7 Big Data Definitions You Need to Know

What exactly is big data? Many people may still be a bit confused, so in this article let's take a look at some of the main definitions of big data. The first thing to note is that everyone in the industry generally agrees that big data is not just more data.

(1)? The original Big Data

Big Data can be characterized in many ways, with Doug Laney pioneering the "3V" model in 2001, which includes Volume, Velocity, and Variety. Since then, many in the industry have expanded the 3Vs to 11Vs, including validity, authenticity, value, and visibility.

(2)? Big data: the technology

Why is an old term from 12 years ago suddenly being put in the spotlight? It's not just because we have more volume, velocity, and variety now than we did a decade ago. Rather, it's because big data is being fueled by new technologies, particularly fast-growing open-source technologies such as Hadoop and other NoSQL ways of storing and processing data.

Users of these new technologies needed a term to differentiate them from their predecessors, and Big Data became their best bet. If you go to a Big Data conference, you're sure to find that there will be very few sessions involving relational databases, no matter how many V's they trumpet.

(3) Big Data vs. Data

The problem with Big Data technologies is that Big Data is somewhat ambiguous, to the point where every vendor in the industry can jump in and claim that their technology is a Big Data technology. Here are two great ways to help organizations understand the difference between big data now and just big data in the past.

Transactions, interactions, and observations: this was proposed by Shaun Connolly, vice president for enterprise strategy at Hortonworks. Transactions are the primary data we used to collect, store and analyze. Interactions are data that people get from actions such as clicking on web pages. Observations are data collected automatically.

Process mediates data, human-generated information, and machine-generated data.

(4) Big data: signaling

Steve Lucas of SAP argues that the world should be divided based on intent and timing, not on the type of data. The "old world" was mostly about transactions, and by the time they were recorded, we were no longer able to do anything with them: organizations were constantly managing "defunct data". In the "new world," organizations can use new "signaling" data to predict what will happen and intervene to improve the situation.

Related examples include tracking attitudes to brands on social media, and predictive maintenance (where complex algorithms help you decide when parts need to be replaced).

(5)? Big data: the opportunity

This is Matt Aslett from 451 Research, who positions big data as "data that has previously been ignored because of technological limitations". (Although technically Matt uses the term "dark data" rather than big data, it's pretty close). This is the author's favorite definition, as it matches what is said in most articles and discussions.

(6)? Big data: a metaphor

Rick Smolan writes in his book that big data is "the process that helps generate the planet's nervous system, in which we humans are just another type of sensor". Pretty deep, right?

(7)? Big data: old wine in new bottles

Many projects are essentially using previous technologies that used to be called BI or analytics to suddenly jump on the big data bandwagon.

The bottom line: while there is a lot of debate about the definition of Big Data, everyone agrees on this fact: Big Data is a big deal and will present huge opportunities in the coming years.

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