The field of data analytics and business intelligence has undergone significant advancements in recent years, with the emergence of new technologies such as Natural Language Processing (NLP). NLP is a subfield of artificial intelligence (AI) that deals with human-machine interactions. It involves processing and analyzing large amounts of natural language data, that is useful in various applications, including business intelligence (BI) which we will explore in this blog with some real-life use cases.
Natural Language Processing (NLP) is a branch of AI that focuses on the interaction between computers and human languages. It involves processing and analyzing large amounts of natural language data, such as text, speech, and images. The primary goal of NLP is to enable computers to understand, interpret, and respond to natural language inputs, just as humans do. NLP techniques involve parsing, tokenization, and machine learning algorithms to process and analyze natural language data. The techniques can be applied to various tasks, such as sentiment analysis, topic modeling, named entity recognition, and machine translation.
NLP has undergone significant advancements since its inception. In the early days, NLP was primarily focused on rule-based approaches, where experts would manually create rules to process and analyze natural language data. However, rule-based approaches were not scalable and were limited in their ability to handle complex language structures.
In recent years, machine learning has emerged as a dominant approach in NLP. Machine learning algorithms use statistical models to learn patterns from large amounts of data and make predictions based on those patterns. Deep learning, a subset of machine learning, has been particularly successful in NLP, as it can handle complex language structures and learn representations of natural language data.
NLP enables machines to understand, interpret and generate human language. By combining NLP with business intelligence, users can perform complex queries without any technical knowledge, making it easier to find insights.
We will look at two different sets of applications. The first three will deal with NLP in BI from the perspective of BI methodologies such as search-driven analytics and conversational insights. The last two will deal with specific NLP applications in data itself, majorly textual data.
NLP algorithms can understand user intent and contextualize queries, making insight access more accurate and relevant. A simple example would be when a sales executive asks, “What are the sales figures of Lamborghini cars for the past quarter in North America?” and the NLP algorithm will understand that the user is looking for sales data of Lamborghini cars with two conditions. The first condition is the timeline – the previous quarter and the second condition is the region – North America.
NLP can understand and interpret user preferences, allowing business intelligence platforms to provide personalized insights and recommendations. For example, a sales manager can ask, “What are the top-selling products in my region?” and the business intelligence platform can provide personalized recommendations based on the user’s location and sales history. It can also vary from the perspective of functions as the requirement of an HR leader is different from that of a sales manager. Only NLP powered BI platforms provide this level of personalization in accessing insights.
NLP has the inbuilt capacity to understand follow-up queries posed by any user. Its ability to mimic cognizance almost like a human in terms of language is what makes it a preferred solution for a variety of BI approaches. To demonstrate with an example, if the sales manager in the previous case asks “What about the worst ones?” the system can understand that is a follow-up question and the user wants to know about the worst performing products in his region.
One of the most common use cases of NLP in BI, sentiment analysis involves the use of NLP techniques to identify and extract the sentiment or emotion expressed in a piece of text. This is useful in various applications, such as analyzing customer feedback, monitoring social media sentiment, and assessing the effectiveness of marketing campaigns. Companies also use sentiment analysis to analyze customer reviews of their products and services. The company can then use the insights gained to identify areas for improvement and make data-driven decisions to improve customer satisfaction.
Topic modeling is another use case of NLP in BI. Topic modeling involves the use of NLP techniques to identify and extract topics from a large corpus of text data. This is useful in various applications, such as content recommendation, content analysis, and trend analysis. A real-life case would be in the media industry. Media companies can use topic modeling to analyze news articles and identify trending topics. The company can then use the insights gained to create relevant content and improve engagement with its audience.
Natural Language Processing has a vital role to play in Business Intelligence. NLP techniques can help organizations to extract insights from a variety of unstructured data, such as customer feedback, social media posts, and online reviews, which are often in the form of natural language text. From the user’s perspective, NLP can intuitively understand and respond to a variety of queries contextually.
The set of applications outlined in the above blog is not exhaustive. In the following blogs, we will discuss in detail the other applications and how NLP helps business intelligence become better one day at a time. Meanwhile, you can read how NLP helped Jake Hallson, a sales manager in an automobile dealership improve his sales figures.
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