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Data analysis is the collection of different process data and the analysis of this data using statistical methods. The analysis can contribute to the management, planning, control or execution of strategies. In the course of digitalisation and through the use of new technologies, the volume of data (big data) is increasing and with it the possibility to make use of it. There are primarily three statistical methods used in practice to draw meaningful conclusions from the collected data:
1. Multivariate data analysis:
In this data analysis method, at least two indicators are measured independently of each other and checked at the same time (simultaneously). Correlations between the variables are considered in the evaluation and analysis. Essentially, multivariate data analysis can be either structure-discovering (exploratory) or structure-testing (confirmatory), depending on the objective. The aim of the structure-discovering variant is to find possible dependencies to gain previously unknown insights. The structure-testing variant, on the other hand, checks the identification of presumed relationships between the objects under consideration.
2. Quantitative data analysis:
This type of data analysis is aimed at quantitative expressions. The focus here is on measurable variables. Quantification is intended to serve as a basis for statistical calculation methods and to establish logical relationships based purely on numbers. Important criteria for the quality of an analysis are:
3. Qualitative data analysis:
With qualitative data analysis, subjective results are visualised and examined for the cause of their occurrence. Due to the subjective environment, the theoretical findings are not considered representative. Rather, they serve to uncover new questions that can be used as the basis for empirically founded theories.