Feature standardization is a widely applied step in data preprocessing that transform the feature values so that the data mean value is equal to zero and standard deviation is equal to one. It allows for easier and more comprehensible comparison between features by making each feature contribute evenly to the process of analysis. The aim of this paper is to investigate the impact of data standardization on the performance of principal component analysis (PCA) and classification using support vector machine (SVM) algorithm. The analysis is performed on two datasets with overlapping feature values and more separately spaced feature values for unstandardized and standardized data. Additionally, balanced and imbalanced datasets are evaluated. Final results showcase that data standardization significantly improves the performance of PCA as well as classification effectiveness of the SVM algorithm.