What does the principle of “normalization” aim to achieve in data?

The principle of normalization in data aims to reduce redundancy and improve data integrity. This process involves organizing data in a database efficiently by structuring it in such a way that relationships among data elements are established clearly, and data duplication is minimized.

When normalization is applied, data is divided into smaller, related tables, ensuring that each piece of information is stored only once. This not only helps in reducing the overall storage costs but also significantly enhances data integrity, as there are fewer chances for inconsistencies when updates occur. For instance, if a piece of information needs to be updated, it has to be changed in only one place rather than in multiple instances scattered throughout the database.

Other choices do not align with the core aim of normalization. While improving the speed of data access may be a potential side benefit of properly normalized data structures, it is not the primary objective. Increasing the volume of data is unrelated to normalization, as the focus is on organizing existing data rather than increasing its amount. Enhancing data visualization is also a different objective altogether, focusing on how data is presented rather than how it is structured within databases.