3 questions with…
Senior Bioinformatician Engineer
AI Digital and Biomatics
What is the role of data science in shaping biotechnology innovation?
Data science relies on access to data to provide useful and significant results. Results generated by data science will guide innovation in biotechnology, so there is encouragement to produce data in ways that enables analysis (essentially FAIR). Having data available for analysis is key to derive value, and for driving innovation.
How can companies equip their teams and technologies for a seamless transition towards a data-driven approach?
The key is to standardise how data is stored, and how it is findable and accessible. At minimum this should be by standardising across the company, but a better approach would be to use community or global standards – these standards have been developed with input from many sources, providing the benefit of development, review, and usage by many companies and organisations. In most cases this ensures better standards than a company could develop individually. Using global community standards also enable usage of open source and community tools to analyse the data in better and more efficient ways.
How can data science transform the biotech & pharma sector in the next 5 years?
Good question. Data science enables insights into existing data, allowing for better planning of products, therapies, etc. in the future. It is a bit of a general question.. data science will make the best use of everything that has been generated to this point, which will make planning for the future easier. It also enables better research, better testing of hypotheses , which will enable innovative proposals and solutions.
As far as transformation, one of big area would be related to digital twins, and in-silico methods as well as AI to guide innovation, but I am not sure to what degree this will be considered ‘data science’.