While there may not be a semantic application that can go to work on every issue that requires grappling with Big Data, it certainly has a role to play in many of them. Throw out outliers such as using power grid data to optimize power distribution, and “the lion’s share of big data problems are semantic problems,” says Dachis Group CTO Erik Huddleston.

Marketing leaders are right up against those challenges – and the opportunities that can flow from meeting them, or the pain that results from missing the mark. The non-stop social media milieu, if its behaviors can be categorized and correlated to an ROI like increased brand awareness or better brand value, can be a goldmine – or if not, a cave-in.

Building its social business intelligence-as-a-service platform over the last year with the help of a significant investment from Austin Ventures, Dachis Group aims to help deliver the Eureka! moments, so that companies can discover via social media data if their marketing strategies are working.

“We make that link from behavior to metric to business outcome,” says Huddleston. “We use semantic analytic technologies to allow us to follow those breadcrumbs, from where a link is posted on Twitter to the [end result of a] brand being more valuable,” as an example. Think of it, he says, as using the same (big) data set that a listening platform would rely on, but with the idea instead of analyzing the conversations marketers do know about vs. alerting them to those they might be missing – of looking at the places where they engage with their market to understand how well they are performing and the impact that engagement is having.

The first lens on its social business intelligence capability was the introduction last week of its Social Business Index. This provides a view into the adoption and performance of the world’s leading social businesses, some thousands of them representing about 22,000 brands. Dachis Group analyzed their social engagement across the major social media platforms, blogs, and the like, to the tune of interpreting conversations on about 100 million social acts.

“By analyzing that we identified the specific behaviors, best practices or tactics employed by those companies,” Huddleston says. It used benchmarking algorithms to compare those and build comparative social business index scores rolled up by industry and then rolled into a composite number that Huddleston says really represents the adoption of social business today. Right now the Index only is available privately to social media practitioners and corporate strategy owners at organizationsit covers, but any organization can apply to be part of it here. There will be a public version of the index providing rankings of the world’s largest, most social organizations soon, and down the road premium paid analytic applications built on top of that data will be delivered.

Social Business Intelligence: Insight Into Marketing ROI

Social business intelligence must span various fronts, from adoption to operationalization, to optimization, where the number one problem in social media is how to tie business outcomes to the activities an organization is executing. As Huddleston describes it, the technology behind its first lens on social business intelligence – and which will also drive its upcoming explorations of social business – involves building a set of ontologies, a catalog of behaviors, metrics and measures that are categorized; performing natural language processing on the signals it harvests – tweets, Facebook wall posts, YouTube video comments and such; normalizing this so each signal can be talked about in the same way as another signal; and running it all through an enrichment process for metadata annotation.

“We stream together the conversations that are taking place, then do NLP to understand what topics are being discussed. We use what we call our Hivemind algorithm where we can analyze the topical variants between a company and its audience, so you can start to see topics propagate through the social graph,” he says.“We build a representation of that social graph, so we understand every company, where they engage online, who they engage with, who the influencers are for their brand, who employees and partners and customers are, and then we watch the content as it gets ingested and we do semantic analysis of the content,” Huddleston says.

It’s not just analyzing the conversations around the brand but also analyzing the brand’s impact on popular culture that represents its value. That is, for example, are people now not just talking about Old Spice, but also tweeting as if they’re the (new) Old Spice Man themselves?

“Deep analysis can be democratized through social business platforms,” he says – no longer does it have to be the exclusive domain “of agency augers or elites like Nielsen.” But with all the data that’s out there, this isn’t necessarily an easy play. “The infrastructure and expertise required is mind numbing,” Huddleston says. Expertise from an analytics standpoint and a Big Data standpoint doesn’t necessarily exist in the average corporate IT department.”So we’re really looking at the domain of SaaS,” he says.

And the next big challenge is just the access and scale of information, he says. “The hurdles to getting there are pretty staggering. But on the flip side a bunch of companies that understand the opportunity behind semantic analysis of Big Data, in particular of social data, all are biting off different corners of the problem, like social business intelligence or listening.”

For Huddleston, it’s an exciting time to be where Dachis Group is. “Everyone talks of the inevitabilyt of social and every business turns into a social business,” he says. With its technology and its new Index, “we have a unique view to watch the adoption over time.”