What does normalization in data processing aim to achieve?

Normalization in data processing is a systematic approach to organizing data within a database. Its primary aim is to enhance data integrity while reducing redundancy. By structuring the data in such a way that each piece of information is stored only once, normalization minimizes duplication and the potential for inconsistent data. This means that when changes are made to a particular data item, they only need to be updated in one place, which helps in maintaining accuracy and consistency throughout the database.

For instance, in a normalized database, a customer’s details would be stored in a single table rather than having redundant copies spread across multiple tables. This not only conserves storage space but also simplifies data maintenance. Furthermore, normalization helps to establish clear relationships between data entities, thereby reinforcing data integrity. This structured organization supports more effective data management and retrieval processes.

The incorrect options highlight concepts that do not align with the true objectives of normalization. Increased data redundancy contradicts the very purpose of normalization, which is to eliminate or reduce unnecessary duplication. A more complex data structure may arise during the normalization process but is not a goal; rather, the goal is a clearer data framework. Lastly, while normalization can sometimes lead to improved performance in terms of data integrity, it may not always result in faster data retrieval