Continuous observability across every integration in Halcomb's stack — Oracle Fusion, OIC, Salesforce, ServiceNow, banks, field telemetry. Job success is the floor. Data quality is the ceiling.
Three things to know before standup:
1. One pipeline failed — the $8M U of T invoice didn't post to Fusion AR. Root cause traced.
2. Data quality drift on Salesforce contracts — values are 18% above 30-day baseline. Could be legit growth, could be a change in how the AE team is filling in fields. Worth a 60-second look.
3. Reconciliation break between Fusion AR and PNC — 3 invoices have no matching cash receipt yet. May resolve naturally. May not.
Walk through them with me?
Three anomalies detected this week. Job success was 100% — but the data flowing through the integrations changed.
delivery_window appearing in 22% of incoming PO recordsdelivery_window is meant to drive Fusion shipment scheduling, the data is being lost in transit and Operations doesn't know.
customer_segment jumped 2% → 23% overnightNone of these are job failures — every integration ran and returned 200 OK. Job-level monitoring would not have caught any of these. Only continuous data-quality observation surfaces them.
The customer_segment drop is the most urgent — that's a real reporting break headed for your Friday close. The other two need RevOps and SF Admin context I don't have.
Salesforce closed-won → Fusion AR invoices → PNC payments. Where the chain breaks, money sits.
Three Salesforce orders never made it to Fusion AR. Two Fusion invoices never received cash. $9.2M in receivables is sitting in cracks between systems — none of it would surface at your Friday close because each system thinks it did its job.
The biggest break is the U of T order — same one I flagged on the dashboard. Let me walk you through the failure that started it all.
Run #SF-OIC-9183 · triggered 02:14 UTC by Closed-Won webhook on SF-O-89423
contract_start_date is missing on Salesforce Order SF-O-89423.Source fixed in Salesforce. Pipeline retried. Invoice posted. Reconciliation chain restored.
Integration Data Monitor watches three layers at once — pipeline health, data quality drift, and cross-system reconciliation. It catches what job-level monitoring misses entirely: schema changes, distribution anomalies, null-rate cliffs, and orphan records sitting between your systems of record.