Faced with the ongoing confusion over the term 'Big Data,' here's a handy - and somewhat cynical - guide to some of the key definitions that you might see out there.
Despite what Wikipedia says, most people in the industry generally agree that Big Data isn't just about having more data, but that was indeed the term's first meaning in the late 1990s (even though warnings about the exponential rise of data volumes go back until at least the 1940s).
Big Data as the three Vs: Volume, Velocity, and Variety. This is the most well-known definition, first coined by Doug Laney of Gartner over twelve years ago. Since then, many others have tried to take it to 11 with additional Vs including Validity, Veracity, Value, and Visibility.
Why did Big Data suddenly become such a widely used term? It wasn't simply because we do indeed now have a lot more volume, velocity, and variety than a decade ago. Instead, it was fueled by new technology, and in particular the fast rise of open source technologies such as Hadoop and other NoSQL ways of storing and manipulating data.
The users of these new tools needed a term that differentiated them from previous technologies, and-somehow-ended up settling on the woefully inadequate term Big Data. If you go to a big data conference, you can be assured that sessions featuring relational databases-no matter how many Vs they boast-will be in the minority.
The problem with big-data-as-technology is that (a) it's vague enough that every vendor in the industry jumped in to claim it for themselves and (b) everybody 'knew' that they were supposed to elevate the debate and talk about something more business-y and useful.
Here are two good attempts to help organizations understand why Big Data now is different from mere big data in the past:
This is another business-y approach that divides the world by intent and timing rather than the type of data, courtesy of SAP's Steve Lucas. The 'old world' is about transactions, and by the time these transactions are recorded, it's too late to do anything about them: companies are constantly 'managing out of the rear-view mirror'.
