A few years ago, in great frustration at the unfettered and meaningless usage of the term “Cloud Computing ” I wrote a short essay called “Clouds in my Coffee. ” Googling (or Binging, take your pick) around, I had found no credible definition of the term. Even Wikipedia’s definition was… cloudy. Today, I’m finding the same frustration around the term “Big Data. ” The term is getting tossed around like a football, used to describe everything from video analytics to merchandise planning.

However, there’s a difference. I actually understand what Big Data is. But the definition just doesn’t roll off the tongue easily. It’s a bit geeky actually, and so it’s a bit hard for the layman to understand.

First of all, it remains my contention that retail has always had big data. All of us have pushed mountains of data around since the advent of POS. In fact, Walmart had the world’s largest data warehouse long before the words “Big Data ” were dreamed up by some analyst or another. There were two challenges: finding hardware fast enough to present the data in a quick and actionable way, and getting retailers to pull their heads out of the detail weeds long enough to actually look at it.

So we’ve got the hardware now, and we’ve probably got enough of a cultural change going that we might even treat the analytic output as actionable.

But I think the more interesting part of Big Data comes out of the consumerization of IT. We’ve got a whole new dimension of data called “customer sentiment. ” I referred to this dimension of data in my piece on Merchandise Planning a few weeks ago. That data is new. And it’s daunting. And retailers have thus far mostly tried to manage it using the squeaky wheel philosophy. Monitor the data coming out of social networks and reviews, and respond to those who are most irritable or upset. That’s also a long-standing retailer habit: Squeaky wheel syndrome. I can remember sitting at a retailer board meeting (not going to name names here) where a board member actually talked about his wife’s observations upon entering our store. And taking it as true customer sentiment, and expecting us to actually change our stores based on her observations. Never mind that she wasn’t in our target demographic, never mind that it was a majority of one: we were supposed to just.do.it.

What would big data look like in this case? It would be the aggregation of all that “noise ” out of social media, reviews, emails and site comments. This is no small trick. It requires a Natural Language Processor to parse the unstructured data and assign it a structured data element (see… it’s already getting geeky) and then associate it with a product, location or promotion. Finally, it must be integrated into a merchandising or marketing hierarchy somewhere, and be aggregated into some kind of exception analysis. You know I had zero luck explaining this to Time magazine, and I knew it as it flew off my tongue.

So, let’s try it this way with a new definition from my partner, Mr. Brian Kilcourse: ‘Big data’ is a lot of granular, usually unaggregated and often non-transactional and unstructured data that would – if we could analyze it – provide new insights that are useful to the business. In a way, the buzz-term ‘big data’ is perfectly accurate – it’s a really big collection of bits of information that up until recently, retailers tended to ignore or throw away outright. But now that retailers need to understand not only what consumers buy, but how and perhaps even why they buy it, this stuff becomes useful. And fortunately, there are actually technologies available that help retailers get answers to those questions.

Brian and I wrote a prospective piece on this a couple of years ago, before Hana and Exadata were even on the market and it led us to our 2011 Business Intelligence benchmark report, called “The Intelligent Retailer’s World of Insight. “

Each of us, as retailers, has to ask ourselves, “What can I really do with Big Data? Am I willing to take the actions it might recommend? Who should own it? Who can act on it? And how do we manage the iterative results of the actions we take? “

So the stepwise approach to Big Data is:

1. Define what it means to your company
2. Define who’s going to consume it
3. Figure out how you’re going to aggregate it
4. Propose a pilot project to try it
5. Execute on the pilot
6. Repeat

This is our point of view. We’re just not ready to spend two more years on another empty buzzword. Let’s get clear now!