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The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance. If they have text, like notes, in the rest of that structured data…we can incorporate that additional free form information for actionable insight."

The world of big data analytics is incredibly diverse, and people are coming up with new analytic tools and techniques every day. But one particularly productive combination that should not be overlooked involves the use of text analytics and machine learning. Tom Sabo, principal solutions architect at analytics giant SAS, says the one-two punch of predictive modeling on structured data, and text mining with unstructured data, can deliver insights that are more than the sum of their analytic parts. "They really run side by side," Sabo tells Datanami. "Let's say somebody has predictive models in place against whether customer will churn or to maximize profit, for instance. If they have text, like notes, in the rest of that structured data…we can incorporate that additional free form information for actionable insight."