OK Google! Hey Alexa! Say those magical phrases and your device comes to life just waiting for your next spoken command or question. Communicatingwith devices in this non-mechanicalway is every bit revolutionary and Natural Language Processing (NLP) is at the center of it, propelling this futuristic drive towards a seamless means of communicating with machines.
NLP blends machine learning, linguistics, andAI to bridge the divide between us and the machines.Its revolutionary extent, however, is not just limited to commands like, “turn on the lights” or google like responses like, “what’s the weather today?” NLP holds the key to unlockinga diverse set of opportunities, especially in the analyticsecosphere.
Expanding the borders of big data
As the world evolves and devices become more connected, there’s bound to be a commensurate increase in the type and volume of data… duh. What’s not so apparent is that while this will result in even more raw material for today’s analytical frameworks to churn through a good chunk of these datasets will be at out of reach for conventional analytic systems. IBM Watson, for instance,considered to be a forerunner of sentiment analysis can only decipher a limited handful of sentiments and emotions ingrained in data. Naturally,human emotions are far more complex and Watson is ‘blind’ to more subtle but important emotions. NLP promises to be the missing link between machinesand thehumanworld. By sensitizing the machine environments to otherwise alien data sets, NLP unravels a massive repository of unexplored multimedia content for analytical frameworks. A feat that will ultimately translate to a broader and more widespread reach for the analytics ecosphere.
Insights will get more ‘insightful.’
Currently,in most analytical frameworks NLP is primarily concernedwith interfacing naturallanguage to present information in an understandable format to machines and vice versa. For instance, if I ask my analytics system, “What are sales this year?”, NLP can interpret and translate the question into structured language that will be then be sent to the analytical system for processing. The same would occur for the system results when they need to be communicated back to the end user. In the near future, however, NLP will confer on analytic frameworks the innate ability to actively sort, manage and process insights into more relevant analyses based on the input fed into the analytical framework. In other words, rather than just provide a generic analysisfor a specified input, NLP will help analytical frameworks understand the underlying intent to produce more ‘natural’ or relatable results. The end goal will be to derive an analysis that is more contextually relevant than was possible before.
Parsing unstructured data
‘Alien’ data sets will become a thing of the past as NLP will also grant analytical systems the ability to derive additional insights from these otherwise ‘unusable’ or unstructured datasets. The implications arebig. Analysis of unstructured data (in tandem with structured data) allows for the development ofmore in-depthinsights. As an example, analyzing data derived from emails, text or voice calls, can help in profiling the unique behavioral patterns of a particular group of people. For sales and marketing outfits, this will mean the ability to programmatically funnel offers to target consumers. Or seen from an opposite vantage point, to funnel the best opportunities to the sales reps more likely to close the deal based off personality fit and past sales performance.
This unique use case of NLP highlights just how excitingthe future of NLP is, with respect toits role in the analytics ecosystem. As long as progress is being made in thesector, there’s no arguing the fact that there’s this and more to come courtesy of NLP.
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