The aim of this presentation is to briefly present generalized (sectional) curvature and see which kind of relations between data points it evaluates and what kind of information is revealed through this quantity. While in topological data analysis the objective is to extract qualitative features, the shape of data, geometric data analysis mainly deals with quantitative features of data. For instance, the prominent scheme of manifold learning is applied to find the comparatively low dimensional Riemannian manifold on which the data set fits best. It then raises the question of whether one can anticipate some geometric properties from initial model before finding this manifold structure. The most important quantitative measures that in a good extent reveal the geometry of a Riemannian manifold are its (sectional) curvatures. Therefore, we wish to see how one can determine the curvature of data and how does it help to derive the salient structural features of a data set.