Methods and techniques for data quality improvement of (linked) (open) data

Good decisions need good data. Hence, only by exploiting good data it is possible to make effective decisions. The goodness of data is usually related to the task they will be used for. However, it is possible to identify some task-independent quality dimensions which are merely related to the data themselves. In order to improve the intrinsic data quality, we propose a proactive approach. Our goal is to offer data providers (and consumers) a set of methods and techniques to guide them in assessing and improving the quality of data they are interested in. We mainly focus on Linked (Open) Data. Since the published data might also contain personal data, there is the need to make the data set compliant with the General Data Protection Regulation (GDPR). Therefore, besides quality problems, we are also interested in discovering any privacy breach and - if needed - in proposing corrective actions. The final goal is to give data providers the possibility of publishing better data. The proposed approach is pragmatic. Thus, we will not only design but also implement it. We plan to wrap it into a social platform, already used by several public administrations, which enable us to test the applicability of the proposed methods in real settings.