There are two types of master data management platforms available today: those with prebuilt data models developed by the software vendor and those that are model-driven, where the implementing company constructs the data model based on its requirements. In the second scenario, project team members from business and IT work together to create data definitions and business rules; this process defines the MDM data model.
One could debate the merits of which is better. However, the ability to model data is a critical skill set for a successful data migration effort, regardless of whether the MDM platform has an out-of-the-box data model or uses a model-driven approach. As an example, your MDM initiative may require that you create a canonical model, which is a design pattern used to communicate between different data formats. You may need to map your different legacy systems' data to that canonical model so you can smoothly move your customer, product or other types of master data into your new MDM hub.
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