Data normalization is a technique used in database design to streamline data and improve its accuracy. It involves organizing data into tables and establishing relationships between them, ensuring consistency and reducing redundancy. Essentially, data normalization is a way to eliminate data duplication and inconsistencies in information.

The process of data normalization involves breaking down data into smaller, more manageable parts, which are then organized into separate tables. This allows for more efficient data management and reduces the risk of errors or issues with data integrity. By breaking down data into smaller parts, it becomes easier to update and modify information, without affecting other data in the database.

There are several levels of normalization, each with its own set of rules and guidelines. The first level, known as First Normal Form (1NF), involves eliminating repeating groups of data and ensuring that each table has a primary key. Second Normal Form (2NF) requires that each non-key column is dependent on the primary key, and Third Normal Form (3NF) involves removing columns that are not dependent on the primary key.

While data normalization can be a complex process, it is an important aspect of database design and management. It ensures that data is accurately represented and maintained, reducing the risk of errors and inconsistencies. Overall, data normalization plays a crucial role in ensuring that databases are efficient and reliable, providing users with accurate and consistent information.