Cedric Berger
Novartis International
Studied in Strasbourg (FR) and obtained a Master's degree in biotechnology/ medical imaging, Berger received a Ph.D. in biophysics in Basel (CH) working at Novartis/ Biozentrum. He then moved to Boston to secure a postdoctoral fellowship at Harvard Medical School doing translational research with the Massachusetts General Hospital and validated an executive MBA (Management of Technology and Innovation) at the Swiss Polytechnic Federal School of Lausanne (CH). After performing research for 8 years at the interface of oncology, inflammatory and neurosciences in the public and private sectors, Berger worked for 7 years in the pharmaceutical industry designing, implementing, conducting and analysing adult and paediatric oncology studies. Since joining Novartis in 2016, Berger is the enterprise product owner for data/information/knowledge modeling and governance. Leveraging semantic web principles and cross-industry data standards, Berger implemented a team featuring knowledge engineering skills, new data management processes, a technological solution to become a platform and modeling services to inventory, FAIRify and expose in a transparent manner Novartis data assets in their business. The Novartis framework provides the most extended and interlinked representation/description of the business, supporting data governance and other purposes.

2021 Talk: Data Governance 4.0 applied to a Unified Clinical Data Model

Driven by legacy paper-based approaches, the design, conduction and analysis of clinical studies requires the creation and transformation of many data in many different formats. This hinders the process and necessitates significant resources. Having metadata-driven transformation is not new, however, adoption of standards (.e.g CDISC) has not shown yet its full added value. We propose to extend this metadata-driven approach beyond CDISC standards from the writing of the unstructured clinical study protocol to submission of the clinical study report to health authorities. By sharing and reusing metadata end-to-end of the drug development critical path, we shorten and automate key tasks hence improving the efficiency of the overall process.