This work looks at the ways in which data held in law enforcement systems can be incorrect; what the implications are for personal safety and security; what mitigations are already extant and what can be done to make the situation better. As larger, more interconnected systems and big data are increasingly being adopted in the criminal justice and law enforcement sectors, data in systems now cover a multitude of types of uses. Many forms of data in this sector are now held electronically and could have serious implications if incorrect: everything from wrong offence codes, misspelt names and premature ageing of records could have serious impacts on both safety and security of the population at large. Several types of common data error in this sector will be considered including: Reuse, Ageing, Biasing, Dissociation, Masking, Overload, Mis-application, Mis-match and Falsification. This latter case is significant in this sector, with both unintentional and malicious causes explored.