Princeton Information’s data migration process is based on years of experience involving interactions between CRM platforms and legacy systems. We first evaluate the existing data to determine what is to be moved and what is to remain behind, not all data will make the final migration. We look to ensure that after the data is transformed and residing on the new platform that it is properly integrated within the overall enterprise data environment.
Data Migration Process
Before you start the migration process you need to establish a plan that includes which sources of data are to be used and it what sequence in order to drive the overall migration process. This includes a look at existing CRM systems as well as related data sources that help enhance the data quality and enrichment.
Once this process is established the solution needs to be architected so that it is repeatable and easily refined as you learn more about the data transformation needs.
It is also important to develop a communication plan that involves business and technology leaders to ensure enrollment of resources.
The build process typically involves reporting and analysis tools to support the actual data transformation and migration process. Since CRM migration encompasses multiple sources from across the organization, there are usually a series of data conversion routines that need to be developed and continuously refined.
As the data converges onto a single platform there are often conflicts in data intgrety that will require validation and further evolution of business rules.
The migration process should be built in such a way that components and data feeds can be reused and rerun over a period of time with the appropriate back-out and data recovery practices in tact.
The run portion involves the execution of data transformation and data migration routines, and data validation exercises that include technicians and data owners, such as business users.
A feedback mechanism should be in place in the event of potential data corruption being detected or the need for possible adjustments in data conversion routines as the new data interacts with production data.