What do we call the issue that arises when redundant data may not be updated properly?

The situation where redundant data may not be updated properly is termed an update anomaly. This occurs when a change to one instance of duplicated data fails to propagate to all instances, leading to inconsistencies within the database. In a well-normalized database, redundant data should be minimized or eliminated to ensure that updates can be made in one place, thereby maintaining data integrity.

When redundant data exists, if only some instances are updated while others remain unchanged, it creates discrepancies, making it difficult to trust the data or ascertain the correct information. For instance, if a customer’s address is stored in multiple records and one instance is updated while others are not, it can lead to a situation where different records reflect conflicting information about the same customer. This highlights the importance of normalization in database design, which aims to reduce redundancy and improve data integrity, thereby mitigating update anomalies.

The other terms, while related to data handling issues, refer to different scenarios: redundancy anomaly pertains to the presence of duplicated data, insertion anomaly relates to challenges faced when adding new data, and deletion anomaly involves data loss when a record is removed. Each describes specific issues that can arise in poorly designed databases, but the update anomaly is specifically about the problems that arise from incorrect or incomplete updates of redundant