Characteristics of Big Data

Volume: The benefit gained from the ability to process large amounts of information is the main attraction of big data analytics. Having more data beats out having better models: simple bits of math can be unreasonably effective given large amounts of data. If you could run that forecast taking into account 300 factors rather than 6, could you predict demand better?

Speed: The importance of data is velocity — the increasing rate at which data flows into an organization — has followed a similar pattern to that of volume. Problems previously restricted to segments of industry are now presenting themselves in a much broader setting. In the past, specialized companies such as financial traders have long turned systems that cope with fast moving data to their advantage. Now, the Internet and mobile era means that the way we deliver and consume products and services is increasingly instrumented, generating a data flow back to the provider. Online retailers are able to compile large histories of customers' every click and interaction: not just the final sales. Those who are able to quickly utilize that information, by recommending additional purchases, for instance, gain competitive advantage.

Variety: Rarely does data present itself in a form perfectly ordered and ready for processing. A common theme in big data systems is that the source data is diverse, and doesn't fall into neat relational structures. It could be text from social networks, image data, a raw feed directly from a sensor source. None of these things come ready for integration into an application. Even on the web, where computer-to-computer communication ought to bring some guarantees, the reality of data is messy.

(Forbes, 2012)

A bit of: used to make a statement or description less forceful or definite

Compile: to build up gradually

Neat: precise, systematic