Redundant data is akin to having multiple copies of the same book on different shelves in a library. In the context of databases, it refers to the presence of duplicated or repetitive information within the data storage system. Imagine a customer's contact information, such as their name and address, being stored in multiple places instead of just once. This redundancy can occur for various reasons, such as poor database design, data entry errors, or the absence of data normalization.

While redundancy may seem harmless, it can lead to several issues. First, it increases the storage requirements, which can be costly, especially for large datasets. Second, it can introduce data inconsistencies, where different copies of the same data may have conflicting values, causing confusion and inaccuracies. Third, it can slow down data retrieval and processing because the database must search through multiple occurrences of the same information.

Effective database management involves identifying and addressing redundant data by employing techniques like data normalization, which minimizes duplication by organizing data into efficient and non-repetitive structures. By eliminating redundancy, databases become more efficient, accurate, and easier to maintain, ultimately enhancing data integrity and system performance.