We built a mRNA facility for research purposes, we transformed on R&D organisation to enable precision medicine, we introduced automated and digilized research service platforms ie. cell repository, compound management and others

Be bold and fight for your ideas. Life Science R&D still has hierarchical structures and mindsets and we need to break silos and change the culture to co-innovate more than ever.

A good data strategy can be characterized by being lean, extendable, and precise. Here are key elements to consider: Lean Approach: The strategy should be streamlined to avoid unnecessary complexity. This minimizes time spent on repeatedly reviewing governance concepts during experiments and ensures quick access to data, facilitating rapid decision-making and iteration. Extendibility: The strategy must be designed to scale and adapt as the experiment setup grows. This includes the ability to integrate new data sources and accommodate evolving business needs without overhauling existing systems. Precise Ontologies: Ontologies help in structuring and categorizing data, but they should be kept lean to avoid unnecessary complexity. This ensures they are useful and adaptable, aiding in data consistency and interoperability.