How do you implement data governance and quality in a data mesh pattern?

1 Define data domain ownership

The first step is to identify and assign ownership for each data domain, based on the business context and value of the data. Data domain owners are responsible for defining the data schema, metadata, quality metrics, and access policies for their domain, as well as ensuring compliance with regulatory and ethical requirements. Data domain owners should also collaborate with other owners to align on cross-domain dependencies and interfaces.

2 Establish data contracts and protocols

The second step is to establish data contracts and protocols that enable interoperability and consistency across data domains. Data contracts are agreements that specify the format, structure, semantics, and quality expectations of the data exchanged between domains. Data protocols are rules and guidelines that define how data is produced, consumed, shared, and updated in the data mesh. Data contracts and protocols should be documented, versioned, and enforced by automated tools and processes.

3 Implement data quality monitoring and feedback

The third step is to implement data quality monitoring and feedback mechanisms that allow data domain owners and consumers to measure, report, and improve the quality of the data in the data mesh. Data quality monitoring involves collecting and analyzing data quality metrics, such as completeness, accuracy, timeliness, consistency, and validity, using dashboards, alerts, and notifications. Data quality feedback involves soliciting and addressing data quality issues, requests, and suggestions from data consumers, using feedback loops, surveys, and reviews.

4 Adopt data governance frameworks and tools

The fourth step is to adopt data governance frameworks and tools that support and facilitate the implementation of data governance and quality in the data mesh. Data governance frameworks are sets of principles, policies, roles, and responsibilities that guide and govern the data mesh. Data governance tools are software applications that help data domain owners and consumers to manage, discover, access, and use the data in the data mesh. Some examples of data governance frameworks and tools are DataOps, Data Catalogs, Data Lineage, and Data Quality Management Systems.

By following these four steps, you can implement data governance and quality in a data mesh pattern, and reap the benefits of a scalable, flexible, and reliable data architecture that supports your business goals and needs.