By Sean Gelles | @SeanGelles | Director, Social Media Marketing
Once again “Big Data” is all the rage as enterprises struggle to cope with the data deluge that is exploding across the globe. To provide an illustration of this phenomenon, consider first that, according to Google’s Eric Schmidt, there were five exabytes of data created between the dawn of civilization and 2003 (one exabyte is equivalent to one million terabytes). Now consider that, according to Cisco, by 2016 global IP traffic will reach 109.5 exabytes per month. Companies, organizations, and governments are all drowning in data and the bulk of what’s contributing to this raging flood is user-generated content.
The tsunami of user-generated content has generated an urgent demand for more sophisticated analytical tools in the social media space. This rising demand is bringing the worlds of big data solutions and social media services (i.e. CRM, marketing, and sales) closer together than ever before. In the past two months the software behemoth Oracle acquired the social media marketing platform Virtue and Salesforce acquired Buddy Media. These two will certainly not be the last of such moves. In the coming months, other vendors such as IBM, EMC, and HP will likely make similar acquisitions. As a consequence, social media specialists, especially analytics experts, will need to become much more data-technology-savvy.
The merging of big data solutions and social media marketing platforms is not the only development that will require social media analytics experts to elevate their technology competency. Increasingly the rudimentary manual analytical methodologies of the past are proving inadequate for fully harnessing the power of Big Data to provide strategic insights for social media marketing campaign planning and measurement as well as social CRM, crowd sourcing and sales. At present, the most common approach to analyzing social media data is manual and protracted. It usually starts with a listening tool, such as Radian6, to gather the data followed by dozens of hours of labor-intensive data cleaning. The resulting dataset only yields insights about the relevant conversation – the major topics, the primary channels where the conversation is taking place and the identities of the individuals engaged in the conversation. Understanding the structure of the social network constituted by the relevant conversation requires additional analysis.