How to overcome the problems associated with new types of data?

The best known classification of scales for measuring data, according to their nature, defines four types of data: nominal, ordinal, interval, and ratio. Only the last three have received official legitimization in metrology. Although a number of researchers from the academia, who proposed an alternative typology, criticized the above classification, this did not significantly affect the industrial and business statistics preferring to be in harmony with the current legal metrology. On the eve of the fourth industrial revolution, we are increasingly confronted with new types of data, which does not fall under any of the above-mentioned classifications. Hence, we often find it difficult to interpret the new data. In our lecture, we present only two kinds of them: tree structured data and preference chains. An example of the first type is the international classification of diseases (ICD), proposed by the World Health Organization (WHO), where diagnosis of a disease is associated to a specific node in the hierarchical tree. Preference/priority chains are needed when studying consumer preferences, customer requirements, in a risk management, decision-making, etc. Recently, a certain progress was achieved in determining distance metrics and analysis of variation allowing to extract useful information from such kinds of data. The lecture will report and demonstrate these achievements.