A Literature Review on Augmented Analytics and Natural Language Generation: A Review of State of Art Techniques, Opportunities and Challenges

Abstract

Augmented analytics is a type of analytics in which machine learning and artificial intelligence are used to provide users with more advanced and understandable analytical capabilities. Data preparation, analysis, and result interpretation are all automated steps in the analysis method. Natural language processing (NLP), automated data collection, machine learning, data visualization, explainable AI, and collaborative analytics are some of the techniques used in augmented analytics. The goal of augmented analytics technology is to simplify and modernize data analysis, making it more accessible to a wider variety of people and enabling improved decision-making across organizations. NLP is a branch of artificial intelligence (AI) and machine learning (ML) that studies the interactions between technology and people. The purpose of this study is to examine cutting-edge approaches in augmented analytics and natural language processing in order to create a sophisticated natural language generation model for augmented analytics data interpretation.