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On Big Data Buzz or Big Data Fuzz?

Thanks for recognizing Gartner's thought leadership on this topic, Ben. In fact the 3Vs definition dates back to the late 90's at META Group (now Gartner) and a piece I wrote in 2001: (ref: http://goo.gl/wH3qG). It has been pretty clear since then and Mark Beyer and I at Gartner reclarified it a couple years ago in our "Big Data: A Definition" piece. We at Gartner shy away from using it as a buzzword and keep to our clear definition.

Also note that the term "Big Data" seems to originate with Silicon Graphics engineer John Mashey around the mid-90s', in a presentation he was giving on "Big Data and the Next Wave of Infrastress" (that you can still find on the interwebs somewhere).

Cheers,
Doug

--Doug Laney, VP Research, Gartner, @doug_laney

 

April 16, 2015    View Comment    

On Big Data Is Really Dead

Something with 125% growth is "dead"?  Seriously?  This anonymous article isn't worthy of comment, but as the creator of the 3Vs a decade and a half ago I'll happily address Daniel's question:

Yes, Gartner has identified and published on a dozen different qualities of data including those you mention. But only velocity, volume and variety are definitional characteristics of big data. Only these three V's are characteristics of magnitute, i.e. "biggness". Others over the years have "cleverly" (?) added other Vs to my original framework (perhaps to avoid crediting Gartner with it?), unwittingly confounding the concept of magnitude.

For my original piece on the 3Vs from 14 years ago, see http://goo.gl/wH3qG. For a humorous perspective on what Batman and others think of additional "V"s, see: http://blogs.gartner.com/doug-laney/batman-on-big-data/.

--Doug Laney, VP Research, Gartner, @doug_laney 

April 6, 2015    View Comment    

On The Biggest Contradiction of Big Data

Oh, and it's great to see others catching on to Gartner's "3Vs" of Big Data, that we first introduced over 15 years ago (albeit most often without the professional courtesy of a citation). To see the original piece I wrote back then: http://goo.gl/wH3qG.

Cheers, Doug

March 23, 2015    View Comment    

On The Biggest Contradiction of Big Data

Hi Martyn,

At Gartner, I have compiled a library of several hundred real-world Big Data related examples from organizations in every industry--most with quantified business benefits. I continue to add strories each week. Gartner clients can request a sample of these use cases related to their business, or any type of data (e.g. social media, IoT, etc.)  or business function (e.g., finance, marketing, etc.) they're interested in by submitting a "written request inquiry" via inquiry@gartner.com or their Gartner rep. I'll be presenting dozens of them at our BI Summit in Vegas next week.

--Doug Laney, VP Research, Gartner, @doug_laney

March 23, 2015    View Comment    

On Big Data and Data Science: Is This Hype?

Daniel, Thank you for recognizing Gartner's/my original 3Vs from over 12yrs ago. For future reference, you and your readers might like to see the my 2001 piece first positing them:Three Dimensional Data Challenges. However, I'm confused why you suggest there should be objective indicators for each dimension. Just to keep people from "abusing" the "big data" term? Who really cares? It's both a relative and moving target: I.e. one organization's big data is another organization's not-so-big data. And next year, this year's big data may not seem so big. Gartner's updated definition recognizes this in suggesting that you have big data when "...new innovative forms of processing" are required.  That's all that really matters--when your current infrastructure components can no longer handle a jump in your data's volume, velocity and/or variety. Otherwise, I totally agree that big data has just entered it's golden run. Cheers, Doug Laney, VP Research, Gartner, @doug_laney 

September 23, 2013    View Comment    

On How Data Hoarding Is Costing Your Business

Great piece Ephriam! Assessing which information is qualitatively deemed "important" is just the beginning however. Gartner is working with clients to quantify the actual *economic value* of their information assets using models we have developed as part of our infonomics research. We gauge the gap between retention costs, realized value and probable future economic benefits to make well-informed retention, management and usage decisions. Similarly, thought leaders at IBM incl Deidre Paknad are proponents of "defensible disposal" using quantified methods to determine information retention. For more on infonomics, including links to articles and research: http://en.wikipedia.org/wiki/Infonomics.  --Doug Laney, VP Research, Gartner, @doug_laney 

September 20, 2013    View Comment    

On What Really Is Big Data? And Why It Will Change the World

Nice piece. Looking forward to future posts. Great to see the industry adopting Gartner's original "V"s of bigdata, albeit 12+ years since I first posited them (http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-construct-for-big-data/) 

Note also that Veracity was added by others trying to be clever or avoid the professional courtesy of proper attribution. Unfortunately, Veracity, Value and other recently suggested V's are not definitional characteristics of bigdata. Value is a goal and Veracity unfortunately is inversely related to the actual "bigness" characteristics. Conflating them only confuses people--as Seth Grimes pointed out in his recent piece. 

Cheers, Doug Laney, VP Research, Gartner. @doug_laney

August 28, 2013    View Comment    

On The Viability of Big Data [INFOGRAPHIC]

Great infographic and data. However, we need to be cautious about attributing new "V"s to Big Data that are not definitional. It just confounds and conflates its meaning. Cool tho to see the industry *finally* adopting Gartner's original "3Vs" framework for Big Data that we introduced over 12 years ago in the piece I wrote on the "3-D Data Management Challenge" (ref: http://goo.gl/wH3qG). Would be great however to receive the professional courtesy of a proper citation. --Doug Laney, VP Research, Gartner, @doug_laney 

July 30, 2013    View Comment    

On Five Factors to Consider for Your Big Data Initiative

Always pleased when people use Gartner's original "V"s from 12 years ago for defining big data, but more pleased when they're both cited and used properly. Here's the piece I authored on "The Three Dimensional Data Challenge" back in 2001 first introducing them: http://goo.gl/wH3qG. The 3Vs (volume, velocity, varity) were meant to be characteristic of Big Data. Other "V"s that people have glommed on, e.g. veracity (actually an inverse attribute of Big Data), value, variability, velociraptor or whatever are *not* definitional and merely confuse matters. They may be important aspects of *all* information (as are the 12 dimensions Gartner later introduced), but should not be used to conflate the definition of Big Data. --Doug Laney, VP Research, Gartner, @doug_laney 

July 15, 2013    View Comment    

On Statistics vs. Data Science vs. BI

Interesting to see your perspective on these similar roles David. At Gartner last year we used a bit of data science (rather than our own ruminations) to settle the debate. We text mined hundreds of job postings for data scientists, statisticians and BI analysts to discover the similarities and differences in what actual companies are looking for in the skills, qualifications and responsibilities of these roles. For my blog on this: http://blogs.gartner.com/doug-laney/defining-and-differentiating-the-role-of-the-data-scientist/. For Gartner clients who want to see the in-depth analysis: http://www.gartner.com/document/1955615 ("Emerging Role of the Data Scientist and the Art of Data Science"). --Doug Laney, VP Research, Gartner, @doug_laney 

May 28, 2013    View Comment    

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

April 11, 2013    View Comment    

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

January 14, 2013    View Comment