Up Next: BI on Social Networks - InformationWeek
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11/30/2008
09:03 PM
Seth Grimes
Seth Grimes
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Up Next: BI on Social Networks

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.

It's time for the BI community to treat social networks as the business-intelligence resource they are. (BI is more than reports, dashboards, and OLAP!) The recent "Motrin moms" clamor and response to Mumbai terrorism prove networks' value. Both cases involved twitter, the first as a conduit for advertising-prompted outrage and the second for early and rapid news dissemination. It has become clear that twitter and the rest of a broad set of social networks &mdash as messaging / blogging / microblogging channels and as a means of publishing and finding personal and corporate information — hold immense business 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.Social networks are composed of individuals and interconnections. The value of the networks derives in part from the intentionality of the connections: that connected individuals are somehow related. The relationships mean that user-originated, peer-filtered narrowcasts can propagate more effectively than broadcasts as well as faster and wider than the nearest, non-automated analogue I can think of, a phone tree.

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.

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