Analytics is indisputably one of the most important mechanisms for maintaining a competitive edge in today's technologically advanced marketplace. Unfortunately, any analytical process is only as complete as the data from which it is derived—and this data is only accessible when it is in a useable format. Historically, converting unstructured text into analyzable data has proven to be a challenge. Yet for the enterprise, the results are likely to be well worth the investment.

The case for analyzing text is strong

According to Rebecca Wettemann of Nucleus Research, in her joint paper with SPSS on 'The Real Benefits from Text Mining', "Text mining can help companies leverage all the unstructured information they have about products, services, competitors, and customers to increase customer satisfaction and loyalty." Even in 2008 when Nucleus explored a number of case studies on the topic, organizations were achieving well-documented reductions in churn rate, improved productivity, greater ROI for marketing, and faster R&D because of the insights gained from text mining. Today, the volume and variety of unstructured text sources that can be mined for information are greater than ever—and the tools to configure and analyze this data are maturing as well.

How is the business world linking tagging to analytics?

According to James Niehaus, VP of Analytics & Digital Strategy at Ensighten, tag management software is most commonly integrated with web analytics solutions such as Google Analytics, Adobe SiteCatalyst, IBM Coremetrics, and Web Trends. Optimization systems like Monetate and Optimizely are also common BI tools that use tagged data as enterprises seek to derive greater direct ROI from their unstructured text. The analysis itself is fed back into further experimentation with multivirate testing (MVT) to uncover even more information about user behavior. Tagging has been slotted fairly seamlessly into the workflow because the concept is simple enough to work with many BI systems.

Is there a limit to the information that should be gleaned from unstructured text?

While text mining is designed to bring new information to light, sometimes it can uncover information that's best left unseen. In these situations, determining what to leave out is as important as knowing what to display. For example, appropriate tagging can actually help protect private information

On a larger scale, even with the wealth of information hidden in the flow of Big Data, not every detail is relevant. Knowing what to ignore is just as important as knowing what to inspect. As tagging becomes the norm, businesses will be faced with new questions about when enough is enough. Perhaps a day will arrive when even the tags themselves will need to be tagged for relevance.

Source: www.theserverside.com