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SAS Text Analytics


With 75% of all data is unstructured and not in relational databases, at best most companies are making decisions on only 25% of available information.

Unstructured text data is trapped in letters, call center logs, emails, and reports. Human review just cannot keep up with reading all the information coming into organisations today - there needs to be a better way. Using Text Analytics, we can determine what is in unstructured text, regardless of source or format. These tools automatically detect keywords, and then use sophisticated linguistic rules to evaluate text - just as human minds do. But it does it consistently and without any personal bias of the reader.

Text analytics generates rules to improve queries – improving the quality and relevancy of data over time.

SAS Text Analytics enhances numerical analytics by providing more context. You save time, improve efficiency, and automate efforts around reading, tagging and indexing. All this leads to more informed decisions with the confidence of knowing it is based on all the available information.

So how is this information being used. Three common uses are:

  1. Detecting future product failures by analysing past warranty claims
  2. Predicting when customers are about the leave by understanding consumer sentiment.
  3. Using insight from text analytics is in predictive models and forecasting integrates the peripheral signals that were previously too obscure to recognise in a timely manner.

Two good advantages of SAS Text Analytics are the customisable, prebuilt methods for text analytics and the open architecture - combined, this gives users complete control over implementing the tool.

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