Understanding Big Data
Jinfo Blog

By Victor Camlek

Abstract

In the first of his series of three articles introducing and defining big data and its importance for the information professional, Victor Camlek posits the notion of the "Four Vs" as they apply to big data and explores how the big data phenomenon came into being.

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Although it sounds simple enough and the descriptor of "big data" is one of the least technical terms one can imagine, many of us may find the concept of big data somewhat elusive to fully grasp.

Introducing Big Data's Four Vs

Based on reading through many articles and reports about the subject I believe there are some basic ways to describe the concept without getting too bogged down in technical jargon. Similar to the four or more "Ps" often used to define marketing, many writers are using two or more "Vs" to describe the impact of big data. My view is based on a four V formula including: Volume, Velocity, Vaulting, and Validation.

Perhaps confusingly, the term big data does not describe a solution. Rather, unlike terms such as "video" or "wireless" or "Bluetooth" that define technical or commonly-used terms that represent things or solutions, big data describes the problem.

In the simplest terms big data as a phenomenon has arrived due to advances in technology, largely driven by technological gains in data networking, data compression, storage and data retrieval - we live within a society or business enterprise capable of transmitting and collecting more data than ever before. The keywords in this last sentence are "transmitting and collecting". Big data is multimodal and there is so much data flying around from all parties involved in commerce that it has become increasingly difficult to manage the storage, analysis, and flow of this vital information.

We Interrogate Our Data Every Day

Whilst in the "old days" we collected data associated with business transactions and stored this information within appropriate departmental databases, we are now in a much more robust data environment that has daily applications. Data is needed to help sustain or reset business models. It is needed to drive innovation and it is needed to assess customers.

In the evolving world, all parties involved in commerce are collecting and transmitting data that is now being integrated across an enterprise and analysed to drive decision making and action.

There's a Record of Every Action

We live in a world of data trails. Literally every click of a mouse or tap on a smart device; every walk down a street in a major city; every ride in a smart or networked car; literally everything we do is transmitting data that is tracked by computers, visible and hidden cameras and discreet recording systems. 

Also, we cannot afford to forget that the problem will get worse as "The Internet of Things" extends to more and more currently disconnected objects. The Internet of Things stands for the ability to connect commonplace objects to the internet.  Everything from the chair we sit on to the kitchen pantry or shelves in a supermarket are poised to become data transmitters. I find the potential of the Internet of Things a staggering concept that will serve to increase the challenge of big data literally everywhere we turn.

Given this background, in my next article I'll try to define the challenges posed by big data, using the "Four Vs" to create a multi-dimensional view of big data.

Editor's Note: Big Data in Action

This article is part of the FreePint Topic Series: Big Data in Action, which includes articles, reports, webinars and resources published between April and June 2013. Learn more about the series here.

Articles in series:

• Understanding Big Data
• Defining Big Data: The Four Vs
• Big Data and the Enterprise Information Professional

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