Data migration is more than just moving records from one system to another. It is, in fact, a challenging and time-consuming task that demands ample planning along with a good understanding of a current system. Poor data mapping can lead to eventual loss of important data and critical functionality. There can also be unexpected effects of existing data and rules after migration. All this can become even more troublesome when you are migrating into a running instance that is being used by several users as opposed to a new one. Therefore, even when importing thousands of records, you have to be sure that all records have been migrated completely.
The factors that need to be considered during data migration may vary depending on the complexity of your source data. However, for any data set, you can avoid having to constantly go back and forth to make sure all data is imported by following the standard protocol of planning, migrating and testing. While these steps are necessary, equally important is avoiding the following:
1 — Planning without involving stakeholders.
You might know the bits and pieces of data migration but stakeholders are the ones fully familiar with data structure and flow. Only they can tell exactly what kind of issues can arise in migration. You might need to make modifications in fields or validation rules or even your overall approach based on their feedback.
2 — Importing related Custom Objects in Random Order.
You might always import accounts before contacts or opportunities as it is reinforced everywhere, taking care of ordering of custom objects in accordance with relationships is equally important as the standard objects. Along with ordering of objects, you will need to include fields of the same object multiple times into other objects, to make sure that all the lookup relationships are set up.
3 — Importing large data in Production Environment in the first go.
Even if you have planned the migration well and cleansed data beforehand, importing all data in a production environment the very first time is never recommended. It is preferable to migrate a small subset of your data in a sandbox so that all issues are exposed at an early stage. This will allow you to make any required fixes easily. You might have to repeat this step several times until all issues are fixed.
4 — Mapping irrelevant data fields
While it might be necessary to add fields like Legacy Id or User Id in your source data, getting rid of unnecessary fields that are used by only very few records is an important step in the mapping process.
5 — Forgetting to review active workflows and triggers.
Prior to data import, you must check if you need to disable any active workflows or triggers that affect the objects in migration. It is possible that you end up sending hundreds or thousands of unwanted emails to customers as your data is uploaded if workflows or triggers are still active. You should also see if any validation rules need to be added, modified or deleted.
6 — Not testing in the end.
The execution of data loader without errors is not a guarantee that all your data has been migrated properly. A good way to test is by running reports or using developer console to check the record counts. You can apply filters or perform SQL queries to check the number of records for various objects.
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