7 Common mistakes in Salesforce Migration!

Data migration in Salesforce is, without a doubt, one of the processes requiring a ton of effort. Planning is important because it will help save time during the transfer process. Moreover, incorrect data mapping will result in inconsistencies, leading to data integrity issues.

Having procedures set in place will help eliminate mistakes and issues from occurring during the migration process.

The following are seven common mistakes in Salesforce migration and some advice for prevention:

Undetailed planning
A detailed discussion of the plans with the stakeholders is vital to familiarizing and understanding the entire data migration process. The stakeholders can provide the information you need regarding the business side of things. As such, you will be in a better place to assess and take action accordingly.

Importing data carelessly
Common data migration practice involves importing Accounts, Contacts, and Opportunities in this order. The confusion begins with custom objects. We recommend importing these objects according to their related fields.

Importing unnecessary data
Data cleanup before the import is crucial because it reduces data size. We recommend importing to a sandbox before production so you will be alerted about issues at a more recent stage.

Inconsistencies in the process
Identifying and defining processes is a crucial step of the entire procedure. Salesforce offers so much capacity of the box. It is up to the user to take advantage of these features. Recognizing and defining these tasks and processes takes you a step further.

Mapping unnecessary fields
Checking and validating data fields prior to data migration is important. Some are no longer significant, while others hold redundant values. Erasing Id values is also important because these values change per environment.

Lack of workflow review
Inactivating any workflows before beginning the data migration is another important step to remember. Skipping this step may cause issues during the migration.

Skipping tests
We recommend running tests after a successful data load. Doing quality checks using reports or the developer console ensures everything is accurate.