of data mining as well as the positive and negative contexts of its application, have been analyzed. The tasks of discovering knowledge from data, in function of creating a data mining model, are viewed through the prism of a wide range of methodological procedures for their implementation. Special attention has been dedicated to the relationship between data mining and statistics, as a science that traditionally deals with the discovery of regularities from data. The results of the research indicate the great potential of integrated implementation of statistical and data mining approaches in finding innovative methodological solutions to specific problems and, at the same time, suggest the need for mutual adaptation and modification of basic paradigms of data analysis on both sides. Empirical part of the dissertation points out innovative conceptually-methodological frameworks for analyzing data from time series of stock exchange indices and survey data on service users. Empirical results, as the exact knowledge extracted from data, confirm the importance of implementing data mining analysis in the problem contexts of economics, business economics and management. The conducted research represents a suitable basis for profiling future research orientations in the field of data mining application concerning the study of economic phenomena.