Data centricity FAIR data ONTOFORCE


Data centricity and FAIR to drive improved patient outcomes

The FAIR data principles play a pivotal role in ensuring that data-centric approaches in life sciences organizations are effective and sustainable to enable efficient timelines for faster drug delivery.


Within the life sciences industry, fostering data-centric approaches is not only essential to the success of an organization. More importantly, these approaches are integral to facilitating more efficient drug development timelines, which translates into patients receiving life-saving treatments faster. 

Data centricity is not just an outcome but rather can be seen as a catalyst for achieving wider goals that are shared across the industry, such as improved patient outcomes and sustainable healthcare systems. When we are using and re-using data maximally and leveraging technology appropriately to do so, it’s patients who ultimately stand to benefit.  

The FAIR data principles play a pivotal role in ensuring that data-centric approaches in life sciences organizations are effective and sustainable to enable efficient timelines for faster drug delivery.  

What are the FAIR data principles? 

The FAIR data principles are a set of principles that were developed and formalized within the life sciences industry to support individuals/organizations wishing to enhance the reusability of their data. Since its formalization in 2016, FAIR has proliferated across the industry, becoming a major tenant in many organizations’ data management strategies. 

The FAIR acronym stands for: 


Findability refers to the ease with which data can be discovered and accessed by both humans and machines. Metadata and data should be richly described with a globally unique and persistent identifier. 


Once found, data needs to be accessible. This doesn't just mean that data is available to view, but also that it is accessible in formats and via protocols that are universally understandable and usable. Importantly, accessibility also considers the necessary security and privacy measures, which is especially critical in handling sensitive health data. 


Interoperability is the ability of data systems, tools, software, etc. to exchange and make use of information and data. In life sciences, this is especially important when integrating data from different sources. The data should be compatible with each other along with applications and workflows. This compatibility is crucial for comprehensive analysis and research.  


Reusability ensures that data can be reused in different contexts and by different users. This principle emphasizes the importance of clear data documentation, including information about the methodology, provenance, and recommended citation. This approach enhances collaboration and avoids duplication of effort in research. 

 A FAIR culture to enable data centricity  

For many organizations, roadblocks still stand in the way of full implementation of the FAIR data principles and thus, in the way of embracing true data-centric approaches across the entire drug development timeline. Despite the fact that adopting the FAIR principles, especially for research data, can enable cost savings - a European Union funded study has suggested that the lack of FAIR research data and associated metadata costs the European economy at least €10.2bn every year – barriers to FAIR will still persist for some companies.   

These barriers might stem from historical problems that have long persisted in an organization, improper infrastructure, lack of resources, and more. Tackling such issues is no easy feat but is nevertheless important, as these types of challenges often influence company culture and attitudes, which can greatly impact to what extent FAIR is adopted and maintained. In all, establishing a culture in which FAIR can be properly and fully implemented is essential to ensuring that the principles can survive and thrive in an organization.  

It’s no surprise that companies that foster data citizenship and stewardship are better suited in driving data centricity and adopting FAIR. A data citizen is an employee that is given access to propriety information and/or data from their organization. In exchange, data citizens assume certain levels of responsibility in working with and handling data, following established corporate guidelines and rules. In large organizations, having these policies explicitly and clearly defined is key, along with establishing data stewardship policies that oversee each stage of the data lifecycle, from creating to archiving, to ensure that data is accessible and that quality is maintained.  

Technology to serve a purpose  

In being caught up in driving data centricity and FAIR, an organization can easily get caught up in building out technology and tools that might end up further complicating data management and governance.  

Leadership teams play a large role in establishing initiatives and strategies and building adequate infrastructure that champion data and FAIR practices. However, the top-down push to build out technology for data can be dangerous without the correct considerations. These pieces of technology are only tools to solve a problem, they are not solutions within themselves, so an organization must first consider which purposes and goals their new technology or tool will solve. While this might seem obvious, it’s important to keep in mind just how easy it can be to get lost amongst the hype of latest and greatest, even for industry giants.  

Succeeding in driving data centricity requires an organization to think concretely about what can actually be generated with their data – explicitly, what use cases can be addressed with the data and said new technology? Additionally, organizations need to consider the value case behind the technology or tool they want to bring in, and how it can be used to create further benefits beyond any immediate needs. 

Industry experts on the FAIR data principles and data-centric and patient-centric approaches  

Hear from experts from Merck, Novo Nordisk, Roche, Novartis, and ONTOFORCE as they discuss data-centric cultures, the FAIR data principles, competitive data, the risks associated with GenAI, and more.  

This discussion took place on 4 October, 2023 at BioTechX Europe in Basel. BioTechX Europe brings together pharma, academia, and clinicians to showcase innovation and foster meaningful collaborations across the field.