Design principles for data quality tools

Abstract

Data quality is an essential aspect of organizational data management and can facilitate accurate decision-making and building competitive advantages. Nu-merous data quality tools aim to support data quality work by offering automa-tion for different activities, such as data profiling or validation. However, de-spite a long history of tools and research, a lack of data quality remains an issue for many organizations. Data quality tools face changes in the organizational (e.g., evolving data architectures) and technical (e.g., big data) environment. Established tools cannot fully comprehend these changes, and limited prescrip-tive design knowledge on creating adequate tools is available. In this cumula-tive dissertation, we summarize the findings of nine individual studies on the objectives and design of data quality tools. Most importantly, we conducted four case studies on implementing data quality tools in real-world scenarios. In each case, we designed and implemented a separate data quality tool and abstracted the essential design elements. A subsequent cross-case analysis helped us accu-mulate the available design knowledge, resulting in the proposal of 13 general-ized design principles. With the proposal of empirically grounded design knowledge, the dissertation contributes to the managerial and scientific commu-nities. Managers can use our results to create customized data quality tools and assess offerings at the market. Scientifically, we address the lack of prescriptive design knowledge for data quality tools and offer many opportunities to extend our research in multiple directions. The continuous work on data quality tools will help them become more successful in ensuring data fulfills high-quality standards for the benefit of businesses and society.