Sifting Through Social Data

dataI attended an executive salon event hosted by one of the global marketing agencies in Minneapolis.  The theme of the presentations focused on the roles of aggregating social data for big data analysis.   Two concepts jumps out during the conversation that drove most of the post presentation discussions.

The first was the topic of cross linking social accounts with corporate CRM systems.    This was starting to expand a big data picture of the customer.   While this is common practice today,  they extended the concept to include cross liking with other identity sources such as LinkedIn or Public Tax Records.   This started to create a vastly larger profile set of individuals outside of your customer base.   These larger sets of profiles could be used to identify trends and patterns that could be leveraged for approach and enticing new customers to your brand or new offerings.

The second topic built on the first but was much more elaborate.   They had some guest speakers from new ISVs that where building tools for markets to access a massive big data pool that had been assembling.   Several years prior they had launched a backend platform that was constantly listening and recording many social media channels.   The platform would be analyzing the content and generating additional meta data and tagging of content to aid in ongoing analysis.  An elaborate architecture of meta tag hierarchies where defined to provide categorization of subject matters.  Even more impressive was the ontologies that where defined between the hierarchies to cross relate topics.   The end result in the analysis seemed to be an enormous multiplier in the ability to cross-relate cross channel data and inter-relate thematic trends and insights.   Since seeing this demo I’ve noticed several new companies building out these types of solutions.   While the science side of the platforms to do this is fairly straight forward it is the Art of creating the inter-relationships of the ontologies across the hierarchies that will define the state-of-the-art of competitive analysis.