How does normalization impact data design?

Normalization is a key process in database design that focuses on organizing data to reduce redundancy and improve data integrity. This process involves structuring a relational database in such a way that related data is stored in separate tables and linked through relationships. As a result, normalization minimizes the duplication of data across the database.

By reducing redundancy, normalization helps to ensure that updates to data occur in a single location, reducing the risk of inconsistent data being stored in multiple locations. For instance, if a customer's address is stored in several tables and needs to be updated, there is a risk that not all entries will be updated, leading to data integrity issues. Normalization addresses these concerns by enforcing rules that contribute to the overall accuracy and reliability of the database.

The other options relate to different aspects of data management that are not directly influenced by normalization. While normalization does facilitate some aspects of data retrieval and may aid in improving visualization indirectly by organizing data effectively, its primary impact lies in reducing redundancy and improving data integrity. This emphasis on structured organization and integrity is what distinguishes normalization as a critical aspect of data design in the context of the DAMA-DMBOK framework.