A PERSPECTIVE STUDY OF NATURAL LANGUAGE PROCESSING IN THE BUSINESS INTELLIGENCE

Abstract

The gathering, storage, analysis, and visualisation of data created by a company's operations is known as business intelligence. There is an uniform five-level architecture that integrates all tools and procedures from data processing to data display. A variety of data science-related programming languages are also available, each of which fully fits each Business Intelligence level criterion. In the digital age, every business generates a large amount of data that must be stored and translated in order to improve the quality of the product and service given. This needs the application of a well-structured data visualisation technique. The discipline of business intelligence has a considerable impact on data manipulation (BI). Many methodologies and software programmes can address such BI requirements, but their effectiveness is determined on the amount, kind, and processing level of the data. The qualities of this BI architecture must first be established utilising particular individual phases because each data-driven business has its own structure. In this circumstance, the general concept of it can be divided into three categories: modifying, storing, and visualising. Transforming level (TL) has the purpose of providing systematised data regardless of the data collecting sources. Depending on the size of the organisation, we can expect information to be accessed in an organised, semi-structured, or instructional fashion. On the other hand, the software solutions employed and their capabilities in respect to data extraction tools may influence the output. For example, some firms use hybrid data input, which entails managing some software products while still saving data to local files. Additionally, processing activities must be completed in order to prepare data for subsequent phases. As a final outcome of the converting level, we should be able to have completely structured data with information separated into logical tables. This means that TL is closely linked to relational databases with well-defined data types and cardinality. In this sector of BI, a combination of cloud-based solutions and local technology can be used for data processing.