Data inconsistency is a term used in database management systems (DBMS) that refers to mismatched data values in different tables within a database. It occurs when data stored in different locations within the database do not match or are not synchronized. This can lead to unreliable and meaningless information, which can cause inaccurate results and reports. Data inconsistency can be caused by software bugs, human errors, hardware failures, and many other situations.

Data inconsistency is different from data redundancy, which refers to the unwanted repetition of data in multiple locations within a database. Data redundancy can lead to data inconsistency, as changes made to one copy of the data may not be reflected in other copies.

To prevent data inconsistency, data validation rules can be applied, regular data audit processes can be performed, and transactions can be used. Databases designed in the OLTP format tend to be highly normalized and more resistant to data duplication errors. However, even with these measures in place, human error can still cause a certain level of data inconsistency.

Ironing out database inconsistencies manually is a long and tedious task, and even with the use of some fuzzy matching models, results that are more than a little bit dissimilar risk not being picked up. Therefore, this task is ripe for the application of automated data quality filters.