Everyone is getting it on with Big Data.

And when I say everyone, I mean everyone. From the Mad (Ad) Men on Madison Avenue to the Obama Administration to Cyber Criminals, Investment Bankers, Insurers, Health Care Providers, Biotechs and even Celebjuicer, a website that tracks conversations between celebrities on Twitter.

Entrepreneurs looking to hang glittery shingles and to reel in loads of cash are being told that a Big Data-related service is a good bet suggests a recent article in Inc. Magazine.

After all, Big Data is hot, and if everybody’s doing it, it can’t be that difficult a concept to wrap your head around, right?

Well, that depends on who you are and on how you define Big Data.

Here a Definition, There a Definition

O’Reilly Radar’s Alistair Croll offers a somewhat sarcastic example of how most of us (not including CMSWire readers, of course) think of Big Data:

  • Everything is on the Internet.
  • The Internet has a lot of data.
  • Therefore, everything is big data.

It’s a definition that we can all understand but that doesn’t mean it’s useful or correct.

Edd Dumbill who chairs O’Reilly’s Strata Conference explains Big Data this way:

Big data is data that exceeds the processing capacity of conventional database systems. The data is too big (Volume), moves too fast (Velocity), or doesn’t fit the strictures of your database architectures (Variability). To gain value from this data, you must choose an alternative way to process it."

Stanford’s Andreas Weigand offers yet another definition:

Big Data is when your data sets become so large that you have to start innovating how to collect, store, organize, analyze and share it."

It’s worth noting that large, even humongous, structured data might be better suited to traditional analytical database approaches rather than to Hadoop, (which is the most often used, and most hyped software in Big Data), notes Dumbill.

Learning Opportunities

And the converse is also true: “When you have a hammer, everything looks like a nail. When you have a Hadoop deployment, everything looks like big data, “ says Croll in a recent O’Reilly Radar post.

Looking to the Future

Though quantity or volume isn’t the only determinant factor that defines Big Data and Hadoop isn’t the cure-all for all large data sets, everyone from global enterprises to hackers to individual researchers are in awe of the raging river of data we’re creating via the Internet of Things (def. courtesy of Wikipedia: "The Internet of Things refers to uniquely identifiable objects (things) and their virtual representations in an Internet-like structure") and the insight that can be drawn from it.

At GigaOM’s inaugural Big Data conference “Stucture” held in 2011 all kinds of questions were asked around Big Data, some of which included:

Though we’re clearly still in the magical, wide-eyed stage of looking at Big Data and all that we can do with it now that information is cheap to store, easy to access, quicker to process and to glean insights from; we’ll inevitably move on to the trough of disillusionment before long.

And it’s in stepping out of that trough that we’ll discover what the Big Data hype was really all about and what kind of technologies, experts and workers we’ll need to make the most of it.

Editor's Note: Another article by Virginia Backaitis you may enjoy:

-- Data is the New Oil