$1.$ It is descriptive analytics that focuses on describing the aggregation of historical data in order to provide information about past performance. It uses different statistical measures and graphing tools, in order to uncover what happened within an organization. For instance, a company may implement descriptive analytics and graph information on sales trends for the past year using bar charts or line graphs. Such an approach, in its turn, enables stakeholders to see where a business is at any given moment. Exploratory analytics goes deeper into the data to discover patterns, relationships or outliers that may not be visible through casual inspection. Unlike descriptive analytics, EDA focuses on novelty and derives hypotheses for in$-$depth research. For example, a retail business could employ EDA techniques such as cluster analysis or association rule mining to detect surprising correlations between customer demographics and purchasing behaviour. The purpose of explanatory analytics is to understand why some events happened given the available data using advanced statistical techniques like regression analysis or causal inference models $($Campi et al$.,2021).$

$2.$ In descriptive analytics, previous data is analyzed to determine what has occurred in an organization or within a particular setting. It seeks to offer historical performance insights by summarizing and presenting critical indicators. Retail sales analysis is a clear example of descriptive analytics. For instance, a supermarket chain may need to evaluate its sales performance over the past year. With the help of DEA, helpful information can be derived about such as total revenues generated each month, bestselling products addition to peak hours for customer traffic and consumer spending trends. However, exploratory analytics concentrates on discovering hidden patterns or trends in data through visual exploration and statistical methods without prescribed hypotheses. For example, suppose an e$-$commerce website is analyzing consumer behaviour on its site using EDA approaches. Clustering algorithms may be used to cluster user interaction data such as clicks, time spent on pages, and purchase history in order for patterns to reveal distinctive segments of users based on their browsing behaviour and buying habits. EPA focuses on creating causal connections between variables in datasets through explanations of observed phenomena or trends as a result of factors that drive them. For instance, an educational establishment aiming to establish factors contributing towards student performance through EPA methods is a good example. Through running regression analyses and hypothesis tests on study habits, socioeconomic backgrounds, teacher$-$student ratios, and extracurricular activities, the establishment can identify which variables have significant effects on academic performance $($Yu et al$.,2021).$