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Cloudingo Becomes First Data Quality Application to Provide Undo and Restoration of Merged Salesforce Records

Symphonic Source, a leader in data management and data quality tools today announced a new feature to Cloudingo, its data quality orchestration product for Salesforce. The new feature, called Undo and Restore, is the first of its kind to allow the restoration of Salesforce records that were merged together.

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Managing data can be error-prone, especially when deduping and cleaning records. Cloudingo’s Undo and Restore feature gives users peace-of-mind when merging duplicate Salesforce records. If a merge is done due to a user or configuration error, users now have the ability to recover records and return them to their original state. Cloudingo is the only Salesforce data quality application with this capability. Before this release, merged Salesforce records could not be unmerged. Deleted records can be reinstated from the Salesforce Recycle Bin within a limited time frame, but object relationships of reinstated records are not restored.

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The Undo and Restore feature, which is available to Cloudingo Enterprise users starting today, works by first backing up and encrypting records and files that are part of a data quality operation in Cloudingo. These can be stored in Cloudingo or in AWS (Azure support coming soon) Merged records can be restored individually, while all relationships, metadata, lookups, and attached objects are retained and re-associated with the restored records.

“The introduction of this new feature is the answer to our number one request from users – the ability to undo a merge,” said Lars Nielsen, CEO of Symphonic Source. “When it comes to data quality, oftentimes people are so fearful that they’ll merge the wrong records that they avoid doing anything at all. And they’re right – no matter how meticulous and careful users are with their configurations, mistakes do happen. We’re excited to give users more confidence and a less risky way to eliminate duplicate Salesforce records and automate data quality across the enterprise.”

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