)
Written by
Table of Contents
“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Preventing merge data loss requires proactive validation and backup strategies that go beyond HubSpot’s native capabilities. The platform’s merge interface allows manual field selection, but this becomes impractical for bulk operations.
You’ll discover how to automate data completeness analysis and create foolproof backup systems that protect your valuable information during merge operations.
Build automated data protection workflows using Coefficient
Coefficient enables robust data loss prevention through automated completeness scoring and systematic backup processes that HubSpot can’t provide natively.
How to make it work
Step 1. Create automated data completeness scoring.
Import your duplicate records from HubSpot to HubSpot and build formulas that calculate completeness scores for each record. Use =COUNTA(B2:Z2)/COLUMNS(B2:Z2) to get a percentage of populated fields. This helps identify which record should be the primary merge target based on data richness, not just creation date.
Step 2. Set up pre-merge backup automation.
Use Coefficient’s snapshot feature to capture your complete database before performing merge operations. Schedule these snapshots to run automatically before your typical merge activities. This creates recovery points that can be referenced if merge operations result in unexpected data loss.
Step 3. Build merge impact analysis reports.
Create dynamic reports that show exactly which fields would be lost in proposed merges. Import both records and use spreadsheet logic like =IF(AND(ISBLANK(B2),NOT(ISBLANK(C2))),”WILL LOSE: “&C2,”OK”) to identify populated fields in the secondary record that are blank in the primary record.
Step 4. Create conditional merge workflows.
Build workflows that flag records requiring manual review before merging. Use Coefficient’s filtering capabilities to identify high-risk merges where valuable data might be overwritten. Set up alerts when completeness score differences exceed your threshold (like when one record is 30% more complete than the other).
Step 5. Implement post-merge validation.
After merges, compare your pre-merge snapshots with current data to identify any unexpected data loss. Use formulas to automatically detect fields that were populated before the merge but are now blank, then trigger recovery procedures using your backup data.
Turn merge operations into data-safe processes
With automated completeness analysis and systematic backup workflows, you can merge records confidently without losing valuable information. These processes provide the field preservation capabilities that HubSpot’s native functionality lacks. Start building your data protection system today.
