Text Mining Analytics

 
 
By Mary Grace Crissey  |  Posted 2009-05-18 Email Print this article Print
 
 
 
 
 
 
 


Component No. 2: Text mining analytics

Text mining analytics technology is a result of advances in natural linguistic processing and the pervasive adoption of data mining on traditional structured data. Text mining software finds explicit relationships and discovers associations between documents.

Unstructured, free-form textual content can now be clustered by grouping the data into categories using a variety of semantic tools. These knowledge-extraction technologies can handle a huge variety of document formats in dozens of languages, and they run on sophisticated, text-parsing capabilities to transform data into a compact, information-rich structure for interactive exploration of concepts and relationships between terms and documents.

Organizations that already have predictive analytical models for their customer relationship management initiatives are finding additional gains when they add text analytics to integrate the unstructured textual data with structured data. Leading organizations are implementing these emerging technologies to find the value hidden in document collections. Those with a unified business analytics framework see improvement in the overall accuracy of their predictive models.

Component No. 3: Voice mining your own business

Sometimes, when we are faced with urgent demands, the last thing we want to do is consider investing in analytical technologies. Unfortunately, if there is no quick manner to communicate the expected return on investment or customer intelligence insights to your superiors-with the authority to act and reap the gains from those analytical insights-the door will remain closed.

The best way to overcome this obstacle is to secure access to analytic experts at the same time you address any voice mining software purchases. A trained analytical expert will ensure you not only "see" insights, but actually move on them and get the value out of predictive analytic workbenches.

One way to efficiently and effectively transfer the value is by customizing a performance dashboard or tailoring a user interface that decision makers are already familiar with. Only then will your voice mining implementation actually hear and comprehend in real time what your customers are saying.



 
 
 
 
Mary Grace Crissey is the Analytics Marketing Manager at SAS, where she applies her Master's of Science degree in Operations Research to show how text analytics, optimization and data mining can solve real world challenges. After 20 years as a military scientific officer, she continues to serve in leadership roles with professional societies including Knowledge Discovery and Data Mining (ACM/KDD) and the Institute of Operations Research and the Management Sciences (INFORMS). She can be reached at MaryGrace.Crissey@sas.com.
 
 
 
 
 
 
 

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