when big data is too big - yet not big enough
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Today’s business and IT leaders have enormous amounts of data, but either don’t know how to or have too few means to channel it into meaningful decisions. This is certainly the case with Big Data.TRANSCRIPT
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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