What does Data Redundancy mean?
Data redundancy is a condition created within a database or data storage technology in which the same piece of data is held in two separate places.
This can mean two different fields within a single database, or two different spots in multiple software environments or platforms. Whenever data is repeated, it basically constitutes data redundancy.
Data redundancy can occur by accident but is also done deliberately for backup and recovery purposes.
Techopedia explains Data Redundancy
Within the general definition of data redundancy, there are different classifications based on what is considered appropriate in database management, and what is considered excessive or wasteful. Wasteful data redundancy generally occurs when a given piece of data does not need to be repeated but ends up being duplicated due to inefficient coding or process complexity.
For example, wasteful data redundancy might occur when inconsistent duplicates of the same entry are found on the same database. Accidental data redundancy could occur due to inefficient coding or overcomplicated data storing processes, and represent an issue in terms of efficiency and costs.
Since the existence of duplicate or unnecessary data fields should be resolved, the reconciliation, integration, and normalization operations required to remove inconsistencies can be costly and time-consuming. Errors generated by accessing the wrong redundant data sets might lead to many issues with clients. Lastly, the additional space taken up by redundant data might start to add up over time, leading to bloated databases.
A positive type of data redundancy works to safeguard data and promote consistency. Multiple instances of the same datasets could be leveraged for backup purposes, disaster recovery (DR), and quality checks.
Redundant data can be stored on purpose by creating compressed versions of backup data that can be restored, and become part of specific DR strategies. In the event of a cyberattack or data breach, for example, having the same data stored in several different places can be critical to ensure the continuity of operations as well as damage mitigation.
Data redundancy can also be leveraged to improve the speed of updates and data access if it’s stored on multiple systems that can be accessed by different departments.
Many developers consider it acceptable for data to be stored in multiple places. The key is to have a central, master field or space for this data, so that there is a way to update all of the places where data is redundant through one central access point. Otherwise, data redundancy can lead to big problems with data inconsistency, where one update does not automatically update another field. As a result, pieces of data that are supposed to be identical end up having different values.
Whenever prevention is not enough, database normalization or reconciliation operations can be required to eliminate already existing redundancies. A series of standardization rules are first defined to set what “normal data” actually is. Then, the database is checked to ensure that the dependencies in all columns and tables are enforced correctly and that all unnecessary duplicates are correctly addressed.