Big data tools can help analyze and find value in large data sets generated by social media.
As businesses dive into social networking, the amount of information exchanged grows exponentially. That data offers a robust foundation for research--the only problem is the sheer mass involved makes it almost unmanageable. People trying to uncover the value of social data are finding help from analytics tools used for big data analysis.
Case in point: In a quest to gain an edge, Wall Street traders and brokers are using socially aware big data analytics programs to monitor and decode the words, opinions, rants, and even keyboard-generated smiley faces posted on social-media sites like Twitter.com, Facebook, and Linked-In.
For those on Wall Street, the insights gained from analyzing large amounts of social media data can offer a competitive edge. Here, analysis can determine the communal mindset of consumers and their general feelings towards the market--the biggest element being consumer confidence.
Online surveillance of social-networking sites is an emerging technology and is proving to be a must-have tool for hedge funds, big banks, high-frequency traders, and black-box investment firms that run money via computer programs. The goal: to gather market intelligence from previously untapped sources.
Wall Street is not the only place benefiting from applying big data analytics to social media, vertical markets are benefiting as well--take for example the wine industry, where a social networking analytics tool known as Cruvee is being used to analyze some 1.5 million daily conversations about wine.
Cruvee proves to be a good example of how social media monitoring vendors are meeting the specific challenges of analyzing social data. For example, Cruvee uses complex algorithms that can identify the "language of wine", where "cab sauv" written on a Twitter feed actually means cabernet sauvignon. It is the design of the algorithms that make the analytics possible.
Of course, the same concept applies to more than just the wine market. The proprietary tools used on Wall Street are able to judge public sentiment about brands. If those posting to a social networking site are happy with a brand, that could indicate the brand is growing in popularity.
The trend of using social media analysis as part of business intelligence is growing. Multiple vendors are coming to the market with tools that are customizable and can be used to service both vertical and broad markets. Some of those vendors have developed robust platforms that take most of the pain and complexity out of social networking analytics.
Founded in 2006, Radian6 offers a suite of products that mine and interpret social media data, and companies are using Radian6 dashboards to monitor trends and tune marketing strategies. Collective Intellect started out by providing social monitoring and analysis to financial firms, but has grown to include monitoring and analysis services for the Fortune 5000. The company offers self-serve client dashboards, which allows customers to tune the analytics for their own needs.
Another competitor is Sysomos, which offers Heartbeat, a real-time monitoring and measurement tool that provides constantly updated snapshots of social media conversations delivered using a variety of user-friendly graphics. Heartbeat organizes conversations, manages workflow, facilitates collaboration, and provides ways to engage with key influencers.
At the 2011 InformationWeek 500 Conference, C-level executives from leading global companies will gather to discuss how their organizations are turbo-charging business execution and growth--how their accelerated enterprises manage cash more effectively, invest more wisely, delight customers more consistently, manage risk more profitably. The conference will feature a range of keynote, panel, and workshop sessions. St. Regis Monarch Beach, Calif., Sept. 11-13. Find out more and register.
InformationWeek Tech Digest, Nov. 10, 2014Just 30% of respondents to our new survey say their companies are very or extremely effective at identifying critical data and analyzing it to make decisions, down from 42% in 2013. What gives?