Definition
Data Integration Metrics is a Setup page that displays performance and health metrics for data integration rules and processes running in the org. It shows success rates, error counts, processing times, and record volumes for integrations that enrich or synchronize Salesforce data with external sources.
Real-World Example
The admin at DataSync Corp checks Data Integration Metrics to monitor the nightly geocoding integration that enriches Account records with latitude and longitude data. She sees that the last run processed 2,000 records with a 98% success rate, and the 40 failed records had invalid addresses that need manual correction.
Why Data Integration Metrics Matters
Data Integration Metrics is a Setup page that displays performance and health metrics for data integration rules and processes running in the Salesforce org. It provides visibility into success rates, error counts, processing times, and record volumes for integrations that enrich or synchronize Salesforce data with external services. For example, if the org has geocoding integration rules enabled (which automatically populate latitude and longitude fields based on address data), the metrics page shows how many records were processed, how many succeeded, how many failed, and the reasons for failures. This monitoring capability is essential for maintaining data quality in orgs that rely on automated enrichment.
As organizations enable more data integration rules — for geocoding, address standardization, and other enrichment services — monitoring their health becomes critical. A silently failing integration can cause downstream problems: sales territory assignments based on stale geocodes, shipping estimates calculated from incorrect addresses, or analytics reports built on incomplete data. Data Integration Metrics provides the early warning system that administrators need to catch failures before they compound. Best practice is to check the metrics page after each integration run, set up alerts for error rate spikes, and maintain a log of processing volumes over time to detect degradation patterns. Organizations that ignore integration metrics often discover data quality issues only when business users report obviously wrong data — by which point hundreds or thousands of records may be affected.
How Organizations Use Data Integration Metrics
- DataSync Corp — DataSync Corp's admin checks Data Integration Metrics every morning to monitor the nightly geocoding integration that enriches Account records with latitude and longitude data. She sees that the last run processed 2,000 records with a 98% success rate, and the 40 failed records had invalid addresses that need manual correction. She exports the failed records list and assigns them to the data quality team for cleanup.
- Meridian Logistics — Meridian Logistics enabled address standardization integration rules and used Data Integration Metrics to validate the results. The metrics showed that 85% of addresses were successfully standardized but 15% returned errors due to international address formats not supported by the service. This insight led them to implement a separate address validation step for international accounts before running the integration.
- Apex Real Estate — Apex Real Estate noticed through Data Integration Metrics that their geocoding success rate dropped from 99% to 72% over two weeks. Investigation revealed that a data migration had imported Account records with PO Box addresses, which cannot be geocoded. They created a validation rule to prevent PO Box entries in the billing address field and corrected the migrated records, restoring the success rate to 98%.