Big data is a big buzz word, but holds little meaning if you don’t know what to do with it.  In today’s technology culture, we’re all about data, from the tweets we send to the videos we stream.  And every piece of data holds intrinsic information about us, our preferances, and our intent.  Determining how to manage, unravel and comprehend all of that data is the next big problem many organizations are trying to solve.

While there’s a growing ecosystem around storing and processing data, there’s another market emerging around understanding it all.  This goes beyond filtering and cataloging, to uncover patterns embedded in the data itself.  The interesting thing is that these data represent our human interactions, whether we like it or not.  Our gaining ability in processing around these patterns puts us in a new position of comprehensive calculations.

Viralheat is one of the startups digging in the data to identify our human intent.  The big data analytics company has found itself a vital business tool in these days of inferred market research, where all one has to do to acquire a sample study is go to the web and run a few data sets.  Recently funded with $4.25 million in its first round of funding, Viralheat is now looking to further build out its automated tools to help businesses determine consumers’ intent around their products and services.  With the deluge of data across social media, applications and beyond, even brands are overwhelmed with the amount of content they need to process.  A more efficient tool is needed.

Parsing out the intent of something like a tweet means analyzing massive amounts of unstructured data (see here for a brief recap on the differences between structured and unstructured data and content).  Viralheat’s system looks to see which tweets look like support requests, and which look like a customer on the verge of buying a product.  To make the process more efficient, Viralheat auto-classifies this content.  Integrated directly with CRM systems, and businesses have a much easier time of finding what they need, and subsequently taking action.

From here, Viralheat is working on more ways to analyze data.  Its clients are demanding more data, too.  Requesting more points of integration across social sites, finding the actual correlations between traffic on sites like LinkedIn, and points of social interaction.  It’s going to be a challenge for Viralheat, because adding more data means more complexities in the system.  But the team recognized the potential demand from the beginning, building in a support system early on.

“We decided early on to build everything in-house.  In a way there’s already challenges with storage, real-time search and near-real-time analytics following sentiment,” explains Vishal Sankhla, CTO and Co-Founder of Viralheat.  “You’re getting data from Facebook, YouTube–the biggest networks out there.  So far we’ve been able to do a good job of scaling horozontially, from our storage to our search.  We can add more nodes easily, and once nodes are stored, we can run analytics on top of this.  It’s very elastic internally.”

Another area Viralheat is looking to build out its its customer support software, enabling brands to reach out and help end users.  It certainly aids in things like customer retiontion, and ties in with some larger trends we’re seeing around big data analytics and business intelligence.  ClickFox is a company that knows the importance of customer service first hand–it’s built a company around understanding consumer behavior for the betterment of the customer service industry, landing QBE First as its latest client.