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Data ecosystems are a novel inter-organizational form of cooperation. They require at least one data provider and one or more data consumers. Existing research mainly addresses generativity mechanisms in this relationship, such as business models or role models for data ecosystems. However, an essential prerequisite for thriving data ecosystems is high data quality in the shared data. Without sufficient data quality, sharing data might lead to negative business consequences, given that the information drawn from them or services built on them might be incorrect or produce fraudulent results. We tackle this issue precisely since we report on a multi-case study deploying data quality tools in data ecosystem scenarios. From these cases, we derive generalized prescriptive design knowledge as a design theory to make the knowledge available for others designing data quality tools for data sharing. Subsequently, our study contributes to integrating the issue of data quality in data ecosystem research and provides practitioners with actionable guidelines inferred from three real-world cases.

Data forms an essential organizational asset and is a potential source for competitive advantages. To exploit these advantages, the engineering of data-intensive applications is becoming increasingly important. Yet, the professional development of such applications is still in its infancy and a practical engineering approach is necessary to reach the next maturity level. Therefore, resources and frameworks that bridge the gaps between theory and practice are required. In this study, we developed a data engineering reference model (DERM), which outlines the important building-blocks for handling data along the data lifecycle. For the creation of the model, we conducted a systematic literature review on data lifecycles to find commonalities between these models and derive an abstract meta-model. We successfully validated our model by matching it with established data engineering topics. Using the model derived six research gaps that need further attention for establishing a practically-grounded engineering process. Our model will furthermore contribute to a more profound development process within organizations and create a common ground for communication.