Data normalization is the process of organizing data into a consistent, standard form. The term covers two related practices. In databases, normalization structures tables so each fact is stored once, reducing redundancy and update errors. In analytics and machine learning, normalization rescales values, such as putting age and income on a comparable range so one feature does not dominate a model.
Both senses share a goal: making data behave predictably. Inconsistent units, mixed formats such as “USA” and “United States”, duplicated records, and wildly different scales all distort analysis and model training.
Normalization is a standard step in preparing data for AI. Teams that record which normalization rules were applied, and to which version of the data, can reproduce a result later. Teams that normalize ad hoc often cannot explain why two runs of the same model disagree.
Related terms: Data Cleansing · Data Quality · Tabular Data