FAIR is not enough -- A Metrics Framework to ensure Data Quality through Data Preparation

Abstract

Data-driven systems and machine learning-based decisions are becoming increasingly important and are having an impact on our everyday lives. The prerequisite for this is good data quality, which must be ensured by preprocessing the data. For domain experts, however, the following difficulties arise: On the one hand, they have to choose from a multitude of different tools and algorithms. On the other hand, there is no uniform evaluation method for data quality. For this reason, we present the design of a framework of metrics that allows for a flexible evaluation of data quality and data preparation results.