How an master data management project should progress part 1

An Master Data Management project plan will be influenced by requirements, priorities, resource availability, time frame, and the size of the problem. In view of the above statement, Most Master Data management projects should include the below mentioned phases:

• ·         Sources of master data should be identified. This step is usually a very revealing exercise. Some companies find out that they have dozens of databases containing customer data that the IT department did not know existed. This step generally saves millions in capital.
• ·         The consumers of the master data need to be identified. After determining which applications produce the master data identified in the first step, and generally more difficult to determine which applications use the master data. Depending on the approach you use for maintaining the master data, this step might not be necessary. For example, if all changes are detected and handled at the database level, it probably does not matter where the changes come from.
• ·         Collect and analyze metadata about for your master data. For all the sources identified in step one, what are the entities and attributes of the data, and what do they mean? This should include attribute name, datatype, allowed values, constraints, default values, dependencies, and who owns the definition and maintenance of the data. The owner is the most important and often the hardest to determine. If you have a repository loaded with all your metadata, this step is an easy one. If you have to start from database tables and source code, this could require significant effort.