Big Data: Everyone’s Favorite Term to Misunderstand
You don’t have to dig very deep to find disagreement over the term “Big Data.” It frequently appears on lists of “The Most Overused Buzzwords” or “The Most Annoying Business Jargon.” As a relative newcomer to the field of analytics, BI, and data in general, I find this tug-of-war between legitimacy and definition to be interesting. And potentially revealing.
At least one blogger makes the assertion that a lot of people are using the term “Big Data” incorrectly, insofar as when most people say “Big Data” they really mean “Analytics.” I don’t agree with all of the blogger’s claims — especially the idea that Big Data really means putting people out of work — but I think her general point withstands scrutiny. There does seem to be a disconnect between what Big Data is, and what people think it is. So the question is, Why?
Well, when in doubt, wiki out! Wikipedia offers as good a definition as any when it comes to tech and business terms. Big Data is defined as “a collection of data sets so large and complex that it becomes difficult to process [them] using on-hand database management tools or traditional data processing applications.”
In other words, Big Data is the phenomenon of data collections getting so big, they end up breaking the tools traditionally designed to interact with them. At first glance, it does not appear that the major disagreement is on the term. What seems to be creating the divide is the process of placing the relevant disciplines inside the Big Data space.
What is interesting here is the shift in terminology. Bloggers like the one I quoted above seem to be using the term “Big Data” as a descriptor of a state of affairs. This stands in sharp contrast to how a layperson might understand the term, where they grasp it as a thing. You know, “Big Data” just means a whole bunch of data, right? We cannot fault someone for making this assumption. If we do, we are as guilty as anyone else for infusing jargon and buzzwords with more meaning than they actually have. I’ve written on this elsewhere, but it bears repeating here: just because you can use jargon all the time does not mean that you should. “Big Data” is industry jargon, but it is also a phrase in the English language that, barring any other information, a regular person is going to feel perfectly comfortable using incorrectly.
I guess the best way to describe this is to use an analogy. If I tell people I work in “Healthcare,” I am talking about a state (in this case an industry), not necessarily a tangible thing. After all, healthcare is a huge industry with many facets. I could be a worker in the “healthcare industry” and never touch a single medical instrument because I work in billing, or social services, or human resources. But a person unfamiliar with the concept of healthcare as an industry might assume I am a doctor, that I do healthcare as a specific thing, that I stitch up wounds and prescribe medicines. They would be wrong, but it would not be their fault.
We face the same issue with terms like “Big Data.” As a writer, I am always concerned with the clarity and functionality of language; one of the biggest pet peeves of any technical writer is having to swim through dense layers of jargon, buzzwords and acronyms to translate some high tech stuff into easy reading. That is part of the job, sure, and it’s why we are here. But it also serves as a reminder of how impenetrable some of these terms can be to an outsider.
And for Big Data, that can be a problem. Companies that are interested in selling their products or providing their services in the Big Data arena need to realize that beating people over the head with the jargon stick is doing more harm than good. It is very easy to get wrapped up in your own terms, and very easy to forget that your audience may have no idea what you’re talking about. Or worse, they may get the wrong idea!
The solution is education. Anyone in the business of selling services in the data industry should be ready, willing and eager to explain even the most basic concepts to a potential customer. Companies must consider how things like overly complicated web content or poorly written social media campaigns can have a customer’s eyes glazing over with boredom. And finally, they should not be in a the business of hoarding this information. The best way to promote your Big Data solutions is to promote the industry as a whole. To do that, your approach needs to be relatable. Otherwise your customers may end up assuming that “Big Data” simply refers to a really large computer.