Data governance and data management are related but distinct concepts that focus on different aspects of handling data. Data governance refers to the policies, standards, and processes that define how an organization ensures data quality, security, compliance, and accountability. It’s about setting rules and assigning roles (like data stewards) to oversee how data is used and protected. Data management, on the other hand, involves the technical execution of those policies—the tools, workflows, and practices used to collect, store, process, and deliver data efficiently. While governance is the “what” and “why” of data handling, management is the “how.”
A key difference lies in scope. Data governance establishes guardrails. For example, a governance policy might require sensitive customer data to be encrypted and accessible only to authorized teams. Data management then implements this by choosing encryption tools, designing access controls in a database, or automating audit logs. Governance often involves cross-functional collaboration (legal, compliance, IT) to align data practices with business goals, while management is more operational—developers might build pipelines to transform raw data into usable formats, optimize database queries for performance, or set up backup systems.
Another distinction is in ownership. Governance defines who is responsible for data accuracy or compliance (e.g., a data steward approving datasets for use). Management focuses on the technical execution of those responsibilities. For instance, a developer might write scripts to validate data quality checks (management) based on rules defined in governance. Governance also addresses broader concerns like regulatory compliance (e.g., GDPR), while management tackles day-to-day tasks like database scaling or ETL pipeline optimization. Both are essential: without governance, data becomes inconsistent or risky; without management, governance policies can’t be enforced effectively.