Although the Big Data approach seems promising in various analytic uses, sharing or integrating data within the same analysis space remains a complex task as existing data is highly heterogeneous and difficult to compare. In this position paper, we address the Variety and Veracity dimensions of Big Data when integrating, sharing and reusing large amount of heterogeneous data for data analysis and decision making applications in the healthcare domain. Many issues are raised by the necessity to conform Big Data to standards in order to make data more interoperable both by humans or computations such as data mining. In this paper, we discuss how ontologies (computerized meaning) can contribute to the improvement of information sharing and address the problem of data sharing together with semantic interoperability data frameworks. We then introduce the main steps required for building domain ontologies as they could be implemented in the context of Forensic and Legal medicine. We conclude with a particular emphasis on the current limitations in healthcare data standardization and the importance of knowledge formalisation. for the coming years, in order to maximise data re-use in forensic and legal medicine.