How is "Data Redundancy" minimized in the Caboodle Data Model?

Data redundancy in the Caboodle Data Model is minimized primarily through normalization techniques. Normalization is a systematic approach to organizing data in a database to reduce duplication and dependency. It involves structuring a database in such a way that each piece of data is stored only once, which significantly reduces the likelihood of inconsistencies and errors that can arise from having multiple copies of the same data spread throughout the system.

By applying normalization principles, such as dividing larger tables into smaller, related tables and establishing relationships between them, data becomes more organized. This not only enhances data integrity but also makes updates and maintenance more efficient since changes need to be made in one location rather than across multiple instances of the same information.

Other methods such as increasing data entry points or using additional data storage do not address the core issue of redundancy and may actually exacerbate it, while restricting data access is unrelated to how data redundancy is managed. Hence, normalization techniques serve as the foundational strategy for minimizing data redundancy in the Caboodle Data Model.