Keynote@CBI-EDOC2025 - The impact of the FAIR principles on Enterprise Architecture: A critical reflection by Luiz Olavo Bonino, University of Twente/University of Leiden

The impact of the FAIR principles on Enterprise Architecture: A critical reflection

His idea in this keynote is to connect FAIR and Enterprise Architecture. May these same principles applied to data also be applied in other dimensions of the enterprise?

History

After the FAIR Principles paper in 2016, it was amazing the speed the principles were spread. A G7 summit in China cited it in a report in 2017; the EU established that new projects need to make sure they adhere to FAIR Data Principles, although they do not explain "how" FAIR data should be, nor how this may be achieved.

The paper in 2016 did not make explicit what the principles were, presenting clear motivation behind them, but not detailing them too much. In 2020, they published a second paper clarifying the meaning of the principles.

FAIR is meant for machines -- How we can make systems deal with the data in a FAIR way

One thing that is important is to understand how much is technical and how much is social. Some social agreements need to be made to make FAIR work. All that is in red in the slide below requires social commitment and decision-making

According to FAIR, computational agents should be able to answer questions like: How can I get more information abou tit? What type of object am I dealing with? What can be doen with it? What am I allowed to do with it?

Based on a model similar to the Internet Hourglass, they want to establish what is in the center of the below Hourglass:

On the Application Layer, they are working on the FAIR Data Train

The idea is not to provide a sytem to which everyone should adhere. But rather define some agrements on:

• metadata access
• metadata format
• minimal metadata schema
• declared semantic data models
• data visiting/interaction API

FAIR Data Train Architecture

On the Data Layer, they have done work on FAIRification

The main idea behind FAIRification is to create a process to allow people to make data FAIR. They have also been tryint to make sure FAIR Data Points are themselves FAIR:

Again, this work regards a set of specifications rather than a system (although they do have a reference system as an example of how this may be achieved).

A good example of FAIR Repository is the Rare Diseases Virtual Platform Index, powered by a FAIR Search Engine.

Another example is the OntoUML Repository, which is being curated according to FAIR Principles.

On the Business Layers, they advocate FAIR prescribes the need for new services, roles, capacities etc. For example, he talked about the importance of FAIR Data Stewards to do the work that many times overload researchers and other personnel within the business and academic environment