Technology: Text Analytics for Reducing Data Overload

Researchers in the area of content analytics or text analytics are building new tools to help make sense of the ever growing amount of data being created.  The focus of text analytics is on being able to unlock information that is buried in unstructured data.

Many companies have untapped treasure troves of information that have until recently been too difficult to unlock.  But that is changing.  Text analytics provides tools that help to identify trends among unstructured data sets.

Google, for example, is avid user of data mining and text analytics.  They use it to try to offer better and more competitive services to their advertisers.  The more that Google can identify a web page and match it to an advertiser’s product, the more likely that a click-through to the advertiser’s web page will result.

In the area of customer feedback, text analytics is being more frequently applied.  Businesses often collect unstructured text in emails, online chat sessions, social media and networking sites, and customer feedback comments.  Businesses can use text analytics to better respond to input that their customers are giving them.

IBM’s BigSheets project applied to the text of archives managed by the British library is another example where text analtyics is being applied to help analyze huge amounts of data.

In a survey taken in August 2009, 47 percent of the users of text analytics say that they are capturing data from blogs and social networks; 44 percent say they are applying it to news articles; 36 percent say they are using it with email messages; and 34 percent are using it for customer/marketing surveys.

Seth Grimes, industry analyst and Conference Chair of the 6th Annual Text Analytics Summit commented on the many hopes and possible applications of text analytics technology.   Grimes said that test analytics “solutions have become a linchpin for successful enterprise feedback, media analysis, claims and fraud, and customer satisfaction initiatives, while long-standing text analytics users in intelligence and the life sciences are relying on the technology more than ever. The technology is being built into e-discovery and listening platforms, law-enforcement IT, BI suites, data-mining workbenches, search interfaces, and the Semantic Web. There is a spectrum of applications, reflecting the business value that text analytics delivers.”