Augmented analytics, managed by machine learning and natural language processing, handles data analysis findings, reducing the time-consuming pre-processing and feature development processes. The article focuses on the importance of Augmented Data Science (ADS), an interactive, data-driven system that combines personal judgement with analysis of statistics to improve decision-making in data interpretation. The challenges are developing the requirements for assessment, developing defined review methods, and comparing suggested methodologies to real-world datasets and use cases. The goal is to create and develop a model for data interpretation and natural language-based generated output in augmented analytics, with objectives including data processing, model design, query processing, and component analysis.