The 'Search' feature has pretty much revolutionized the way we function today. It was on September 10th, 1990 when the first search engine with content files (specifically FTP files) made its debut. Three decades on, the search is the ubiquitous feature for a fulfilling experience on the Internet.

Specifically, enterprises have had an organized funnel to channel data, but in the times of search, they have to rework their strategies to cast their net far and wide to gather more data, realtime. This is where NLP can be a boon – for its very quality of analyzing vast amounts of unstructured data with ease.

Accenture states that NLP’s market estimate size is likely to touch $16billion this year. NLP is a core enabler of making AI very efficient for enterprises to unlock data. Primarily, content understanding and query understanding are two broad areas where enterprises can deploy NLP techniques.

Let’s see how NLP is being leveraged across enterprises:

Chatbots and Virtual Assistants:

Having a chatbot or VA has become a necessity today. 56% of CIOs and CTOs believe conversational bots are driving disruption, 57% believe infographics can deliver significant RoIs for comparatively streamlined efforts. Chatbots will drive cost savings of $8bn annually, and by 2021, 15% of customer service interactions will be handled completely by AI. Deeper neural networks can greatly enhance the quality of conversations, and enhance the nature of interactions between customers and employees. A combination of content processing, powered by NLP, can transform information portals into capable virtual assistants. This allows companies to improve business processes, enhance search optimization, and improve brand reputation.

Smart Document Analysis:

Enterprises have a huge amount of documents and inter employee correspondence constantly. Intelligent document analysis includes NLP, entity extraction, semantic understanding and ML, with core functionalities such as Optical Character Recognition (OCR), text analysis, deterministic classification and machine learning.

Document Search & Match:

The vast amounts of data an organization holds on to is one thing, not being able to retrieve the exact information at a time is whole other challenge. For functions that involve cross matching data, running multiple scenarios and simulations and the like, its imperative that data is easily available and rapidly retrievable. Advanced search techniques involving NLP and ML provide statistical and linguistic capabilities to carry out these data-heavy tasks efficiently.

Data Storage Analytics:

Another mounting challenge for enterprises is that of obsolete data. Now with every bit of data on servers, enterprises needn’t have to purchasing cloud space to continue storing old data. NLP could be utilized to help CIOs understand what kind of data sits on servers and rank its importance. This could help them decide the course ahead with this data, and explore options like low-cost storage.

Sentiment Analysis:

This is one of the most exciting aspects of data capitalization, especially for businesses that are closely tracking their social media presence and brand awareness. Also known as opinion mining, sentiment analysis offers a true test to NLP’s capabilities today. In its simplest form, sentiment analysis categorises data as either positive or negative, but with text analytics, NLP, linguistic analysis and ML, sentiment analysis can help enterprises richer value from data gathered

Insider Threat Detection

As AI is growing, so are cyber threats. Nefarious activities online don’t exist merely as an outsider threat, but can originate from within organisations too. Industry experts believe that application of AI technologies itself could mitigate data leaks and data breaches, and help control the fallback effects significantly.