Integrating Data Quality in Industrial Big Data Architectures: An Action Design Research Study

In today's data-driven business environments, organizations heavily rely on high-quality data to make informed decisions and gain a competitive advantage. Organizations typically use big data architectures to store, process, and manage their exponentially growing enterprise data. However, ensuring data quality in such scenarios remains a significant challenge for many organizations. Despite the vast number of data quality tools available, integrating such tools into big data architectures has not been fully explored. In this study, we aim to formulate design principles to support systematically incorporating data quality testing into big data architectures. For this purpose, we performed an action design research study at a large organization in the Netherlands. Finally, we employed the architecture trade-off analysis method (ATAM) to evaluate our solution design.