Data normalization is the process of organizing data within a database to ensure that it appears similar across all records and fields. The goal of data normalization is to reduce data redundancy and improve data integrity by eliminating unstructured or redundant data and making the data appear similar across all records and fields. Normalization entails organizing the columns (attributes) and tables (relations) of a database to ensure that their dependencies are properly enforced by database integrity constraints. This is accomplished by applying some formal rules either by a process of synthesis (creating a new database design) or decomposition (improving an existing database design) . The normalization process seeks to guarantee that the consistency of the database is maintained regardless of whether any data is changed, added, or destroyed.
Normalization can have different meanings in different contexts. In statistics, normalization can refer to adjusting values measured on different scales to a notionally common scale, often prior to averaging). In feature scaling, normalization is used to bring all values into the range ). In educational assessment, normalization may refer to aligning distributions to a normal distribution).
Benefits of data normalization include reducing redundant data, providing data consistency within the database, and enabling better data querying and analysis. A fully normalized database allows its structure to be extended to accommodate new types of data without changing existing structure too much, minimizing the impact on applications interacting with the database.
