RSR Research: What’s So Big About Big Data?
Through a special arrangement, what follows is an excerpt of an article from Retail Paradox, RSR Research’s weekly analysis on emerging issues facing retailers, presented here for discussion.
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 around, I had found no credible definition of the term. Even Wikipedia’s definition was … cloudy. Today, the term "big data" is similarly 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 is a bit geeky and just doesn’t roll off the tongue easily.
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. 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 being enough to act on.
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." That data is new and it’s daunting. 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 talked about his wife’s observations upon entering our store. Taking it as true customer sentiment, he expected 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.
- What’s So Big about Big Data? – RSR Research
- The Intelligent Retailer’s World of Insight – RSR Research
What’s your technical as well as more basic definition of big data? How well do you think retailers understand what big data is?