Data Spaces must preserve sovereignty and privacy while ensuring FAIR (Findable, Accessible, Interoperable and Reusable) principles. To do so, policy-based strategies have to be developed in order to describe the agreements reached in the Data Space. In this context, two open questions arise: how to define the right Data Space policies, as well as, how to enforce (and monitor) them. Despite the efforts towards defining and enforcing data access and usage policies, there is no solution to operationalize the enforcement of those considering data quality dimensions. However, data quality is becoming a hot topic due to the surge of federated learning and alternative analytical techniques, which require all providers to guarantee a data quality threshold in order to learn robust models. Currently, we have means to describe policies related to data quality rules (e.g., by combining standards such as ODRL and standard vocabularies) but we are missing means to elicit these policies from data providers and enforce them while preserving the data sovereignty. In this paper, we discuss the challenges and open questions that must be addressed in order to operationalize (and eventually, automate) data quality in Data Spaces, which span from requirements elicitation to data validation.

This work has been partially supported by the EU HORIZON program under GA.101135513 (CYCLOPS) and by CIAICO/2022/019 project from Generalitat Valenciana.

Peer Reviewed