DataEcoSys - Data EcoSystem in Information Systems
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Paper Type
Complete
Paper Number
1394
Description
Data quality is an important aspect for the success of data ecosystems. Sharing low-quality data causes large data preparation efforts, can disrupt the chain for value co-creation, and can damage the mutual trust among partners in the ecosystem. While there are many data quality tools available in literature and practice, there is limited knowledge on the peculiarities of assessing and managing data quality in data ecosystems. In this study, we present the results of a design science research project that was concerned with the development of design principles for ensuring data quality in data ecosystems. The proposed concept extends an existing ecosystem with an artifact that technically enforces data quality checks on shared data in the manufacturing domain. Our work aims to provide the prescriptive design knowledge needed for such systems. For practitioners, we offer generalized design principles that can inform custom implementations of data quality tools in data ecosystems.
Recommended Citation
Altendeitering, Marcel; Dübler, Stephan; and Guggenberger, Tobias Moritz, "Data Quality in Data Ecosystems: Towards a Design Theory" (2022). AMCIS 2022 Proceedings. 3.
https://aisel.aisnet.org/amcis2022/DataEcoSys/DataEcoSys/3
Data Quality in Data Ecosystems: Towards a Design Theory
Data quality is an important aspect for the success of data ecosystems. Sharing low-quality data causes large data preparation efforts, can disrupt the chain for value co-creation, and can damage the mutual trust among partners in the ecosystem. While there are many data quality tools available in literature and practice, there is limited knowledge on the peculiarities of assessing and managing data quality in data ecosystems. In this study, we present the results of a design science research project that was concerned with the development of design principles for ensuring data quality in data ecosystems. The proposed concept extends an existing ecosystem with an artifact that technically enforces data quality checks on shared data in the manufacturing domain. Our work aims to provide the prescriptive design knowledge needed for such systems. For practitioners, we offer generalized design principles that can inform custom implementations of data quality tools in data ecosystems.
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DataEcoSys