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A rapid understanding of data features is the main advantage of visualization compared to other ways of presenting information. Moreover, the purpose of visualization is to identify the necessary data transformations and features of methods for further work with the data. The application of heatmaps to visualize a dataset characterizing the quality of a system provides an effective implementation of the objectives under consideration. The paper considers the use of heatmaps to identify the features of a dataset composed of parameters characterizing the quality of life and Russia's constituent entities and to determine the necessary data transformations. Composite indicators of quality of life of the population are determined based on statistical data for the period 2010-2017. The author's modification of principal component analysis is used, which takes into account the presence of unrecoverable errors in the initial data. Signs of calculated weighted coefficients are used as an estimate of the reliability of the calculated composite index. Obtaining negative weighted coefficients is the main obstacle to the widespread use of multidimensional analysis methods in building complex indices. Heatmaps allow identifying variables with abnormal behavior. The use of logarithmic data correction for such variables can eliminate negative weights and allows one to build a more reliable composite index of a system's quality. [ABSTRACT FROM AUTHOR]