Data governance is the set of rules, roles, and processes that control how an organization manages, secures, and uses its data. It defines who is responsible for data quality, who can access specific data, and how that data gets used across the business.<\/p>\n\n\n\n
Poor data quality is not a minor inconvenience. Gartner has found that poor data quality costs organizations millions of dollars every year<\/a> in wasted decisions, lost opportunities, and unnecessary operational costs.<\/p>\n\n\n\n This guide breaks down what data governance actually means, its core components, and the practices that separate a program that works from one that exists only on paper.<\/p>\n\n\n\n\n\n Data governance is the discipline of managing data through defined policies, processes, and accountable roles, so that data stays accurate, secure, and usable across an organization.<\/p>\n\n\n\n It combines two sides of the same problem. The systems side determines how data is stored, moved, and protected, similar to the technical work behind customer data integration<\/a>. The people side determines who develops policies, who owns which data, and who is accountable when something goes wrong. Neither side works well without the other, since even the best technology fails if no one owns the decisions about how it gets used.<\/p>\n\n\n\n Data governance sets the policies and accountability for data, while data management is the operational work of actually storing, organizing, and moving that data day to day.<\/p>\n\n\n\n Governance answers “who decides and why,” while management answers “how does it actually get done.” A useful comparison:<\/p>\n\n\n\nWhat is data governance?<\/strong><\/h2>\n\n\n\n
How is data governance different from data management?<\/strong><\/h2>\n\n\n\n