Automated Text Mining Solution Captures Value from Unstructured Text DataAutomated Text Mining Solution Captures Value from Unstructured Text Data

Genalytics, a provider of advanced analytics software, has announced the availability of its new Knowledge Extract module. Coupled with Genalytics' advanced analytics platform, the new module is designed to help organizations make better customer targeting and risk management decisions by mining various sources of unstructured data, extracting relevant metadata and meaning, and then putting the results into a structured format. In turn, this new structured data can enhance the performance of any enterprise analytics, business intelligence, or search engine application, dramatically improving the accuracy of organizations' marketing or risk programs.

Genalytics points out that valuable information about customer behavior is increasingly stored as unstructured data in call centers, customer relationship management (CRM) systems, news feeds, PowerPoint presentations, Excel spreadsheets, email, and PDF documents. The challenge is that this data is not easily accessible by traditional business intelligence and analytics applications. As a result, many organizations struggle to employ this data as actionable business intelligence that can be used to build effective marketing and risk assessment programs.

"In a world where over 80 percent of all enterprise data is unstructured, organizations are continuously seeking ways to leverage this information to improve bottom line results," said Ray Kingman, CEO of Genalytics. "Knowledge Extract helps marketers capture the value in unstructured data which often lies dormant in call center or CRM systems. This new product enables marketers to derive the context and meaning of data from a range of documents and systems that can be used for understanding and predicting future customer behavior."

The Knowledge Extract module is fully automated and can extract the important metadata from documents without any special tuning or configuration. It combines Genalytics' plug-and-play content acquisition engine with a "best-of-breed" concept extraction technology to recognize the vital information within a document. The module uses existing search engines, web spiders, and other tools to gather relevant content from HTML, XML, Word, and PDF formats. This new approach to text analytics also extracts important entities such as people, companies, places, and document summaries from the data.

The "magic" behind Knowledge Extract lies within its Salience Engine. The Salience Engine derives the sentiment of a document providing additional depth and insight into the meaning of the content, such as positive or negative message tone, emerging risk, and threat assessment. Knowledge Extract scores sentiment at the paragraph, sentence, and entity levels within a document to help it identify positive and negative phrases and events.

Genalytics Knowledge Extract can also be integrated with a web-based analytic portal application that can expose summary information to a wide range of users.