When the topic of data migration comes up, many organizations avoid it like the plague. They envision the process as being painful, time-consuming and daunting.
The fact is, data migration is critical for organizations to thrive, particularly in product lifecycle management (PLM). The problem for many organizations is that migration is an afterthought in the PLM process. As a result, we have compiled some of the best PLM data migration practices for you to consider.
The point of the migration process is to evaluate what you currently have, get rid of what you don't need, and keep what's important. Sometimes it's challenging to decide what you should and shouldn't keep — especially since many companies keep mounds and mounds of data they don't need. Use this process as a "cleansing" period and evaluate your legacy data. Determine if the data is relevant for future product implementations. If it's not critical, chances are you don't need to keep it.
Many companies decide to migrate multiple PLMs into one environment. While this does make sense, you can run into major issues. Naming conflicts and data integrity can compromise the project, so consolidate as much as possible.
Many of these migration projects involve moving massive amounts of data, which creates concern about completing the migration on time. Deciding to have project phases may make sense for your organization so you don't have a looming deadline and a ton of data to move.
Migrating data can be similar to applying shampoo. You first need to test the system to ensure it's working properly. Then you need to validate the test results. Lastly, you need to repeat the test. Regardless of what the little birdie says, don't skip this process. Set testing parameters up front so everyone knows what's expected.
You are now ready to implement a successful PLM data migration project. Although these are just a few best practices, they will help you moving forward.