Data governance and data management are two distinct yet complementary concepts that play crucial roles in how organizations handle their data. Data governance focuses on the policies, procedures, and standards that ensure data is accurate, available, and secure. It involves defining roles and responsibilities, establishing decision-making processes, and ensuring compliance with regulations. For instance, a company may implement data governance by assigning data stewards to oversee specific datasets, ensuring they are properly categorized and used according to established guidelines.
On the other hand, data management refers to the actual tasks and processes involved in storing, organizing, and maintaining data. This includes activities like data storage, data integration, data quality management, and data architecture. For example, data management involves using database systems to efficiently store customer information, ensuring that data is backed up regularly, and developing processes to clean duplicate records. While data management is concerned with the operational aspects of handling data, it does not usually set the rules or policies that dictate how data should be used.
In summary, data governance creates the framework and rules for managing data effectively, while data management is concerned with the practical implementation of those rules. Together, they help organizations maximize the value of their data while minimizing risks. For developers, understanding the difference is vital for developing systems that comply with governance policies and for implementing best practices in data handling.