Data editing is the process of checking and correcting data. In practise, these processes are often automated. A large number of constraints needs to be handled in many applications. This paper shows that data editing can benefit from constraint simplification techniques that are often used in Operations Research and Artificial Intelligence. Performance can be improved and a better quality of automatically corrected data can be obtained. First, a new procedure for constraint simplification will be proposed that is especially developed for data editing; a procedure that combines several known algorithms from Operations Research and Artificial Intelligence. Thereafter, it will be demonstrated that real-life edit sets can actually be simplified.