This paper aims to develop a theory of statistical observation in terms of scientific and methodological approaches to processing big data and to determine the possibilities of integrating information resources of various types to measure complex latent categories (using the example of social comfort) and to apply this experience in practice through the use of the financial situation indicators in forecasting. The authors have built a social comfort model in which the choice of weights for its

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... omponents is based on a modified principal component analysis. The assessment is based on Google Trends data and official statistics. Google Trends data analysis methods are based on the development of an integrated approach to the semantic search for information about the components of social comfort, which reduces the share of author's subjectivity; methodology of primary processing, considering the principles of comparability, homogeneity, consistency, relevance, description of functions and models necessary for the selection and adjustment of search queries. The proposed algorithm for working with big data allowed to determine the components of social comfort ("Education and Training", "Safety", "Leisure and free time"), for which it is necessary to directly integrate big data in the system of primary statistical accounting with further data processing and obtaining composite indicators. The authors conclude that a stable significant correlation has been found for the "Financial Situation" component, which makes it possible to use it for further calculations and extrapolation of financial indicators. The scientific novelty lies in the development of principles and directions for the integration of two alternative data sources when assessing complex latent categories. The findings and the results of the integral assessment of social comfort can be used by state statistics authorities to form a new type of continuous statistical observation based on the use of big data, as well as by executive authorities at the federal, regional and municipal levels in terms of determining the priorities of socio-economic policy development.