Database normalization is a design technique used in relational databases to organize data into tables in such a way that it reduces redundancy and dependency. The process involves dividing large tables into smaller, manageable ones and defining relationships among them. This ensures that each piece of information is stored in one place, which helps maintain data accuracy and consistency.

1. Reduces Data Redundancy: Normalization minimizes the duplication of data across the database. By ensuring that each data item is stored only once, it reduces storage costs and the risk of inconsistencies.
2. Improves Data Integrity: By organizing data logically and enforcing relationships through keys, normalization helps maintain the accuracy and reliability of the data. This prevents anomalies during data operations such as insertions, updates, and deletions.
3. Enhances Query Performance: A well-normalized database can improve the efficiency of queries. Although excessive normalization can lead to complex joins, a balanced approach allows for optimized query execution.
4. Simplifies Data Maintenance: With data organized into smaller tables, updates and modifications become less complex and less prone to errors. For example, changing a customer's address only requires updating a single record in the relevant table.
5. Facilitates Scalability: Normalized databases are generally easier to scale, as they minimize contention and allow for more efficient data processing. This is particularly important for organizations dealing with large volumes of data.

In summary, database normalization is a critical aspect of database design that enhances data integrity, reduces redundancy, and improves overall performance. By following normalization principles, organizations can ensure their databases are efficient, reliable, and easier to maintain, ultimately leading to better data management practices.

Normalization is a systematic approach used in database design to organize data efficiently. It involves breaking down large tables into smaller, related tables and defining relationships among them using keys. The primary goal of normalization is to eliminate data redundancy and ensure that each piece of data is stored only once, thereby avoiding inconsistencies and duplication.

1. Reduce Data Redundancy: By organizing data into smaller tables, normalization minimizes duplicate data, which can lead to inconsistencies and errors. This helps maintain a cleaner and more efficient database structure.
2. Improve Data Integrity: Normalization enhances data integrity by ensuring that related data is stored in a structured manner. This makes it easier to maintain consistency across the database, especially when updates or deletions occur.
3. Enhance Query Performance: A well-normalized database can improve the performance of queries by reducing the amount of data that needs to be processed. This leads to faster data retrieval and more efficient use of resources.
4. Simplify Database Maintenance: With a normalized structure, it becomes easier to manage and maintain the database. Changes to data can be made in one place, and related data can be updated automatically through defined relationships.
5. Facilitate Logical Organization: Normalization helps in logically organizing data, making it easier to understand and navigate. Each table serves a specific purpose, and relationships between tables are clearly defined, which aids in data management.

In summary, normalization is a crucial process in database management that helps reduce redundancy, improve data integrity, enhance query performance, and simplify maintenance. By organizing data effectively, normalization ensures that databases function optimally, leading to better performance and reliability in data handling.