Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization. It is a crucial part of implementing IT systems that run business applications and provide analytical information to help drive operational decision-making and strategic planning. Database management systems (DBMS) are data maintenance systems that are used to automate or monitor this process. Relational DBMS rely on the SQL programming language to structure and connect data, while NoSQL databases are better suited for unstructured data.
Data integration is the practice of ingesting, transforming, combining and provisioning data where and when it is needed. Organizations need to set goals to speed up the collection and storage of data, but also perform periodic checks to check its accuracy so that the data is not out of date or out of date in any way that could adversely affect the analyses. Data governance is primarily an organizational process; there are software products that can help manage data management programs, but they are an optional element. A robust approach to data warehousing is critical to good data management.
Red Hat has developed a new complementary service, Red Hat OpenShift Database Access, which makes it easier for administrators to provision and manage access to several third-party database services. If an organization does not have a well-designed data architecture, it can end up with isolated systems that are difficult to integrate and manage in a coordinated way. The unique needs of any organization practicing data management may require a combination of some or all of these approaches.