A data management framework is a model of the people, processes, and policies needed to successfully manage business data. It brings together the elements required to deliver great data to the business. Enterprise data managers are typically database administrators, IT administrators, or IT project managers. They are responsible for managing the entire lifecycle of their company's data, from documenting and directing the flow of data from ingestion to controlling the process of deleting data that is no longer needed.
This lifecycle is also known as a data lineage. The first step in the enterprise data management process is to complete a data audit. Once the data has been cataloged, it must be cleaned and transformed into a standard format. This ensures that companies can easily find and analyze data for internal analysis and make well-informed decisions and strategies. For more than a decade, professionals have been discussing the differences between master data management (MDM) and enterprise data management (EDM).Effective EDM helps organizations transfer data to other business applications, processes, and users with relative ease.
The person leading the data management exercise will list the data that was produced, used, or deleted during the course of a business process. It is important to clearly define the roles and rules of your enterprise data management program and decide how involved your IT department and database administrators will be. Documented data management procedures ensure transparency for the rest of your organization and their integrity should be carefully considered. By making enterprise data management a top priority, organizations ensure that data is stored in a safe place and available to users when they need it. This also offers an internal benefit in terms of reducing the time spent on new data regulation. There are many tools that data managers can use to begin their work even before it starts at the organization level.
Poor data quality affects everything from analytics and business intelligence (wrong conclusions are drawn) to employee productivity (time wasted due to rework and poor communication). A more complicated example of master data management would be creating a master file with complex categories or dimensions.
