This chapter argues that a bivariate problem, comprising two categorical variables, can lead to a multidimensional interpretation of the data. It explains the spectrum of analysis methods that handle multivariate categorical data and multivariate quantitative data, ending with a very topical issue, which is the one of the visualisation of complex and heterogenous data. The chapter provides an understanding of a proper distance: the choice of a distance has to be made regarding some research questions. The distance generated by the variables conditions the shape of the statistical units positioned relative to each other. The multidimensional analysis of data consists in finding the dimensions that best represent the distance between the statistical units, in other words, thanks to the Pythagorean Theorem, the dimensions that maximise the inertia of the orthogonal projection of the statistical units on the dimensions. The chapter analyses the main principles behind the visualisation of multidimensional data.