A business application doesn’t need to be social to contribute social insights
When I talk with folks about social analytics I tend to start with the reasons why social analytics is not the same as analytics on social media and then rapidly move onto the reasons why social analytics needs to be considered as more than just social, or at least social as it narrowly applies to social applications. Companies have always been social and the only difference is that we now have software that supports and simplifies the process of communicating and collaborating, and explicitly codifies the information and connections derived from those interactions. Information that is currently under-utilized in my opinion, but we will come to that later.
If we look at the academic definition of social analytics “the process of measuring, analyzing and interpreting the results of interactions and associations among people, concepts, and facts” and apply this more broadly to the business, then a couple of things happen. Firstly we start to be able to harvest actionable social insights from existing enterprise applications, secondly we create a bridge that allows us to marry legacy business solutions with the new generation of social business platform, and thirdly we significantly increase the ROI we can realize from our social investment.
So how can we apply social analytics to a legacy business application which is not a “social application” per sei? Firstly its important to appreciate that these applications hide a rich set of social and semantic links within their underlying schemas. They may be well hidden and distributed across a myriad of tables and cells, but they are there nonetheless and can be represented as a socio-semantic graph on which social analytics can be easily applied.
With a flexible crawling infrastructure, such as a seedlist framework, you can translate the relationships implicit in the database schema into the appropriate semantics and make them available for indexing. You then need a social indexer that is powerful enough to index both the entities and relationhips and provides the required social network analytics on the resulting graph. Ideally, this is the same indexer that you us within your Social Business Platform which means you now have the seemless integration. All sounds good so far :-)
Now lets finish with a concrete example to validate this hypothesis, or at least to give it some credence. Over the next few weeks I will post a number of other scenarios to further demonstrate the broad applicability of this approach. How about a sales scenario? Here we would have a sales database that tracks customer interactions associated with the opportunity. It would contain records that link (perhaps partially in some cases, so there may be some gap filling required):
Opportunities -> Products
Opportunities -> Clients
Clients -> Industry
Opportunities -> Meetings
Meetings -> People
People -> Documents
Documents -> Topics (if missing in schema, provided by indexer)
As you can see, you are now rapidly building out a social graph that is painting a clear picture of the people interactions within the enterprise as it relates to the sales process, very useful information in ascertaining expertise for example. And on that note, I will end this post reiterating the message that Social Analytics is way too much fun to be limited to social applications!!!!