I don’t believe you can commit to the success of a data conversion without addressing quality (or lack thereof). Do you agree? If so, then why are there so many conversion projects that just move data from one place to another? Let me give you an example. I had a client who did this HUGE database conversion from flat files to an Oracle database (yes, the flat files had seven years of history). Here is the chain of events:

1. The business requirements were gathered, and they basically stated that the business users wanted the same reports they had now. Mistake #1: it is usually setting up a project to promise the same level of reports. Instead, offer an enhancement using words like "drill thru," "drill across," etc. This sets expectations that the new reports are BETTER, AND THEY SHOULD BE!
2. ETL platform chosen
3. Data model for new environment created with referential integrity
4. Project plan created
5. Resources acquired
6. Kick-off planned

During the requirements gathering and the data modeling, no one really addressed the quality of the data. No profiling, and no quick SQL queries to check out the data. This cost time during implementation. Here's why:

1. As soon as some data didn’t load, they dropped the referential integrity and loaded garbage
2. The garbage had to be filtered out for the reports
3. The data models had to reflect the lack of database enforced referential integrity

Planning for quality is definitely in my top 10!