Data Migration Challenges (and Solutions)

Is your data complex and cluttered? Most companies have huge volumes of data - Contacts, Leads, Accounts, Opportunities, and data cleanup is a real job that needs to be managed on an ongoing basis.  Depending on your comfort level with data projects, this may come as a good reminder of best practices, or it could save you on your next migration.  

Issue: My Source Data Is Complex - Large Volumes of data, multiple data sources, and many business partners to confer with are just some of the issues you may run into with source data. Keep in mind that it is never really as simple as moving information data from the data source to the data target, especially when analysis and configuration work needs to be considered.

Solution: Plan For Data Transformation And Data Normalization

1. Data Transformation might be needed if the data source has outdated data storage formats and different picklist values before it can be stored in the target data source.
2. Data Normalization is another aspect to consider, especially for organizations that have used the same database for several years. In such cases, the same pieces of data may be stored in several different places. Data normalization will help identify duplicate records and ensuring that only the most recent and relevant records be merged and migrated to the new system.

Issue: Data Loss or Corruption - Data loss or corruption can be a major problem for any organization. Just one missing record could harm the organization significantly. In order not to be a victim of such...

Solution: Create a series of best practices for all data migration projects

1. Rule #1 BACK UP YOUR DATA!!! Back it up! Did you back it up? When you start a data project, back up your data in multiple locations.  Our best practice is to export data, test the export file to be sure it is not corrupted, then save the files in two secure locations that are not the developer’s laptop, please.  In case the worst happens, the data can be recovered from a back-up.
2. Use various tools to help validate the migrated data - be sure that the expected data fields in the target system match what was migrated from the legacy system. 
3. Give Business Management the ownership of particular data-sets. For instance, you can have a Primary Account Manager responsible for ensuring the data for the top X accounts at the company is clean. While mapping data, this person can validate that the mapping is correct. When the migration is complete, this person can confirm that all data for the Primary Accounts was brought over correctly.

Issue: Limited Testing - Data is sensitive, complex, and data management is time-intensive.  Don’t take testing lightly.

Solution: Thoroughly Testing and Validating Data

1. Consider any data events that may have occurred in the Legacy System and try to test data on or around that time frame to ensure data quality.
2. Test a large volume of data for quality assurance. The common recommendation is 10-20% of all the data to ensure that a large bandwidth is covered. As soon as possible, start testing and make sure to do so as often as possible, preferably during the entire migration process and definitely after.
3. Load a sample set in a Sandbox and give users access to the data—the more eyes on the data, the better.

Once you’re a pro at data management, take the next step and back up your sparkly clean data; here's how. If you don't feel like a pro and need some help, don't panic, Contact Endiem, and we’ll have your Salesforce data looking spick-and-span.