Data Management (DM) is a large and encompassing process at the enterprise level. It is work that involves the planning, development, implementation, and administration of systems for the acquisition, storage, and retrieval of data.
According to Cindy Hauer, Data Management is the “process for establishing and maintaining verification and validation of a product’s requirements, associated management data, and traceability of the life cycle development and operation.”
Since Data Management involves all of the data, not just the deliverable data, it is crucial for companies to employ a good DM process. Both users and customers rely on accessing quality data quickly.
DM touches every aspect of an organization. However, many people think DM is just about holding the data, but it is more than that. A good DM process will keep track of data that evolves throughout the lifecycle of the project.
With more digital data being used, it is vital for DM to handle digital data. To maintain relevancy, DM must stay current with technological changes.
According to Adrienne Tannenbaum, President of Database Design Solutions, Inc.,“For the most part, data management’s objective is to standardize and reuse accurate data, focusing on the set of data that is clearly ‘corporate’ in that it does not change across departments and applications. Examples of such data are ‘customer data’… ‘employee data’… ‘product data’ and so on.” Tannenbaum emphasized the fact that data management is at the enterprise level, and she underlined the importance of having accurate data. DM is only as good as the data that goes into it.
