Streaming Text Analytics: Finding value in real-time events


As technology and analytics continue to evolve, we're seeing new opportunities not only in the way that we analyze data, but also in deployment options. More specifically, real-time deployment of analytical algorithms that enable organizations to detect and respond to security threats, offer timely incentives to customers, and mitigate risk by detecting compliance or safety risks...all in real-time.

Text analytics is utilized in varying ways across organizations. At a high level, text analytics may involve:

  • Identification of data-driven topics and clusters across collections of text.
  • Automatic categorization of textual data to tag categories and sub-categories.
  • Extracting entities (such as name, currencies, ID numbers, company names, or complex facts). This may involve simple keyword tagging, or more advanced matching based on regular expressions, taxonomies, linguistic/NLP patterns, or a combination of these in order to extract information.
  • Sentiment analysis, which is used to understand the polarity of a comment at the document level as well as the category/feature level.
  • ...and more depending on the analytical maturity and business needs of the organization.

In many organizations, these algorithms are applied against historical data in batch mode. Depending on the business requirements, this may be exactly what is needed. But for others, real-time scoring opens up new opportunities and creates additional value for the organization and their customers.

So what is SAS Event Stream Processing? This technology enables organizations to integrate business logic, pattern matching, and statistical algorithms/predictive models against real-time data streams. This data may come from operational transactions, server or network logs, call centers conversations, sensors, or a variety of other sources.

Here are a few use cases where customers have seen value by integrating text analytics with event stream processing technology:

Voice of the Customer

Monitoring customer contact channelsESP_Text1 in real-time enables organizations to quickly identify emerging trends, respond to customer concerns, and escalate critical issues as they occur.​​ Today, many organization analyze call center notes long after the call has ended. I've seen examples where compliance issues and high-value customer complaints have gone undetected or the event was detected too late to be of any value.

E-Surveillance and Fraud DetectionESP_Text

Monitoring both internal and external communication is valuable (and sometimes required) within organizations. In regulated environments, communications around insider trading, collusion, and other fraudulent events can cause reputation and financial damage. Undetected, these events can have huge implications, but just as important, a delayed response can bury the information and further complicate the investigation.

Compliance and Safety

In many industries, early detection of adverse events and safety issues can save millions. This information comes in many forms, standard customer complaints, internal communications, and maybe even social media to name a few. When it comes to safety, real-time response is not only critical, but a delayed response is drastically devalued or worse yet, has no value at all.

The top 3 sections and use cases are just a few, but will hopefully help you in identifying areas beneficial to your organization. Ultimately, the areas listed below are where real-time analytics is critical and where organizations can expect to see significant value and ROI:

  • Safety (Safety of patients or customers.)
  • Security (Security around cyber threats, reputation threats, etc.)
  • Personalization (We've seen over a 20% increase in customer acceptance rates when the message is timed appropriately. This is applicable in call center and marketing settings within organizations.)
  • Risk (Across organizations various types of risk need to be responded to and acted upon immediately.)

Within your organization, where do you see text analytics and event stream processing creating value and opening up new opportunites?

To learn more about SAS' Text Analytics technology, visit SAS Contextual Analysis, and SAS Event Stream Processing.


About Author

Dan Zaratsian

Sr. Solutions Architect

Dan Zaratsian is a Sr. Solutions Architect with SAS' Global Analytics Practice, specializing in real-time event stream processing, text analytics, and machine learning. He works with a variety of technologies, both open-source and enterprise software, in order to design, program, and implement analytical solutions for clients. Dan holds a M.S. in Analytics from North Carolina State's Institute for Advanced Analytics and a Bachelor’s degree in Electrical Engineering from the University of Akron.

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