Why is redundancy of data undersirable?

 Redundancy of data refers to the unnecessary repetition of data within a database or a data storage system. Redundancy is generally considered undesirable for several reasons:

1. Wasted storage: Redundant data occupies additional storage space, leading to inefficiencies in data storage and increased costs for storage hardware or cloud services.

2. Data inconsistency: When the same piece of data exists in multiple places, any updates need to be made across all locations to maintain consistency. If this is not done effectively, it can lead to discrepancies, with different versions of the same data appearing in different places.

3. Increased complexity: Managing redundant data requires additional effort, as it compounds the complexity of data management tasks such as data integration, transformation, and cleaning.

4. Slower processing: The presence of redundant data can slow down data processing, as systems need to handle more data than necessary. This can affect performance, especially when executing complex queries and reports.

5. Higher risk of errors: The more data that is replicated across a system, the higher the chance of introducing errors during data entry or data migration processes.

To manage redundancy and avoid these problems, many databases use normalization, which is a process of organizing data to minimize duplication.