• NLP deploys algorithms, text and grammar rules, and intelligent parsing to decrypt meaning and intent from the user’s voice utterances.
  • Regarding BI, language processing facilitates information analysis for all users, from beginners to advanced analysts, irrespective of the BI tools’ knowledge level.

Language has been a primary medium or communication tool to convey things. Several semantic cues in the language, such as signs, symbols, words, and images, offer a close conveyance of the things represented in the real world.

Humans, when interacting with text, generally understand the underlying meaning. Computers, on the other hand, when they come across a text, can only analyze character strings without any context of the real-world intent of the text or meaning. The growing reliance of humans on computers made it essential for the latter to expand analyzing capabilities for understanding relevant text and languages. This brings natural language processing (NLP) on board. With the widespread adoption of artificial intelligence (AI) and machine learning (ML), NLP is emphasizing augmenting the human-machine interface (HMI) and communication between computer systems and humans.

Natural Language Processing

Natural Language Processing hails from computational linguistics and computer science, closely associated with the system and natural (human) language. Semantics, in this perspective, is the meaning and integration between words. NLP assists computer systems in enhancing semantic data structures and data context to explore underlying implications.

Computer systems use semantics to allocate intent and meaning to text and words, facilitating an interface between the user and computer.

Natural Language Processing Illustrations

NLP has intervened and penetrated HMI to a large extent. The most common instances that can be cited include smart technologies such as Siri, Alexa, Google Assistant, and other voice assistive tech that identify speech patterns to decipher the underlying meaning of the command and accordingly serve the suitable response.

NLP also supports a few of the mailing functionalities of Google. The e-mail service uses NLP to voluntarily convert unstructured or unreadable data into the most comprehensive format to decode the content of messages and analyze inclusions such as notifications, reminders, invitations, and others.

A web search engine is another general example of NLP in action. Whenever a phrase is entered into a search engine, various suggestions that algorithmically follow similar search patterns pop out. The same can be observed in the search functions of social media sites.

Basically, NLP deploys algorithms, text and grammar rules, and intelligent parsing to decrypt meaning and intent from the user’s voice utterances. The user’s command is either a statement fragment or questions asked in a common language composed of a series of keywords.

With advanced language processing, systems can better handle linguistic suggestions and decipher references to break down the intended meaning of the statement. This has made NLP all-pervasive for systems and users.

Role of NLP in Business Intelligence (BI)

Business intelligence personnel offer a language interface for visualizations assistance. Hence, users can seamlessly interact with the data by raising questions and concerns. When it comes to the BI domain, NLP is housed under smart analytics, closely associated with AI and ML.

Regarding BI, language processing facilitates information analysis for all users, from beginners to advanced analysts, irrespective of the BI tools’ knowledge level. Users are always searching for data insights, and NLP is a crucial functionality that assists in quenching that. It is capable of enquiring about data attributes and, accordingly, follows the necessary underlying algorithm.

Users generally don’t input random questions without a frame of context. Here in BI also, NLP comprehends the context of the conversation to evaluate the user command intent behind the question and then proceed with the dialogue, thereby leading to a robust conversational experience. It saves the user’s efforts of specifying or elaborating. When the follow-up question pertaining to the users’ data is to be asked, they need not rephrase the question any deeper or clear the vagueness; the NLP does it all. For instance, you can ask the BI tool “gas station near my home” and then a follow-up question like, “How about near my office?” without mentioning ‘gas station’ in the latter query.

NLP capabilities in BI tools enable users to access insights by conversing with the relevant data. With the growing NLP access across the BI domain, it’ll be easier to surpass barriers to analytics and transform the way of user engagement.

NLP leverages computational and analytical methodologies to assess the user’s comprehensible language and facilitate smooth and efficient system interaction. Developing complex analytical models with deep neural networks’ deep learning has assisted NLP in BI. The conventional methods of analyzing user commands and actions are obsolete in the existing business sphere. NLP applications are becoming more user-oriented to work as a roadmap to the BI future. A robust synchronization exists between BI, NLP, and analytics.

The natural language interface has been changing the way of interaction with complex systems involving large datasets and databases. Even novice personnel in BI with little or no knowledge of technological systems can assess data all by themselves without waiting for IT experts to intervene and execute complex reports.

Though it majorly works on translating natural speech into the machine or system comprehensible language, it is expanding capabilities to make the system understand commands and convey corresponding responses than mere raw search results. In organizations, BI data generally exists in unstructured form. NLP assesses patterns of this scattered data to make it suitable for later-stage analysis.

Analysis of user search-flow algorithms with the help of language process techniques to extract specific data from a text sample is called opinion mining. Simplified data access enables businesses to save on expenses and time and land safely into hassle-free decision-making.

NLP-equipped chatbots are becoming integral to several BI-supported systems with advanced search features. Both prominent and novice BI players are in a highly harsh environment competing with the help of MLOps technologies and data science.

NLP’s integration and update are found across several systems such as Google Looker, Domo, Microsoft Power BI, and others. These systems have made data consumption simpler by allowing users to obtain data using natural language searches.

NLP Enhancing BI

In most BI units, accessing data is usually a long monotonous process that demands technical proficiency, thus leading to reduced adoption rates. These days, many organizations are increasingly integrating NLP-supported BI chatbots that can comprehend natural language and execute complex operations.

BI is now becoming adept at prescribing and predicting associated actions based on real-time data. With the help of innovative NLP, most operations can now be executed in machine-readable language and retrieved from an extensive array of sources.

Users can gain actionable information by using a conversational interface rather than opening a BI program every time. The extensive database and complex dataset provide quick responses. Hence, businesses can make crucial data-based decisions effectively.

Both text- and voice-based natural language interfaces (NLIs) can interpret input questions and generate relevant data insights.

Multiple workflows can be automated using NLP to help businesses access and work on the most relevant data. Moreover, search engines can leverage NLP algorithms to recommend relevant results depending on user requirements and the pattern of search history.

Several NLP use cases are used for workflow optimization. For instance, it can evaluate social media to explore brand engagement or current market trends. Besides, a chatbot is another significant use case that enhances customer service by streamlining the response process to FAQs, thereby strengthening user interaction.

It is essential to note that NLP solutions’ implementation should emphasize a particular domain area. This is because if the domain of the model is vast, there are chances that NLP might offer specific incorrect outcomes.

Wrapping Up

NLP systems in BI require a vast database to function. It becomes essential to generate lucid explanatory BI models using language processing to understand how the model made it to certain business decisions.

Both novice and established organizations should generate robust enterprise versions of NLP-based BI systems to support the large-scale development of well-equipped BI models that can help resolve some existing hurdles in language processing and deliver businesses the optimum anticipated results.