Big data is used to refer to high volume, high velocity and high variety data. It consists of such vast and complex sets of raw data that traditional data processing software, applications and tools prove ineffective in converting in to useful information. The internet nowadays is home to a ginormous swamp of data which corporations and governmental institutions alike, along with other agencies, are interested in (Brown, Chui, & Manyika, 2011). However, given this large quantity, it is important to separate the relevant from the less meaningful data, and this creates the need for entities capable of processing big data and filtering it to the advantage of commercial units. Such is the importance of getting valuable and timely insight nowadays that the exploitation of big data is considered a crucial component in the chances of success of a firm (Lynch, 2008).
Investment in big data however does come with significant risks which participants must account for. A recent report published by Transparency Market Research indicates that the global big data market is expected to grow at an average annual rate of 40.5% from the years 2012 to 2018 (PR Newswire, 2014a; 2014b). Seemingly great news on the outset, it does have some important connotations. Given the affordable rates of internet subscriptions, service providers do not necessarily have the cash to invest in their network bandwidth. This creates the possibility of speed deficiencies, network overload and collapse as the upsurge in the availability of big data will not be matched by requisite bandwidth to support it. Without the necessary ancillary services present, revenues of the big data industry could be affected.