At the analyst firm's recent Innovation in Action summit, SMA founder Deb Smallwood admitted that one of the five emerging technologies the Boston-company is keeping its eye on is actually two techniques: big data and analytics. And with good reason: according to the company's research, there is as much money being spent directly by biz units on data and analytics as there is by just the IT department.
Later in the program, Smallwood's colleague Mark Breading posited that there are five "V" words that companies should think about when considering big data initiatives. Three are well-known, but two are less illustrious -- at least for now.
First is volume: "What we're talking about with big data is not just lots of data, we're talking about huge amounts -- petabytes -- of data, and doing analytics on full data sets, not just samples," Breading says.
Then there's variety: "That's lots of different kinds of data both structured and unstructured," he adds.
Last of the well-known "V"s is velocity: "This is probably the biggest 'V' in terms of driving investment," Breading explains. "You can't afford to take a long time to build and run models, then tweak them. We need to be able to run these iterations within a day -- or, sometimes, minutes."
Now we're getting into the lesser-known terms. First of those is veracity: "There is this notion with big data that you can use dirty data, it doesn't have to be perfect because we're just going for speed," Breading says. "There is some truth there, but the data still has to represent the underlying truth. I have a concept I call 'good enough' data -- you don't have to cleanse everything until it's perfect, but it has to be good enough so that you can gain some insights."
Last but not least -- and probably not unexpected -- is value. Breading says this boils down to answering one question: "Why do we want to implement this technology?"