Studying Data Normalization Methods in the Sustainable Development Indicators Analysis

Data normalization is an important step in the sustainability analysis. This is the process of bringing data to a single scale, which makes it possible to compare them with each other and identify the patterns. The article discusses the following data normalization methods and their application in the Sustainable Development Indicator (SDI) analysis: mean normalization, min-max scaling, Z-score normalization, sigmoid and robust sigmoid. It is important to note that selection of the normalization method should be based on specifics of a particular study and data requirements. For analysis criteria, the most suitable method is the sigmoid normalization or its variation robust sigmoid, since it is most resistant to stochastic outliers in the data sample. A meaningful analysis could also require other methods and approaches, such as data aggregation, computation of the sustainability indicators and statistical analysis.