A huge part of data management is data migration. Fortunately, in many cases, this is a pretty straightforward process and nothing goes wrong. However, this is not always a guarantee. When it comes to proper management, adequate data re-engineering and migration are paramount for risk mitigation and complete data safety.
You can talk with any data professional and see that mistakes happen. This is why you have to be prepared and you should avoid the really common ones that are mentioned below.
Improper estimation of time and effort needed
If you do not plan properly, you will fail. This is true with practically everything you do in business and data management is not different. A migration project usually involves cooperation between different teams. The project plan has to take into account all applications and objects involved. You have to think about the best solution so that you can maximize the use of time and other resources. Moving data can be much more time consuming than what many believe.
Doing everything at the same time
There are numerous migration projects that stall right now because there is an attempt to do way more than optimal at the same time. For instance, you might have a database migration underway while another team starts deploying codes. If this happens, it tends to lead to a disaster as it is close to impossible to figure out if problems happened because of the migration, code, hardware and so on.
Junk data migration
When migration happens, it is a wonderful time for you to clean out data and remove what is no longer needed. This means you archive everything in a proper way. Remember that data always lasts a lot longer than code. Do not ignore it.
When performing a migration move, evaluate data value and volume. Quality checks are also necessary before the project is launched.
Improperly understanding tools used
Various migration techniques and tools are now available. You can easily end up thinking that the tools you have are all that you need right now. Usually, issues with dependencies appear. You can end up thinking that migration is very simple and you quickly figure out that some objects were not taken into account, like the triggers.
No performance baseline
When you do not know how fast a specific query was before the data migration, you do not know how fast it will or should be after the process is complete. This is a huge hurdle that often appears with cloud migrations. The baseline allows you to see if everything was done properly or not.
Lack of a rollback plan
Rollback rarely happens when data migration is properly done. However, this does not mean that you should not be ready for such a scenario. In various cases, we see a migration that leads to problems and the rollback should be used. Unfortunately, there is no rollback point established or the team simply wants to solve the current problem instead of moving back to the stable version.