Each network member can be classified: by current and past employment, by demographic characteristics (age, sex, location, etc.), and by interests and sub-community (group) memberships. With the right access to network-utilization data, members can be clustered by behaviors such as type, frequency, and timing of network use, propensity to forward or respond to information and requests, and so on. From this type of meta-information, it should be possible to understand which individuals are thought leaders and which are connectors, who will act on network messages and who will ignore them, and even how the various networks — noone belongs to only one social network — interdepend. On the latter point, observe that the emergent anti-Motrin-ad "campaign" would have had less reach and impact if the ad video hadn't been posted to YouTube.
The networks' potential business value seems almost obvious, yet traditional BI, reliant on spreadsheets, reports, and pivot tables, has concerned itself almost exclusively with slicing and dicing numerical data extracted from transactional and operational systems. Data and text mining allow us to extend BI to new sources and to a new form of fact, to relationships and their development over time. On this basis, I've been expecting the emergence of broad-market tools that will help us see the business value of connectedness. Perhaps the Motrin and Mumbai-news examples will finally, convincingly make the case by illustrating the value that social-network BI, including the understanding of connectedness, can deliver.It's time for the BI community to treat social networks as the business-intelligence resource they are. The recent "Motrin moms" clamor and response to Mumbai terrorism prove networks' value. The value of the information that flows through these networks is indisputable. A deeper challenge is next on the agenda: optimizing that flow by better understanding the networks themselves.