Data governance is a data management concept that addresses the risk versus value of data across an organization from ingestion to analysis. Data governance is policy-driven to manage regulatory compliance, data quality, and data access. Good data governance means getting the right data to the right people at the right time to drive faster time to insights.
Data governance policies and procedures practices are underpinned by intelligent technology. Examples of technology enablers to enforce said policies include metadata-driven data lineage tracking, automated masking of sensitive information, role-based access to information to ensure trusted data is available to trusted data consumers.
Data governance is focused on mitigating risk while improving data accuracy. Example use cases include GDPR and CCPA compliance for data privacy, role-based access to information to foster collaboration and self-service for data consumers and masking sensitive financial or medical information. Good data governance is growing in importance as demand for multisource data and associated insights is growing.
Data governance is one of the tenets of a DataOps practice. DataOps (intelligent data operations) is a methodology: a technological and cultural change to improve your organization's use of data through better collaboration and automation. That means improved data trust and protection, shorter cycle time for your insight's delivery, and more cost-effective data management.