Summary
We understood the importance of data quality to the data mesh and defined data quality and all of its attributes. We dived deeper into understanding these attributes and the methods of capturing them. Data quality can be implemented in multiple ways, e.g., centralized, decentralized, and hybrid. We looked at these approaches. Finally, we explored a few examples of third-party tools available for implementing data quality frameworks.
In the next chapter, we will discuss master data management—yet another important component that brings consistency to the analytical products of the data mesh.
