Organized crime groups and law enforcement agencies are caught in complex system similar to a continuous game of cat-and-mouse, in which the latter frequently remains two or more steps behind. Law enforcement agencies are therefore seeking for more proactive strategies in targeting these criminal network structures more effectively. This starts with a better understanding of the way they operate and adapt over time. A key element to developing this understanding remained largely unexploited: big data and big data analytics. This provides novel insight into how criminal cooperations on a micro- and meso level are embedded in small-world criminal macro-networks and how this fosters its resillience against disruption. This paper discusses the opportunities and the limitations of this data-driven approach and its implications for both law enforcement practice and scientific research.
