Augmented Analytics platforms frequently integrate natural language processing (NLP) functionalities, enabling users to engage with data through natural language inquiries. This simplifies the process for non-technical individuals to pose questions and obtain valuable insights from the data. Data preparation stands as a pivotal stage in the analytics journey. Augmented Analytics tools possess the ability to automate tasks encompassing data cleansing, transformation, and integration. As a result, the investment of time and effort to mold data into a fitting format for analysis diminishes significantly. Automated data discovery, aiding users in recognizing patterns, correlations, and outliers within their datasets. Enhanced visualization tools then construct interactive, user-friendly charts, graphs, and dashboards to vividly represent these insights. Augmented Analytics systems harness AI and ML algorithms to produce pertinent insights and recommendations derived from the dataset. This technology enables the identification of trends, anomalies, and latent opportunities that could remain overlooked through conventional methodologies. Augmented analytics offers predictive and prescriptive insights. This encompasses more than comprehending past events (descriptive); it entails foreseeing future outcomes (predictive) and proposing actions to enhance and guide those results (prescriptive). Contextual analysis substantially enhances the ability to make well-informed decisions. AI and ML models, empowering users to seamlessly embed predictive models or tailor-made algorithms into their analyses without necessitating extensive technical proficiency.