Hear a panel of experts discuss the buzz about measuring buzz and the data analysis technologies behind it at the Enterprise 2.0 Boston conference.
Big data and social media analytics are a couple of the biggest buzzwords in information technology today, and next week we'll be mashing them up in a presentation I'm moderating.
My goal for this session at Enterprise 2.0 in Boston, a UBM TechWeb event, is to make sure attendees understand these technologies and how to put them to work.
A lot of the buzz about social media analytics is around monitoring buzz--mentions of your company or your brand, along with sentiment analysis to determine whether those mentions are positive, or negative, or maybe a question about your product. But it's also analyzing the social networks for how people are connected to each other, who they talk with most, and who is most influential or knowledgeable.
Big Data is just a term for managing really enormous, Internet-scale quantities of data that you can't load into a traditional relational database--because the database would explode. It's not just the volume of data, but the fact that it's not neatly organized into rows and columns. Typically, it needs to be parsed and organized, or maybe even run through an advanced natural language processing routine, before you can make sense of it.
Social media is one of the most frequently cited examples of the need for that kind of analysis. If you want to parse all the posts on Twitter, you're going to need to run that data through something that like Hadoop, which is an open source project loosely based on the technologies Google uses to harness thousands of PC class servers, working in parallel, to analyze Web content.
I'm really pleased with the quality of the panel for this session. Zach Hofer-Shall from Forrester Research calls the kind of analysis I'm talking about Social Intelligence, and he is one of the people I go to for his knowledge of the vendors in this market.
I first spoke with David Gutelius in April when his company Proximal Labs was acquired by Jive Software. Proximal had been in existence for less than a year at that point, but Jive snapped it up largely for the team of Big Data analytics experts he had pulled together. Some of David's ideas about how to map social networks grew out of research he had done for DARPA on analyzing military communications. I was fascinated by this--one of the military applications in Iraq was analyzing communications between leaders in the field to determine who had the most knowledge and experiencing countering new types of improvised explosive devices being used against our soldiers. That's a life saving example of using social analytics to spread critical knowledge.
Michael Wu is principal scientist at Lithium Technology, which hired him straight out of academia at UC Berkeley. He earned a triple major in applied math, physics, and molecular and cell biology and before joining Lithium in 2008, he was a research scholar at Berkeley studying things like mathematical modeling of the behavior of neurons. These days, he is thinking up better ways of analyzing social networks, looking for things like patterns to identify the most influential members of a community. He is also one of Lithium's deep thinkers on the social dynamics of the Web and using game mechanics to boost participation.
Gutelius and Wu both know a lot about working with Hadoop, and one of my questions for them is how much those of you who work in enterprise IT need to understand about the nature of that technology. The programming and operational models are a lot different than for traditional database development and administration, so you'll have to bring some new skills into the organization if you want to run your own Hadoop clusters.
However, you may not have to worry about that if you sign up for a hosted analytic application that handles all those complexities behind the scenes. Even if you want to do custom analysis against the data, you may be able to accomplish that using a cloud vendor's application programming interface to access the data in a different way.
Often the customers for these social monitoring and analysis applications are marketing departments that don't care a bit about the back-end technology, as long as it delivers answers. "They may be heavily dependent on the technology, but they don't care as long as they see what they're looking for," Forrester's Hoffer-Shall said.
Arguably, the most sophisticated organizations will want to know more, if they want to get the most mileage out of the hosted services and perhaps blend them with data from enterprise systems. The IT advisory firm Gartner suggests that enterprises explore Hadoop at least as a research and development exercise--a Gartner report from January is available for download from Cloudera, which provides an enterprise distribution of Hadoop.
Hoffer-Shall said the typical social analytics team may include one or two IT people who are curious about the underlying technologies, but they don't tend to be the ones in charge. In the long run, there probably needs to be a "left brain and right brain collaboration" between IT and marketing, he said.
If you are interested in this topic, send me your questions and comments, and I'll do my best to include them in the discussion at Enterprise 2.0.
Attend Enterprise 2.0 Boston to see the latest social business tools and technologies. Register with code CPBJEB03 and save $100 off conference passes or for a free expo pass. It happens June 20-23. Find out more.
Social is a Business ImperativeThe use of social media for a host of business purposes is rising. Indeed, social is quickly moving from cutting edge to business basic. Organizations that have so far ignored social - either because they thought it was a passing fad or just didnít have the resources to properly evaluate potential use cases and products - must start giving it serious consideration.
Social is a Business ImperativeSocial media is critical in the age of digital business. How can IT help? First, work with the marketing team to set up social networking programs on Facebook, Twitter, and LinkedIn, at minimum. Then work to put social media sentiment analytics in place to measure success.