Revolutionizing data management in the pharmaceutical industry
Ben Gardner
Lead Data Mesh and Semantic Infrastructure
The FAIR data principles
In a recent insightful discussion, Ben Gardner, the R&D Lead for Data Mesh and Semantic Infrastructure at a top pharmaceutical company, shared his perspectives on the evolving landscape of data management in the pharmaceutical industry, namely the use and evolution of the FAIR data principles.
Understanding FAIR as an acronym
While FAIR is widely used to denote the quality of data, as Gardner points out, many overlook that FAIR is still an acronym (findability, accessibility, interoperability, reusability) and that all components of the acronym are important. Gardner emphasizes the need to move beyond merely labeling data as 'FAIR' and to focus on enhancing these specific parameters. This approach ensures that data is not only high-quality but also functional in various contexts.
Driving findability, accessibility, interoperability, and reusability
Delving deeper, Gardner explains how by increasing the findability, accessibility, interoperability, and reusability of data, it becomes more valuable across different processes and applications. This holistic improvement in data quality is crucial for the dynamic needs of the pharmaceutical industry.
Addressing data limitations and challenges
A significant challenge highlighted by Gardner is the legacy processes of data capture, which are often siloed and process-specific. This type of vertical approach to data management hinders the integration and alignment of data across different processes. The data, while sufficient for its initial purpose, often becomes unusable when applied outside its original context. This limitation necessitates a paradigm shift in how data is captured and utilized.
The shift to data centricity
Gardner advocates for the continued move towards data centricity, shifting away from traditional application-centric approaches. In a data-centric model, data is aligned in a common format, enabling applications to surface this data in support of various processes. This approach turns the traditional model on its head, prioritizing data over applications and thereby facilitating greater flexibility and utility.
Conclusion The role of knowledge graphs
Central to the move towards data centricity is the use of knowledge graphs. Gardner sees knowledge graphs as pivotal in integrating and enabling the exploration and analysis of data. He distinguishes between two major types of knowledge graph applications. Firstly, aiding in information exploration and navigation, and secondly, driving insights and intelligence through more analytical behavior (AI and ML activities).
Ben Gardner's insights offer a glimpse into the future of data management in the pharmaceutical industry. By embracing the FAIR principles and shifting towards more data-centric approaches, the industry can overcome traditional limitations and unlock the full potential of data. This evolution, powered by tools like knowledge graphs, is not just a technical upgrade but a strategic revolution in how data is perceived and utilized.
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