Though social networks may soon contain the visible sum of humanity’s communication and interaction, the challenges of deriving what is increasingly called social business intelligence are two-fold.
First, big data sets itself apart from previous approaches because it applies new ways of thinking about the capture, storage, and processing of truly vast amounts of data, precisely the kind that emanates from today’s social media ecosystems. This includes the supporting technology, often starting with emerging tech such as data mining grids or MapReduce infrastructures (see my exploration of one example, Hadoop, here) as well as software architecture that is often surprisingly non-deterministic and non-linear in design. For a quick example, see this discussion of LinkedIn’s challenges and counter intuitive solutions to data scale in social networks.
In practice, this means that there is a distinct generational and technical divide between how most organizations are dealing with data today, and the very different methods they’ll be employing in the future.
Continued in: How Social Media and Big Data Will Unleash What We Know | ZDNet
I, For One, Welcome our New Social Data Overlords | Collaboratory
Web Squared Emerges To Refine Web 2.0 | On Web Strategy