Data redundancy in DBMS refers to the practice of keeping the same piece of data in two or more places within a database or data storage system. It can occur intentionally or accidentally. Intentional data redundancy is done to keep the same data in different locations with the organization making a conscious effort to protect it and ensure its consistency. This data is often used for backups or disaster recovery. On the other hand, accidental data redundancy may cause data inconsistencies, which can provide a company with unreliable and/or meaningless information.

Data redundancy can be found in a database, which is an organized collection of structured data that’s stored by a computer system or the cloud. For example, if the same product gets entered twice by mistake, data redundancy takes place. Redundant data costs extra money, demands higher storage capacity, and requires extra effort to keep all the files up to date. It may also cause an increase in storage space and cost, increase the size and complexity of a database, and cause inconsistency in data due to partial updates to the database.

To reduce data redundancy, it is possible to use normalization techniques, which involve organizing data in a database to minimize redundancy and dependency. Another way to reduce data redundancy is to use a relational database management system (RDBMS), which allows data to be stored in a structured way, with each piece of data stored only once.