Almost every IT professional has experienced a data conversion nightmare. Data conversion projects encompass a little bit of everything - requirements gathering to understand the source and target data, some database modeling to better understand the data, data cleanup work to refine the data and custom development in the form of transformation tools. You have the classic quality/scope/cost trade-off decisions that need to be made in project management.
In a perfect world, all organizations would have enterprise data models and conversions would not be necessary. Because this is rarely the case, data conversions are a great opportunity to resurrect and reinforce all business rules. However, data assessment and data cleanup efforts can be time-consuming and expensive - two things that will make it difficult to include in the scope of the data conversion project. The project sponsor and the users should be aware of the risks associated with omitting this step. It doesn't make much sense to convert poor quality data because nothing can alienate users more than poor quality data. Ultimately, the business case for a data assessment will depend on the level of data quality necessary to support the business purpose of the application.
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