High-quality data is key to interpretable and trustworthy data analytics and the basis for meaningful data-driven decisions. In practical scenarios, data quality is typically associated with data preprocessing, profiling, and cleansing for subsequent tasks like data integration or data analytics. However, from a scientific perspective, a lot of research has been published about the measurement (i.e., the detection) of data quality issues and different generally applicable data quality dimensions and metrics have been discussed. In this work, we close the gap between data quality research and practical implementations with a detailed investigation on how data quality measurement and monitoring concepts are implemented in state-of-the-art tools. For the first time and in contrast to all existing data quality tool surveys, we conducted a systematic search, in which we identified 667 software tools dedicated to “data quality.” To evaluate the tools, we compiled a requirements catalog with three functionality areas: (1) data profiling, (2) data quality measurement in terms of metrics, and (3) automated data quality monitoring. Using a set of predefined exclusion criteria, we selected 13 tools (8 commercial and 5 open-source tools) that provide the investigated features and are not limited to a specific domain for detailed investigation. On the one hand, this survey allows a critical discussion of concepts that are widely accepted in research, but hardly implemented in any tool observed, for example, generally applicable data quality metrics. On the other hand, it reveals potential for functional enhancement of data quality tools and supports practitioners in the selection of appropriate tools for a given use case.

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applied in the field of manufacturing and production is needed. Therefore, we have conducted a systematic literature review as an attempt to characterize the state-of-the-art in this field, i.e., by identifying existing research and by identifying gaps and opportunities for further research. We have focused on finding the primary studies in the existing literature, which were classified and analyzed according to four criteria: bibliometric key facts, research type facets, knowledge graph characteristics, and application scenarios. Besides, an evaluation of the primary studies has also been carried out to gain deeper insights in terms of methodology, empirical evidence, and relevance. As a result, we can offer a complete picture of the domain, which includes such interesting aspects as the fact that knowledge fusion is currently the main use case for knowledge graphs, that empirical research and industrial application are still missing to a large extent, that graph embeddings are not fully exploited, and that technical literature is fast-growing but still seems to be far from its peak.

The main challenges are discussed together with the lessons learned from past and ongoing research along the development cycle of machine learning systems. This will be done by taking into account intrinsic conditions of nowadays deep learning models, data and software quality issues and human-centered artificial intelligence (AI) postulates, including confidentiality and ethical aspects. The analysis outlines a fundamental theory-practice gap which superimposes the challenges of AI system engineering at the level of data quality assurance, model building, software engineering and deployment. The aim of this paper is to pinpoint research topics to explore approaches to address these challenges.