  The General Data Protection Regulation (GDPR) came into force in 2018. After
this enforcement, many fines have already been imposed by national data
protection authorities in Europe. This paper examines the individual GDPR
articles referenced in the enforcement decisions, as well as predicts the
amount of enforcement fines with available meta-data and text mining features
extracted from the enforcement decision documents. According to the results,
three articles related to the general principles, lawfulness, and information
security have been the most frequently referenced ones. Although the amount of
fines imposed vary across the articles referenced, these three particular
articles do not stand out. Furthermore, a better statistical evidence is
available with other meta-data features, including information about the
particular European countries in which the enforcements were made. Accurate
predictions are attainable even with simple machine learning techniques for
regression analysis. Basic text mining features outperform the meta-data
features in this regard. In addition to these results, the paper reflects the
GDPR's enforcement against public administration obstacles in the European
Union (EU), as well as discusses the use of automatic decision-making systems
in judiciary.
