Police analysts are required to unravel the complexities in data to assist operational personnel in arresting offenders and directing crime prevention strategies. However, the volume of crime that is being committed and the awareness of modern criminals make this a daunting task. The ability to analyse this amount of data with its inherent complexities without using computational support puts a strain on human resources. This paper examines the current techniques that are used to predict crime and criminality. Over time, these techniques have been refined and have achieved limited success. They are concentrated into three categories: statistical methods, these mainly relate to the journey to crime, age of offending and offending behaviour; techniques using geographical information systems that identify crime hot spots, repeat victimisation, crime attractors and crime generators; a miscellaneous group which includes machine learning techniques to identify patterns in criminal behaviour and studies involving reoffending. The majority of current techniques involve the prediction of either a single offender's criminality or a single crime type's next offence. These results are of only limited use in practical policing. It is our contention that Knowledge Discovery in Databases should be used on all crime types together with offender data, as a whole, to predict crime and criminality within a small geographical area of a police force.