Transforming Business Intelligence with Natural Language Processing 

Humans rely heavily on language as a means of communication.  We humans have an innate talent for language since we are so good at deciphering the ‘context’ and meaning’ of words. However, this is not the case with digital devices.  

Both written and spoken language are reduced to character strings and auditory signals when processed by a computer. Computers are unable to separate ‘context’ from ‘content,’ unlike humans.  

In order to facilitate communication between humans and computers on an equal footing, the fields of computer science and computational linguistics have developed the theory and practice of Natural Language Processing (NLP). The fields of linguistics, machine learning, and AI came together to form NLP. The heart of natural language processing is in teaching computers to grasp the ‘context’ and, by extension, the ‘intent’ of any spoken or written message. 

Role of NLP in Business Intelligence 

It is critical that we train computers to ‘understand’ natural language because computers have become so integral to our daily lives. Natural language processing (NLP) enters the scene at this point. Here are some of the ways in which Natural Language Processing transforms Business Intelligence:  


NLP Based Research 

NLP-Based Search Search is a crucial feature of any business intelligence platform. Using natural language processing (NLP), BI search can better comprehend the user’s intent and return more relevant results. Using natural language processing, consumers can have a BI experience more akin to Google’s.  With NLP-based search, users don’t have to restate their questions because the conversation continues naturally after they submit a query. 


Enhanced Query Accuracy 

Access to insights is improved by NLP algorithms’ ability to interpret user intent and contextualize searches. This can be illustrated with the relatively straightforward question, “What are the sales figures of Lamborghini cars for the past quarter in Australia?” from a sales executive. and the natural language processing algorithm will deduce that the customer is interested in sales figures for Lamborghini automobiles meeting two criteria. The first need is timing (the preceding quarter) and the second is geography(Australia). 


Customized Data Analysis 

As a result of NLP’s ability to grasp user preferences, business intelligence platforms may now offer tailored insights and suggestions. If a sales manager were to query the business intelligence platform, “What are the top-selling products in my region?” the system would respond with specific suggestions based on the manager’s location and past sales. The needs of a manager of human resources (HR) will be different from those of a manager of sales (SM). This degree of customization in gaining insights is unique to BI platforms powered by natural language processing. 


Analyses of Context 

Because it can simulate human-like cognition in terms of language, it is a go-to option for many different BI methods. If the sales manager in the preceding scenario were to ask, “What about the worst ones?” the system would recognise this as a follow-up inquiry and assume he was interested in the least successful goods in his area. 


Unorganised Data 

Tapping into unstructured data is a significant use case for NLP in BI. And most businesses aren’t making full use of this data yet. With the growth of digital and social media and IoT enabled devices, unstructured data is expected to grow at an unprecedented rate in the future. The value of this information is locked away, but NLP allows for efficient examination of it. 


Topic Modeling 

This has several potential uses, including in areas like content analysis, trend detection, and recommendation. The media business provides a real-world example. 

To Conclude 

BI data should ideally be accessible to everyone, which is an ongoing problem. Natural language processing (NLP) has the potential to greatly aid in resolving these difficulties, boosting BI adoption rates and making data accessible to more people. 


Companies are already putting NLP to use for many purposes beyond simple text or speech processing. The market for voice assistant apps and devices has exploded, and new ones appear regularly. More and more apps and gadgets will integrate natural language processing in the next few years, dramatically improving user engagement and experience. 

In an age of abundant consumer data, more than 90 percent of it is unstructured. Our Natural Language Processing services comprehend this data, extracting actionable insights to inform the development of effective marketing strategies. Provide a gratifying customer experience by equipping your chatbot with multilingual capabilities and sentiment analysis. 

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