, Gartner, @doug_laney
On The Buzz About Big Data Analytics
Hi, Here is a link to the original 3Vs article from 2001: http://goo.gl/wH3qG. Yes, TDWI and others use the concept without the professional courtesy of attributing it to Gartner. -Doug
On Big Data Vs. Traditional Business Intelligence
Great piece Kathryn. Monetization is definitely the big win with big data. Cool to see others in the industry finally adopting Gartner's original "3Vs" from 12 years ago. Ref: http://goo.gl/wH3qG. --Doug Laney, VP Research, Gartner, @doug_laney
On Why Big Data and Business Intelligence Are Like One Direction
Thorough piece Lachlan. Enjoyed it. Good to see the "3V's Gartner identified and first published about 12 years ago are finally understood and have been broadly adopted by the industry. For proper attribution, here is the piece I wrote in 2001 first suggesting and defining these three dimensions: http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/.
Since then, Gartner has identified 12 dimensions of data management challenge and published an updated definition of Big Data that reflects (as you also suggest) the value side of Big Data equation: "Big Data are high-volume, -velocity, and/or variety information assets that require innovative forms of processing for enhanced decision support, business insights, and process optimization."
Gartner has also developed methods for quantifying the economic value of Big Data sources that companies can use to gauge investments and opportunities in information management/analytics/infosec/etc, and a data magnitude index (DMI) that can help in planning and anticipating needs for new infrastructure & architectures.
--Doug Laney, VP Research, Gartner, @doug_laney
On Text Analytics, Big Data and the Keys to ROI
Another great piece Meta! And cool to see Gartner's 3Vs (volume, velocity, variety) finally catching on, albeit 11 years after we first defined them. For future reference or anyone that's interested in seeing that original Gartner piece from 2001: http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-.... Cheers, Doug Laney, VP Research, Gartner, @doug_laney
On Report from the 2012 Hadoop Summit
Great piece Kimberly. Good 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
On TechAmerica Foundation Announces Leadership for “Big Data” Commission
I think citizens should be *very* concerned about a commission comprised only of vendors (i.e no independent thought leaders) advising the government on technology investments. -- Doug Laney, VP Research, Gartner
On Facebook’s IPO and the Laws of Big Data
Hey Gil,
Great piece. Not so sure about the network effect and the applicability of Metcalfe's Law of data tho. Perhaps the challenge is with the conflated way in which most people think about "value" and who is the recipient of the value. At Gartner we ascribe three kinds of value to information 1) its realized value from actually deploying it, 2) its probable (accounting asset) value that reflects the likelihood it will be used, and 3) its potential value if it were deployed across all relevant business processes. We do this to help clients understand and measure the value gap between their underutilized "dark" data and what its information value potential is. Yes, I have developed models to quantify all this.
For Facebook, its realized value of data is the $3.7B of revenue, well because all they do is sell data (more or less). They're a pure info-based business. But it's information's probable value (in accounting terms) is the gap between its recognized $7B in assets and its investor-expected ability to monetize this info (i.e. its current $63B market cap), or $56B. Who knows for sure whether FB will actually be able to monetize its data more or less than this.
The WSJ published a piece of mine pre-IPO in which I discussed this and computed the value of an account at $81. (WSJ-To Facebook You're Worth $80.95). Interestingly, researchers at the University of Vienna saw this and contacted me that they'd done a study showing that users threatened with FB deleting their data, value it at $12 on average. So yes, a user's data has more value to FB and to other users than to the user him/herself.
But this isn't a network effect at all. A network effect says the value of a phone (or one's FB account) grows the more other people have a phone (or a FB account). The data is produced by using the account (like a phone call). An odd thing about data is that the cumulative realized value of a piece of data grows the more its used/shared/copied, but the data's market value diminishes because it has become less scarce.
Ultimately in your argument I think you're first using data in the singular (piece of data) and then in the plural (data set), which somewhat confuses things. So yes, the aggregate realized value from a piece of data grows the more it is shared, but its value to FB or to any given consumer of it is actually reduced. That is, I'd pay less for data that everyone else already has.
In short, we all need to be careful when we use words like "value" (which kind of value and to whom?) and "data" (a single piece of data, all of its copies, or a data set including that piece of data?) in the same breath.
Keep up the great ideas and writing!
And for more on the topic of information economics, or "infonomics", see recent pieces also in Forbes and the Financial Times:
Forbes - Infonomics-The Practice of Information Economics
Financial Times interview - Extracting Value from Information (free registration req'd)
Cheers,
Doug Laney, VP Research, Gartner, @doug_laney
On Big Data Goes Real-Time
Fun piece! Great to see IBM and the rest of the industry adopting the "Vs" of Big Data that Gartner originated over a dozen years ago (See: http://goo.gl/wH3qG). Note however that while "veracity" and other V's that people have posited recently are important, we do not believe they are definitional when it comes to Big Data. They apply to all data and are mutually exclusive of Big Data. As clever as additional "V's" are (and as much as it enables vendors to forgo citing Gartner's original research), the level of data's veracity has no bearing whatsoever on whether it is in the realm of Big Data or not.
Note also that Gartner, as part of our original research into what we call "infonomics" (the economics of information), has developed valuation models that enable organizations to quantify the contribution of individual information assets to financial or other goals. (Search Gartner.com for "infonomics" to find related research or visit the Wikipedia page on Infonomics for more resources and articles.)
Cheers,
Doug Laney, VP Research, Gartner
@doug_laney