The goal of this paper is to develop novel tools for understanding the local structure of systems of functions, e.g. time-series data points, such as the total correlation function, the Cohen class of the data set, the data operator and the average lack of concentration. The Cohen class of the data operator gives a time-frequency representation of the data set. Furthermore, we show that the von Neumann entropy of the data operator captures local features of the data set and that it is related to the notion of the effective dimensionality. The accumulated Cohen class of the data operator gives us a low-dimensional representation of the data set and we quantify this in terms of the average lack of concentration and the von Neumann entropy of the data operator by an application of a Berezin-Lieb inequality. The framework for our approach is provided by quantum harmonic analysis. Contents 1. Introduction 1 2. Part A: Context, Results and Numerical Examples 2 2.1. Concepts and Notation 2 2.2. Main Results 9 2.3. Examples and Numerical Illustrations 13 2.4. Interpretation and Summary 17 3. Part B: Technical Details and Proofs 17 3.1. Technical Background 17 3.2. Correlations and essential dimensions 27 3.3. More relations for average lack of concentration and the approximation of data operators 31 References 33