Iterative data quality management system.

High-quality data are the precondition for analyzing and using statistics and for guaranteeing the value of the data. In this paper, the Iterative data quality management system is proposed. The methodology consists of two methods developed by the authors - the Iterative method for the reducing the impact of outlying data points in 2015 and the Data Quality Scale in 2018. The novelty of the Iterative method for the reducing the impact of outliers is the following: an iterative approach for determining the outlying data points is proposed; outliers are determined considering the impact of conjoined factors; estimation of weight coefficients of the outliers and estimation of the total measurement error of the nonlinear regression model is carried out. The Iterative method received the Young Statistician Prize of the International Association for Official Statistics (IAOS) in 2015. The Data Quality Scale has good expansibility and adaptability as makes it possible to evaluate the quality of data at various levels of detail: at indicators' level, at the level of dimensions, and to determine the entire quality of data. The Data Quality Scale gives an opportunity to identify certain shortcomings of the quality of statistical data and to develop proposals to improve the quality of the data. The research results enrich the theoretical scope of the statistical data quality and lay a solid foundation for the future by establishing an assessment approach and studying evaluation algorithms. [ABSTRACT FROM AUTHOR]