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“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.”
Data aggregation errors in merged HubSpot dashboards stem from inconsistent data relationships, conflicting filter logic, duplicate record counting, or mismatched object associations. HubSpot’s limited error reporting makes troubleshooting difficult without direct access to underlying data and calculation logic.
The solution is implementing comprehensive troubleshooting through transparent data access and systematic debugging approaches.
Implement systematic debugging with complete data visibility
CoefficientHubSpot’sHubSpotprovides comprehensive troubleshooting capabilities by importing raw data from all objects feeding your merged dashboard using multi-object import capability. You can create diagnostic columns, build comparison tables, and implement automated data quality checks with complete visibility into calculation logic, unlikeblack-box aggregation process that makes error diagnosis difficult. This approach works across bothintegrations.
How to make it work
Step 1. Import raw data and create diagnostic tracking.
Use Coefficient’s multi-object import to pull raw data from all HubSpot objects feeding your merged dashboard. Create diagnostic columns showing record counts, unique identifiers, and data quality indicators for each import, then build comparison tables contrasting aggregated totals from merged dashboards against individual dashboard totals.
Step 2. Identify and resolve common error patterns.
Address duplicate counting using Coefficient’s association handling with “Primary Association” settings to avoid counting records multiple times across related objects. For missing records, apply identical filtering logic using advanced filtering to ensure complete data capture. Fix date range mismatches by creating standardized date filtering with dynamic cell references.
Step 3. Build systematic debugging tools.
Create data lineage tracking showing exactly which records contribute to each aggregated metric and build validation formulas that identify orphaned records or broken associations. Use Coefficient’s snapshot feature to capture data states before and after troubleshooting changes for comparison analysis.
Step 4. Implement ongoing error prevention.
Set up automated data quality checks using spreadsheet functions to flag aggregation anomalies as they occur. Create reconciliation reports comparing your calculations against HubSpot’s native aggregations, and establish Coefficient alerts to notify you immediately when aggregation errors reoccur or new issues arise.
Resolve aggregation errors with complete diagnostic visibility
Start buildingHaving complete visibility into data and calculation logic enables effective troubleshooting that’s impossible with HubSpot’s limited error reporting.merged dashboards with comprehensive error detection and resolution capabilities.
