Analytics for Human Information: New Top Ten Myths of Big Data - Myth #2

In installment two of this series (see my previous post for Myth #1) I must first make a confession: I have been just as guilty of propagating this myth as nearly everyone else in the industry.  This myth has been present in nearly all of my PowerPoint presentations, I have blogged it, tweeted it, etc...  Heck, I even believed it, at least until recently.  But as I am about to argue, my thinking about this myth has changed radically over the last six months or so.

 

Big Data Myth #2: Big Data is about Volume, Velocity and Variety of Data

Who hasn’t seen or heard this statement, regarding Big Data over the last 3-4 years?  We all have known and loved the “Three V’s” of Big Data and they are almost sacrosanct.  However, I’m going to break with tradition and propose that it’s time for us to drop these “Three V’s” and move on to a new set of Big Data drivers.

 

What is true of Volume, Velocity and Variety is that these are all input factors; they all relate to how data gets into a Big Data system.  This issue of consumption has been critical over the last few years because we simply didn’t know how to do it.  Input WAS the problem.  However, our technical capabilities are moving so quickly that we’ve reached a point of inflection.  When I can consume and digest a trillion rows of data, and not feel that this is anything special, then the three “V’s” of input really no longer matter.  Yes, we must move from petabytes to exabytes and so on, but we can and we will.

 

In contrast, I now argue that we need to consider three new “V’s” of Big Data: Veracity, Viability and Value.  These are “Output V’s” and relate to what we do with the results of Big Data.  Let’s consider them in turn.

Veracity is the measure of how believable are the results of our analysis.  Have I used the right data for the question that I have asked?  Have I processed that data correctly?  And do the results that I have received make sense?  Note that I am not talking about data cleansing.  Frankly I’m against making data “clean.”  One person’s noise is another person’s signal; it all depends upon what question you are asking. But veracity is a relevant concern in Big Data because business leaders need to know that the information that they are acting upon is reliable.  This is especially true as we deploy ever-greater numbers of real-time systems with automated logic.  So expect to see veracity becoming increasingly important.

 

Second, we have Viability, or, can I act upon the information that I’m receiving. Businesses are investing heavily in their ability to analyze data and come up with insights at lightning speeds, and yet we’re operating the rest of our business the same old way.  What could be more frustrating than knowing what my customers are doing this very second, and not being able to respond to that information in under a minute, or an hour, or a day?  Hence I expect that issues of viability, of being able to act upon the data, will come to the forefront over the next six to eighteen months.

 

Finally we come to the third Output “V”: Value.  Many companies are investing in their Big Data initiatives as if they were creating a startup.  They invest a few million dollars on infrastructure, software and expertise with no idea of how the results of these efforts will lead to value.  This era of exploring Big Data technologies is rapidly fading and now it is time to produce results.  Many companies are impatiently waiting for such results, and the clock is ticking.  Since there are dozens of companies who have figured out how to monetize their analytics efforts, the expectation of value is now “out there.”  And so this final V may be the most urgent for Big Data proponents.  It’s time to deliver measurable value to the businesses being served, before we end up too deeply in the trough of disillusionment. Find out more about HP Autonomy’s approach to this.

 

Check out New Big Data Myth #3, and I promise it definitely WON’T randomly mention #MileyCyrus in an attempt to drive page hits.

 

Click below to continue reading about The New Top Ten Myths of Big Data :

Comments
Doug Laney(anon) | ‎10-14-2013 05:01 PM

Yes, everyone's quite clever(?) lately coming up with new "Vs" to extend or supplant those Gartner (then META Group ) first posited over 12 years ago in my piece on the Three Dimensional Data Challenge (ref:  http://blogs.gartner.com/doug-laney/deja-vvvue-others-claiming-gartners-volume-velocity-variety-cons...).  However the 3Vs are meant to be definitional not aspirational. Best not to be confused or confuse others. Value, viability, veracity etc., are all valid (oops! there's another) objectives for *any* kind of data, but adhering them exclusively to Big Data does a disservice to those trying to comprehend and deal with its unique challenges and opportunities. As Seth Grimes admonished recently in Information Week piece: Don't be a "Wanna V"! (ref: http://www.informationweek.com/big-data/commentary/big-data-analytics/big-data-avoid-wanna-v-confusi...)   

 

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

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About the Author
Chris Surdak is a Subject Matter Expert on Information Governance, analytics and eDiscovery for HP Autonomy. He has over 20 years of consul...
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