NLP-Making Business Intelligence Easier!

Humans use languages to convey information and meaning, and we are basically good at understanding and analysing languages as how the globe looks at it. We accomplish this through semantic prompts, which can be words, images or signs that provide a more direct link to the thing they represent in the real world.

When most people see text, they understand what it means. When machines recognise text, they only see character strings, no context or understanding of how that text relates to a person’s intent.

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As people become more reliant on computing systems, the ability of computers to understand text and language becomes increasingly important. This is where natural language processing (NLP) enters the picture. Machine learning and artificial intelligence are expanding fields, and natural language processing is making it easier to communicate between computers and humans.

A complicated sequence of high-end code is activated whenever you ask your Google assistant about nearby restaurants. This sequence allows your assistant to understand your question and provide you with the desired response.

We used to interact with computers in a way that they could understand; we had learned their language. But now they understand what we’re saying?

All of this is facilitated by the use of Natural Language Processing (NLP solutions). And this is already transforming the business world at a breakneck pace.

In this highly competitive world of business, the current technique for analysing customer actions appears to be out of date. It is necessary to comprehend the customer’s preferences and moods. As technology advances, the application of NLP will become more user-friendly, serving as a road map to the future of business.

Today’s businesses must be as adaptable and flexible as possible in response to market changes.

NLP and business intelligence have a direct relationship. The use of NLP can make BI-based data more accessible. Natural language interfaces have the potential to transform our interactions with complex systems that use databases and large datasets.

For large corporations, it appears to be a way to connect non-technical people with the data they require to make critical decisions. The use of Natural Language Processing in business intelligence tools can make it easier for non-technologists to begin analysing data on their own rather than waiting for IT professionals to run complex reports.

Every person on the planet generates approximately 1.7MB of data per second. Purchasing a cup of coffee. I’m currently reading a blog post. A photo that you like. Consider a constant stream of ones and zeros emanating from everyone at all times. And businesses are attempting to capitalise on it. Data from businesses and customers can be used to inform inventory logistics, marketing strategies, and sales opportunities, among other things. The ability to pull and report on key metrics on an as-needed basis can significantly improve performance, efficiency, and growth. However, data comes in a variety of formats from various sources.

The current NLP strategy is focused on translating speech into machine or computer understandable language. However, there is a good chance that the emphasis will shift to making the computer understand the query and deliver meaningful responses rather than just raw search results. Soon, we will be able to receive responses in natural languages.

The most obvious benefit of questioning data with NLP is that it provides access to complicated business information through one of the most basic learned skills: natural language. It solves the shortage of data scientists by allowing people with almost no technical knowledge to access and view data.

Today, a growing number of global companies are implementing Business Intelligence Chatbots that can understand natural language and perform complex BI tasks. As a result, data consumption between business users has become much simpler. By integrating NLP-driven chatbots with your existing BI systems such as Power BI, Oracle, SAP, and others, users can access data using natural language.

Businesses will no longer have to spend resources and time learning how to ask a data query. Natural Language Processing enables users, irrespective of technical skill to execute complex queries by simply asking a question in plain English.

The use of unstructured data is an important application of NLP in business intelligence. According to IDC, by 2025, 80 percent of global data will be unstructured. And the majority of this data is still being underutilised by businesses. Unstructured data is expected to grow at an unprecedented rate in the coming years as a result of the data explosion from digital and social media, as well as IoT-enabled devices. NLP aids in the effective analysis of this data, unlocking its value.

Search is an essential feature of any BI system. NLP improves BI search by deciphering the intent behind user queries and displaying highly relevant results. Users can get a Google-type and consumerized BI experience with NLP. NLP-based search advances the conversation after a query and eliminates the need for users to retell their questions.

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In BI, NLP aids in the translation of analytical results into shared language, making data more accessible to a broader audience. Because of NLP, users from various business functions, such as Finance, Marketing, and Sales can easily access the desired information from the BI system without the assistance of highly technical data personnel.

Another way that NLP can be used to make data more accessible to a wider audience is by implementing a Natural Language Generator (NLG). NLG converts visual analytical output into descriptive or narrative text, allowing people with special desires, such as visual impairment and visual processing deficits, to work more easily with BI systems.

As more businesses collect massive amounts of structured and unstructured data, there will be a greater demand for more user-friendly Business Intelligence tools. Because of social media, eCommerce, and other factors, big data has become even more complex (and valuable). BI software must be both powerful and user-friendly in order to perform complex analytics such as customer sentiment analysis.

You can make queries on your phone using texts or voice commands, and the processing is done in the cloud. You can easily use Google to find out what the weather will be like tomorrow, but soon you will be able to ask personal data Chatbots about customer sentiments and how they feel about your brand, all while walking down the street.

With the advancement of technology, computers will become better at understanding the query and will begin to respond in the form of answers rather than showing the search results. Furthermore, as you progress in your ability to ask questions in natural languages, you will soon be able to receive responses and replies in the same manner.

Instead of simply showing you the data, once the Chatbot has acquired the semantic relations and inferences of questions, it automatically performs the filtration and organisation required to serve a relatable and significant answer.

Integrating your business intelligence software with all aspects of your operations will provide you with data-driven analytics at all hours of the day. You can access the data at any time, in any context, and on any device. This ease of use is exactly what makes NLP so valuable. To communicate with text analytics software, a person no longer needs to be fluent in the appropriate language. It expands end-user access to information and can provide insights into data analysis.

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