The technological advancement has had a significant impact on the field of statistics. Long ago, the statistical analysis was often limited to study variables one at a time, requiring the creation of multiple histograms to represent each variable individually. However, this approach has not repeatedly provided a complete understanding of the complex relationship among the different variables and also it did not provide a complete overview of the data. This is where data analysis techniques come into play. Data analysis is an interdisciplinary field that encompasses a set of methods and approaches aimed at uncovering the often-complex underlying structure of a set of multidimensional data. These methods go beyond traditional analysis of individual variables, allowing for the exploration of relationships and patterns that exist between different variables. The objective is to reduce the dimensionality of the data while preserving as much important information as possible, in order to facilitate the interpretation and visualization of the results. Through data analysis, it is possible to graphically represent the structure of the data in a more concise and intuitive manner. For instance, instead of producing multiple charts for each variable, techniques such as principal component analysis (PCA) or an automatic classification can be used to reduce the dimensionality of the data and represent the relationships between variables on a single graph or flat map. The purpose of this essay is to obtain a clearer and more synthetic overview of the data, thus facilitating interpretation and decision-making.