FAIR metadata

FAIR data adds significant value. According to the European Union, the cost annual of lacking FAIR research data amounts to €10.2 billion. This calculation can be applied to any research organization and can easily be adapted for non-research data as well. Regardless of the type of organization, this article describes a compelling case for investing in data FAIRification.

In alignment with our article on FAIR data, our sets of metadata (orange loop in figure below) support not only FAIRification but also extend to business contextualization (yellow loop in figure below) enabling additional business intelligence.

What we did

As shown in red boxes in the figures below, our methodology includes two crucial steps:

    1. the representation of your data in a graph format
    2. its annotation with predefined FAIR metadata

This approach offers three main benefits:

  1. It does not disrupt routine business/data operations
  2. It allows the FAIRification of both legacy and future data
  3. It connects the FAIRification process to the value it generates (using our “Business value monitoring” ontology)

 

Here is a graph version of the FAIR principles and what it entails for business and technology stakeholders (GO FAIR and Swiss National Science Foundation).