The Hyve has been advocating for the adoption of FAIR principles since the very beginning and recently we have indeed been noticing a focus shift towards sustainability. FAIR enables powerful new AI analytics to access data for machine learning and prediction. FAIR is a fundamental enabler for digital transformation of biopharma R&D and this is what excites me about our work. All the conversations we have with our clients share an important element: the strong will to improve and speed up medical and pharmaceutical research to save lives and make personalised medicine and affordable drugs the norm.
The FAIR principles describe how research output should be organised so it can be more easily accessed, understood, exchanged and reused. In times of pandemic, working hard with many of the top pharma companies wanting to implement those guidelines gives me hope and a higher purpose to my work.
Our 4-year journey with the FAIR principles at The Hyve has allowed us to find the answer to questions like: Why are FAIR principles important when planning a data strategy? How do they help an organization using a multitude of tools and systems? How can FAIR guidelines accelerate discovery pipelines? Why should I FAIRify legacy data? How can external data from CRO’s or other data partners be natively FAIR?
The technical solution in all these cases is the same: a cross-domain, semantically interoperable data layer, built with the FAIR principles in mind. Some of you may find it difficult to stay up to date with all the new technology trends and wonder how to discern hype from fact. Don’t worry, The Hyve is there to help you out. Our colleagues always like to try new things out, together with other pioneers in the field, and share insights with the larger biomedical research community.
When I started my career in IT, my solutions sales was focused on helping large corporations migrating from a monolithic architecture to cloud-native solutions. Data mesh is now the new black in information architecture, and it is why at The Hyve we center our advice on metadata-driven approaches and distributed solutions that thrive in a culture that is based on collaboration.