Research reveals that the global natural language processing (NLP) market size is projected to grow by $33 billion within the next five years, an annual growth rate of 25%. 

The use of natural language processing for search capability is ubiquitous – from the Google-type “ask a question and get an answer” environment consumers love to the augmented analytics solution that provides an NLP environment to make it easy for non-technical business users to perform data analytics, using the tried-and-true question/answer approach, without the need for data science or IT skills. 

“Clickless” analytics ensures that your business users can achieve results and gain insight into issues, challenges, opportunities, and the root cause of problems and results – all without the need for time-consuming, complicated analytics. 

It’s simple, really! Natural language processing-based search capability allows business users with average technical skills to gather and analyze data without the need to scroll through menus or write complex queries. The user simply asks a question such as “Who sold the most baked goods in the Southwest in April of 2019?” and the system translates the query and returns the results in natural language, providing those results in an appropriate form, e.g., tables, numbers, visualization, or written descriptions. 

Natural language processing search analytics is a crucial component of search analytics and smart data discovery today. NLP search allows business users to create complex searches without endless clicks and complex navigation and commands. Using this type of search analytics, users can access and view clear, concise answers and analysis quickly and easily.

Natural language processing (NLP) search analytics allows for advanced data discovery, leveraging sophisticated algorithms and analytical techniques, using machine learning and providing results in a self-serve environment that ensures swift user adoption and requires no complex training. Users who are familiar with existing online search engines and techniques can easily transition to augmented analytics and use the same search techniques in real-world business use cases. 

If your business wishes to democratize data and improve data literacy and fact-based decision-making, it is wise to choose an augmented analytics solution that is based on natural language processing (NLP) search capability and will make it as easy as possible for your business users to adopt these analytics tools and use them to gain insight into data, make accurate predictions, and plan with confidence.