Data management refers to the overarching processes involved in collecting, organizing, and accessing all data within an organization. Master data management is a subset focused on core business entities and their characteristics – details for customers, suppliers, products, assets, and more.
Data governance is a set of practices, policies, and rules that ensure data is consistent, trustworthy, and compliant – and that it doesn’t get misused. A governance framework creates an organizational structure and names the people who are responsible for safeguarding specific types of data. Data governance and master data management are complementary: the rules from data governance are attached to the master data (as well as all other business data).
Master data management is focused on creating and then maintaining master data across the enterprise. It covers the process of enhancing, merging, and removing duplicates in order to improve data quality. Master data integration, on the other hand, is tasked with moving master data around and harmonizing it (regardless of quality) so that it can be viewed holistically across all applications. A master data integration layer allows for end-to-end process integration, gives line of business applications a consistent view of data, and ultimately, reduces the cost and effort of sharing data.