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In today's data-driven landscape, organizations across the life sciences industry are grappling with the challenge of extracting meaningful insights from their ever-growing data assets. The stakes are high: data is critical to unlock speed and efficiencies across the drug discovery timeline, driving success, productivity, and a competitive edge. This is where the FAIR data principles come into play, offering a framework to make data findable, accessible, interoperable, and reusable to ultimately facilitate data reuse at scale for humans and machines.
But beyond the technical benefits, key questions remain: What is the business value of FAIR, and how do you measure its return on investment (ROI)?
In December 2024, ONTOFORCE hosted a FAIR fireside chat with FAIR experts from the life sciences industry. The discussion focused on FAIR’s business impact, practical advice on FAIR implantation, and how to measure and report on FAIR’s value and ROI. In this article, we’ll build on this discussion to explore the business case for FAIR with insight from our panelists.
Before understanding the value FAIR drives for a business, it’s important to consider the inverse: what costs are an organization incurring due to a lack of FAIR data?
According to a 2019 European Commission report , the lack of FAIR data leads to inefficiencies and other challenges, costing the European economy at least €10.2 billion annually. When factoring in its effects on economic turnover, research quality, and machine readability, this cost rises to €26 billion per year.
For a business, costs incurred due to a lack of FAIR data can be difficult to concretely quantify. They often relate to these elements:
A company lagging with FAIR data is not necessarily doing so willingly. There are barriers hindering FAIRification for organizations, with budget often topping the list, and cultural aspects and infrastructure capabilities trailing shortly behind. It’s important to understand what barriers exist in order to better tackle them to get started with FAIR in the first place. Further, understanding these hurdles is important so that they don’t stand in the way of the value FAIR can deliver.
A company’s culture plays a crucial role in the successful implementation and realization of value from FAIR data principles. If an organization fosters a data-driven mindset, where data is treated as a strategic asset rather than a byproduct, FAIR adoption becomes more seamless. Leadership commitment, cross-functional collaboration, and employee engagement are key factors that determine whether FAIR principles are integrated into daily workflows or remain an abstract concept.
John Apathy, Chief Solutions Officer at XponentL Data and former Vice President of Digital/IT for Research and Early Development at Bristol-Myers Squibb shares the following insight on influencing culture to realize value with FAIR:
“You need to win the hearts and minds of the organization. In this way, it’s about doing things the right way, not doing something extra. It should be clear cut: this is the way we work; we bake FAIR into everything. We adopted this slogan: we're becoming data-centric, not system-centric. That was a recognition that the IT organization was very much oriented on a system or a project with a beginning, a middle and an end. When you pivot to data centric, there is a full life cycle that endures.
This work is a team sport. There are a lot of participants. IT has a role, R&D participants have a role, data scientists have a role. There are many players and it’s important to understand the roles and how they each contribute to a data-centric strategy. It is worth thinking through for an organization: How do you have to change research? How do you have to change data science? How do you have to change any specific function to achieve this outcome?”
Getting to FAIR requires many resources, both technical and human. On the technical side, infrastructure can be a compounding variable. investigating the costs and benefits of FAIR implementation in pharma R&D, highlights that many pharma leaders are concerned with the infrastructure needed to implement FAIR. A well-integrated internal infrastructure was deemed essential for FAIR implementation by the leaders participating in the report. This is due to inconsistencies across existing internal systems. Respondents highlighted various internal IT applications, such as identifier systems, ontology services, and storage databases, as key enablers of FAIR principles. However, they also noted that these applications often lack compatibility, requiring a sophisticated design to ensure seamless integration and effective data reconciliation. This type of infrastructure comes with costs, not only for implementation but additionally for the manpower needed to run and maintain.
Valerie Morel, Chief Executive Officer at ONTOFORCE advises: “Infrastructure is essential for FAIR but implementing additional tools only for the sake of FAIR can be dangerous. Companies should seek out enterprise-grade solutions that can help drive FAIR and business goals simultaneously and importantly, don’t require additional tools or solutions added on top to truly enable FAIR.”
Gentiana Spahiu Pina, Director, Data Governance Lead at Pfizer believes that an inability to properly showcase FAIR’s value is holding organizations back when it comes to rolling FAIR out, she says: “Part of the reason that we're almost 10 years into the fair data discussion is because we're still trying to find ways to articulate the value of FAIR.”
Beyond the dollar amount that FAIR will save an organization, champions of FAIR should be prepared to articulate the value of FAIR in a well-rounded way that addresses a multitude of factors and goals. After all, FAIR does more than just save an organization money.
The business value of the FAIR data principles can be understood through several key dimensions that drive tangible outcomes for organizations: efficiency, resource optimization, AI, and collaboration. Understanding how these dimensions play into FAIR’s value can help FAIR champions build their case.
On a basic level, the FAIR principles streamline data findability and access, reducing the time spent searching for and preparing data. Enabling FAIR data translates into faster, better quality data analysis over broader datasets, enabling accelerated project timelines.
