Implementing data governance in microservices architectures is crucial yet challenging due to the distributed nature of these systems, which can lead to inconsistencies, compliance risks, and security vulnerabilities. This approach involves frameworks and strategies that balance microservices' autonomy with centralized control to maintain data quality and regulatory compliance. Key components include clearly defined data ownership, metadata management, data quality standards, and compliance and security controls. Effective governance models such as federated governance, data mesh, and API-centric governance are highlighted, emphasizing the importance of automation, metadata tools, and CI/CD integration to ensure consistent governance across growing microservices ecosystems. These efforts help organizations preserve agility while responsibly managing data, supported by modern tools and practices like DataOps and continuous governance, which are integrated into development lifecycles to maintain robust governance standards without hindering development speed.