Safety, efficacy, speed and costs must all be prioritized and balanced in the delivery of life-changing therapies to patients. A drug that's quickly and cost-efficiently delivered to market, but isn't effective and safe is unacceptable. An effective, safe drug that doesn't get to patients in time to save lives has failed those who needed it most. When it comes to patient health and safety, there can be no compromises. Fortunately, in a world with abundant data and advanced analytics, we have more tools than ever before to optimize this balance for the betterment of patient safety and outcomes.

But given the exponential increase in complaints year over year, how can the CFPB not only keep up, but help more people with the wealth of data they have? For instance, is there a way for them to quantitatively assess the data for various trends? Is there a better way to discover the areas of greatest concern for consumers and help address those problems on a macro-level, before they become unmanageable? "Adding more readers for a manual analysis of the text is not the answer," says SAS' Tom Sabo, who has explored the problem at length. "First, unless very specific standards are adopted, the method that one reader uses to address and tag a complaint can be quite different from the method a second reader uses. Scale this difference up to many readers, and you have many different, qualitative interpretations of the textual data."

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."