In practical terms, this means:
On top of this, research into FAIR data indicates that implementing FAIR data management can possibly increase the efficacy of drug research and development. In fact, some studies reported that the availability of FAIR data for primary and secondary use can potentially reduce the time needed to bring a drug to market.
One of the hidden costs in large organizations is the redundant purchase of data simply because existing datasets are hard to locate. FAIR data minimizes these inefficiencies by making data easily discoverable and reusable. Organizations could be spending thousands repurchasing datasets they already own. FAIR eliminates this waste, creating direct cost savings.
Additionally, by enabling data reusability, FAIR can also power insights that contribute to cost savings. For example, a researcher might be able to repurpose existing data enabling him to forego additional testing or research. As John Apathy puts it:
“FAIR enables the generation of new insights because data is interoperable and available to be acted upon and in novel ways. You can generate a new insight that might allow you to not have to run a control arm or test whether an animal model is truly predictive and maybe you can drop it from your screen. These are saving months and, or millions of dollars.”
The rise of AI and machine learning in life sciences has made high-quality, well-governed data more critical than ever. There is no AI without well-governed data. FAIR ensures that data is ready for AI, enhancing model performance and reliability. By providing structured, interoperable data, FAIR accelerates the development of predictive models and supports robust AI applications. Gentiana Spahiu Pina explains:
“FAIR primes data for AI use by enabling trust and quality. What are the implications of putting data that we don't trust the quality of or we don't know the provenance of into an AI model? What is the liability of that? FAIR plays a very critical role in terms of building the credibility for the data sets that are inputted into models.”
The FAIR data principles break down silos within organizations, enabling cross-functional teams to collaborate more effectively. They also facilitate more efficient external partnerships, as standardized, interoperable data can be shared with academic institutions, CROs, and regulatory bodies with ease. This openness fosters a culture of innovation, where data becomes a strategic asset that drives new discoveries and business growth.
Measuring the ROI of the FAIR data principles is challenging because the benefits often accrue over time and span multiple departments, making direct attribution difficult. Additionally, intangible gains such as improved collaboration, enhanced data quality, and accelerated innovation can be harder to quantify compared to more immediate cost savings. There are, however, still a few key strategies to consider that can help demonstrate ROI:
1. Align with strategic business goals
To demonstrate ROI, tie FAIR initiatives to the company’s strategic objectives. For example, if a company is focused on accelerating drug discovery timelines, show how FAIR could specifically speed up certain R&D processes to gain back time. This could look like accelerated target identification or validation through better data integration and streamlined access to high-quality datasets thanks to FAIR processes.
When FAIR is positioned as an enabler of business-critical goals, its value becomes undeniable. As Gentiana Spahiu Pina warns, be cautious of hollowed out FAIR initiatives that aren’t aligned with higher priorities and goals:
“One thing that I have found that is not very well received is doing FAIR for the sake of doing FAIR. We have to be very mindful of that. It has to be in service of a business strategy. Whether it's new drug candidates, whether there is M&A activity that needs operational efficiencies, whatever those business drivers are, that will help prioritize what data assets need to be FAIR-ified first. Start with what you're trying to enable. What are you trying to accomplish?
2. Start small, show quick wins
Begin with targeted use cases or proofs of concept (POCs) to demonstrate immediate value. A strong use case will clearly define the FAIR initiative’s parameters, ensuring the scope stays narrow enough to properly measure efforts, resources, and benefits.
A use case in clinical trial design can be a good place to start. Enabling reuse of existing clinical data assets can inform smarter clinical trial design with improved site selection, streamlined eligibility criteria, and more. This type of data-driven clinical trial design can lead to faster clinical timelines and reduced costs for recruitment, providing a compelling case for broader FAIR adoption.
Michel Dumontier, a distinguished professor at Maastricht University and co-found of the FAIR data principles believes in the power of use cases:
“Use cases can help an organization make breakthroughs on their challenges with FAIR, from the technical architecture of where the data are going to be stored and served up, to exactly how people are going to find and reuse it, and even how to credit data producers for putting assets in the repository. So, I'm a firm believer in having a few concrete use cases to first work through the entire process and then to add more. At Maastricht University, we now have hundreds of thousands of people who are doing FAIR data as a result of six use cases that we started off with. In my consulting work with pharma, I see that it's very similar."
3. Track key metrics
Metrics are essential to quantify the impact of FAIR. Consider tracking:
To give a concrete example, a study on a FAIRification project launched at a large company looked into time-saving facilitated by the implementation of a FAIR platform for over 3000 users. Based on usage activity after two months, a projection for the year was made: the platform provided better search results that would enable a time-savings worth 3.5 full-time employees.
Hear directly from the FAIR experts at Pfizer, XponentL Data, Maastricht University, and ONTOFORCE as they share their insights and FAIR implementation strategies. Watch on-demand now.
