Lori Wright.jpgBig data can be a big disappointment. As TIBCO CMO Lori Wright explained, it's hard to find the value in complicated, ginormous and ever-growing masses of information about everything from "weather to social sentiment."

So maybe it's time to start thinking about something else — something that may be a more meaningful piece of the data-fueled puzzle. "Fast data," she said.

In this information intensive, hyper-connected, data-driven world — where right time business insight can separate the movers from the losers — speed thrills.

"How do you find the data you need at the moment it’s needed? You speed it up," Wright told CMSWire. "Fast Data that’s filtered and delivered to the right place as quickly as new information arrives solves the (slow) big data problem."

Breaking Big Data into Pieces

The best definition of big data I ever heard was simple and more accurate than any other description. "It’s the stuff we used to call 'a whole lotta data,' " said a presenter at a conference I attended a few years ago.

He wasn't joking. Big data attained its status as an epic buzzword before anyone bothered to agree on a definition. So what is it anyway?

You've probably heard of the three big data V's -- volume, velocity and variety. And now there are fourth, fifth and potentially more V's, too, like veracity and value.

More often than not, we focus on volume. Yep, big data is big. Or is it?

Bruno Aziza, CMO at Alpine Data Labs, a San Francisco-based provider of advanced analytics software, thinks otherwise. In a blog post titled "Why I don’t buy the hype about 'big data'," he argued that big data does not necessarily have to be big.

Rather, he wrote, big data needs to be refined and redefined. "We need to approach big data differently, and design solutions that allow smaller companies [to] take advantage of this opportunity," he wrote.

What if, instead of focusing of the proverbial 3 V’s (velocity, volume and variety), we tried something like this: 'Big data is a subjective state that describes the situation a company finds itself in when its infrastructure can’t keep pace with its data needs.'"

What if we stopped worrying less about size (aka volume) and more about speed (velocity)? And that brings us back to what Wright wants business to do.

Now, Now, Now

To extract real value from big data, businesses have to do more than capture it: they need to continuously process and analyze that data in real time to gain instant awareness and take instant action. Wright's not the first to say that.

Fast data is a priority at plenty of companies, including Oracle and IBM. Forrester defines it as "Any business intelligence, metrics or other operational data that is updated from the source continuously, or at the very most, every few minutes." Or, more simply, "really urgent analytics."

And as early as two years ago, global analyst firm Ovum acknowledged fast data had gone mainstream.

In fact, you could describe capitalizing on real time data as an almost universal goal of everyone from marketers to analytics software vendors.