Data synthesis is a process that makes use of statistical techniques to coalesce ideas and theories from different sources and blend them into one idea of the overall effect of a particular intervention or variable. It pools data from many studies to create a clear answer. In criminal justice, data synthesis is critical because it connects, combines and puts together the necessary data which is then utilized in the investigation process. It is very essential in trying to solve a case as it pieces together all the necessary information of that puzzle.
Crime overtime has become an extremely complex phenomenon, that keeps changing across cultures and time. As a result, criminal investigations have to adapt with such complexity. It has to involve complex management tasks that include collection, processing and analysis of information. This analysis is significantly useful since it aims at transforming the raw data into useful intelligence to crack the crime puzzle.
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Data synthesis in criminal justice continues to make a difference in a number of areas. Of particular importance is that data synthesis helps in retracing the history of criminal networks. This ensures that the investigator together with his team is able to review the progress of their investigation, while at the same time being able to retrieve their previous information and patching them together with incoming information (Fei et al., 2006).
The second importance is prediction support, which data synthesis helps connect the missing links. When the data collected is geographically referenced, those responsible with the law enforcement are able to narrow their data further and take advantage of it to predict the likelihood of a crime happening at a particular place and in a particular time. The measures used in such an analysis may include the degree, closeness and other social network analysis used in prediction of crime (Petersen & Wiil, 2013). Data synthesis in criminal justice has therefore been a very effective tool in management of crime.
References
Fei, B.K.L., Eloff, J.H., Olivier, M.S., & Venter, H.S. (2006). The use of self-organising maps for anomalous behaviour detection in a digital investigation. Forensic Science International , 162 (1-3), 33-37.
Petersen, R.R., & Wiil, U.K. (2013). Crime Fighter Investigator: Integrating synthesis and sense-making for criminal network investigation. Security Informatics , 2 (1), 10.
