by Gil Allouche
Big data, big data, big data. These words seem to find their way into every business conversation, article or forum. Almost every industry wants to take advantage of big data, and to be honest, it’s the right decision. Long story short, the ability to use large quantities of data to learn important insights and build predictive models will greatly improve the way organizations function.
However, big data analytics aren’t easy. Businesses can’t just boot up Microsoft Excel, make a few graphs, and call it good. Infact, traditional spreadsheets and analytical software is nowhere near capable of managing and processing big data. There is just way too much information. In order to compensate, a number of programs, like Apache Spark, have been created to help manage the massive amounts of data and offer real-time analytic power.
It may be a bitter pill to swallow, but computers can do many things faster and more efficiently than humans can. That shouldn’t come as a surprise. However, there is one area in particular that humans have the upper hand, and that’s language. Our brains are trained to recognize speech patterns and understand meaning at speeds that dwarf computers. In fact, computers don’t even have the capacity (yet) to understand the depth of language. Computers understand and process unambiguous and structured information, whereas language is the direct opposite of that. It’s filled with emotion, implied intent and semantics. It draws off of prior references and is unique to each culture, with words of the same language taking on different meanings. And sometimes what we don’t say is even more important than what we do.
This presents a significant problem. As developed as big data analytics have become, they still really only excel at managing structured information. But not all data comes as numbers There’s still much to be learned from emails, phone conversations and dialogue on social media. Of course, where there is a need, there are those looking to build a solution.
There are lots of companies and entrepreneurs looking to create Natural Language Processing (NLP) solutions and teach computers how to better understand human communication. NLP involves building machines or programs that have the ability to understand and derive meaning from both written and verbal communication. This shouldn’t sound completely foreign, because NLP software is already involved in our day-to-day lives. It’s this technology that allows us to speak with Siri or tell our cars to change the radio station.
There is a significant benefit to be had from merging NLP with data analysis. Being able to accurately analyze increasing amounts of unstructured data, like emails, text messages or voice calls would lead to more accurate insights into human behavior, especially when combined with other structured data. For example, there is tremendous potential in using this type of technology to improve the success of sales calls. Managers could monitor sales calls and learn the right variables, whether words, or tones, that help predict whether or not a call will close with a sale. That information can then be used in future calls to improve the overall success rate.
There’s also a significant demand for improved language capabilities in the healthcare industry. Analyzing healthcare information could help us diagnose symptoms quicker or provide better treatment. However, healthcare information is more than just numbers in a system. There are other very important documents, like doctor notes, which are often still handwritten. NLP could help in finding a way to categorize this information into structured databases, which could then be analyzed quickly, like the numeric elements of big data.
Needless to say, there are lots of different applications for combining NLP with big data analytics. Experts predict that we’ll be communicating with computers like Captain Kirk within a few short years. That remains to be seen, but if we can find a way to track and analyze language in a similar way to how we track other form of data, we’ll open the doors to more detailed insights and better predictive models.
