examples are the refinement of the measurement strategy and preprocessing, e.g. data balancing or outlier filtering. Also falling under this category are methods that leverage domain knowledge to design better data features, more commonly known as 'feature engineering • More data: in this branch of the data-centric approach, the attention is shifted to increasing the number of data points. This is generally considered to be more significant in view of a better-performing statistical model and a compelling alternative to vast domain knowledge .