Towards an End-to-End Data Quality Optimizer

To provide good results and decisions in data-driven systems, data quality must be ensured as a primary consideration. An important aspect of this is data cleaning. Although many different algorithms and tools already exist for data cleaning, an end-to-end data quality solution is still needed. In this paper, we present our vision of a well-founded end-to-end data quality optimizer. In contrast to many studies that consider data cleaning in the context of machine learning, our approach focuses on various scenarios, such as when preprocessing and downstream analysis are separated. Our proposed adaptive and easily extend-able framework operates similarly to proven methods of database query optimization. Analogously, it consists of the following parts: Rule-based optimization, where the appropriate data cleaning algorithms are selected based on use case constraints, optimizer hints in the form of best practices, and cost-based optimization, where the cost is measured in terms of data quality. Accordingly, the result is a data cleaning pipeline that provides the best possible data quality. The choice of different optimization goals enables further flexibility, e.g. for environments with limited resources.