When Big Data is Too Big–Yet Not

Big Enough ©2014 TransVoyant LLC. All rights reserved.

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The proverbial cowboy—all hat, no cattle—has been flipped over

in the data world.

Today’s business and IT leaders have enormous amounts of data, but either don’t know how to or

have few too means to channel it into meaningful decisions.

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This is certainly the case with Big Data.

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j  Over the past several years, corporations have spent

billions to store exabytes (1021 bytes) of structured and

unstructured data.

Gartner  predicts  that  the  cumulate  Big  Data  spend  for  the  period  between  2011  and  2016  will  reach  over  $236  billion.    [Source:  Predicts  2014:  Big  Data  (Gartner,  Nov.  20,  2013)]  

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While proper storage was both a natural evolution in the Big Data

market and remains a precondition of its use, storage

can never be an end itself, as it is too often positioned.

www.transvoyant.com wIn this respect, Big Data has become TOO BIG in the minds

of many.

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So when is Big Data not yet big enough?

The answer is simple: when it doesn’t address your

business problems.

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According to the renowned AT&T Bell Telephone

Laboratories statistician John Tukey, “Data may not

contain the answer…

the coordination of some data and an aching desire for

an answer will not ensure that a reasonable one can be extracted from a given body

of data.”

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While Tukey (1915-2000) is correct in an important respect—one should not overvalue data per se—he was never exposed to the field of predictive analytics as it

exists today.

Today’s corporate decision makers have learned that their data must be analyzed so that

they can infer how that data creates actionable business

intelligence.

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This is no easy feat, and it will prove impossible for

companies that do not apply the rules and algorithms of predictive analytics against

their disparate data sources and streams.

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Even then, C-suite support for those business (not IT) goals

enabled by predictive analytics is critical.

Without it, the very gold nuggets found in its data risks traditional

internal fragmentation.

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According  to  Gartner,  through  2017,  90%  of  informaLon  assets  from  Big  Data  analyLcs  will  be  ‘siloed  and  unleverageable’  across  mulLple  business  processes.  We  say  no  company  can  afford  to  leave  it  there.      [Source:  Predicts  2014:  Big  Data  (Gartner,  Nov.  20,  2013)]  

In this respect, Big Data is not big

enough.

Companies must leverage one of their most critical assets as a

core part of their analytical efforts, not just as a supplement, to gain advantage over lagging

competitors.

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It cannot be emphasized enough that business line executives—

not data scientists—

need to man this helm.

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McKinsey writes that “sophisticated analytics solutions . . . must be embedded in frontline tools so

simple that business managers and frontline employees will be eager to

use them daily.”

[Source: Mobilizing Your C-Suite For Big Data Analytics (McKinsey & Co. 2013)]

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The National Academy of Sciences has referred non-judgmentally to these employees as “naïve users” for the

purpose of impressing upon corporations the need for Big Data to

be a business line imperative only supported by IT.

[Source: Massive Data Analysis (National Academy of Sciences 2013)]

So is Big Data both too big and too

little?

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Yes—it merely depends on the problem.

The “too big” problem (storage) has effectively been solved.

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The “too little” dilemma—“What can we do with all our data

streams?”—is far more critical for corporations to address with the help of core platforms that create

derived intelligence and knowledge in real time.

TransVoyant, with its long record of predictive analytics in

the military and intelligence communities,

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is now working with corporations across industries to answer with

predictive analytics some of the most important questions its

users face:

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“What is happening in our company?”

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“What is going to happen next?”

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“Where and When?”

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“What do we do with this intelligence?”

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Any company that neglects these questions renders

irrelevant the issue of too big or too little.

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It has already eliminated itself from the competition.

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