What type of data would most likely benefit from normalization?

Normalization is a process used in database design to reduce data redundancy and improve data integrity. It involves organizing data in a way that eliminates unnecessary duplication and ensures that relationships between data are properly managed.

Highly redundant and repetitive structured data is particularly a candidate for normalization because this type of data often suffers from inefficiencies related to storage and maintenance. When a database contains repeated data, it can lead to inconsistencies—where the same data is altered in one instance but not in another—making it harder to ensure data accuracy. By applying normalization techniques, such as dividing a database into tables and establishing relationships through foreign keys, the structure becomes more efficient, easier to maintain, and less prone to error.

In contrast, completely structured database records might not need normalization as they are already organized efficiently. Unmanageable unstructured data lacks a defined structure, making it difficult to apply normalization processes effectively. Similarly, data that is perfectly organized would also not benefit from normalization since it is already optimized for storage and retrieval. Thus, the need for normalization is most pronounced in scenarios where redundancy is significant, emphasizing the benefits of option C.