Normalization assigns equal weights/importance to each variable, ensuring that no single variable biases model performance in one direction simply because it is larger. Clustering algorithms, for example, employ distance measures to determine if an observation belongs to a specific cluster.
Normalization is required to ensure that the table contains only data that is directly related to the primary key, that each data field contains only one data element, and that redundant (duplicated and superfluous) data is removed.
Better execution is ensured, which is related to the previous point. As information bases become smaller in size, the processing of the information becomes faster and more confined, boosting reaction time and speed.
Linear normalization (max – min) is the optimum normalization technique. It's by far the simplest, most adaptable, and intuitive.