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Commerce Data Observability: The Missing Layer in ERP + Ecommerce Integrations

Your ERP-to-ecommerce integration is a black box. You find out about failures when customers complain. Here's how to build the observability layer that catches problems before they cascade.

February 6, 2026

Here’s a question: Is your ERP-to-ecommerce integration healthy right now?

Not “was it working yesterday.” Not “it seemed fine last time I checked.” Right now, at this moment, can you tell me whether:

  • Orders are flowing from ecommerce to ERP?
  • Inventory updates are syncing within expected latency?
  • Customer pricing is being applied correctly?
  • No errors are piling up in a queue somewhere?

If you can’t answer that question in under 60 seconds—with data, not assumptions—you have an observability problem.

And observability problems become business problems. Usually at 2 AM on a Saturday.

Why Commerce Integrations Are Black Boxes

Most B2B ecommerce integrations are built to work. They’re not built to be observable.

This is understandable. When you’re implementing an ERP integration, the priority is getting data to flow. Making it reliable. Meeting go-live. Observability feels like a nice-to-have.

But here’s what happens without observability:

Silent failures accumulate. An API starts returning 503s. The retry logic backs off and eventually gives up. No error is logged because the code handles failures “gracefully.” Three days later, you discover 400 orders never made it to the ERP.

Latency creeps up. The inventory sync used to take 5 minutes. Now it takes 45 minutes. Nobody noticed because nothing alerted. Customers see stale inventory and place orders for products you don’t have.

Partial updates go undetected. 90% of products sync correctly. 10% fail validation and are silently skipped. Those products show old prices or incorrect availability for weeks until someone manually compares systems.

Debugging becomes archaeology. When something does break visibly, you have to reconstruct what happened from scattered logs across multiple systems—if the logs still exist.

What Good Commerce Observability Looks Like

Observability isn’t just monitoring. Monitoring tells you whether something is broken. Observability tells you where, when, and why.

For commerce integrations, good observability means:

1. Integration Health at a Glance

A single dashboard that shows:

  • Sync status for each data type (inventory, pricing, orders, customers)
  • Latest successful sync time (and whether it’s within expected window)
  • Current queue depths (are items backed up?)
  • Error rate over time (trending up? stable? zero?)
  • Latency percentiles (median, 95th, 99th)

You should be able to open this dashboard in the morning and know, in 30 seconds, whether integrations are healthy.

2. Anomaly Detection and Alerting

Thresholds alone aren’t enough. “Alert if queue > 1000” fails when your queue is normally at 50—1000 is already a catastrophe.

Better approach: Alert on deviation from normal. If inventory sync usually completes in 5-10 minutes but today it’s been running for 45, that’s an alert—even if 45 minutes isn’t technically a “failure.”

Key alerts for commerce integrations:

  • Sync hasn’t completed in [N] hours
  • Error rate exceeds [X]% of transactions
  • Queue depth 3x higher than rolling average
  • Latency exceeds 2x baseline
  • Zero transactions processed in the last hour (possible silent failure)

3. Structured, Searchable Logs

Logs that say “Error processing order” are useless. Logs that say:

level: error
service: order-sync
order_id: ORD-12345
customer_id: CUST-9876
erp_response_code: 500
erp_response_body: "Customer credit hold"
timestamp: 2026-02-06T13:22:47Z

Those are useful. You can search. You can correlate. You can build dashboards.

Structured logging isn’t hard to implement, but it requires discipline at development time. Every log event should include:

  • Transaction ID (order ID, sync batch ID)
  • System context (which integration, which direction)
  • Outcome (success, failure, partial)
  • Timing (duration, timestamps)
  • Error details (codes, messages, stack traces for dev environments)

4. Distributed Tracing

When an order flows from ecommerce → middleware → ERP → WMS → shipment notification → customer, you need to trace that journey.

Distributed tracing assigns a correlation ID at the start and propagates it through every system. When something goes wrong, you can query: “Show me everything that happened to order ORD-12345” and see the complete timeline.

Tools like Jaeger, Zipkin, or cloud-native solutions (AWS X-Ray, Google Cloud Trace) handle this. The key is instrumenting every integration touchpoint.

5. Reconciliation Checks

Observability isn’t just about watching transactions in flight. It’s also about verifying outcomes.

Reconciliation checks compare state across systems:

  • Do order counts in ecommerce match order counts in ERP for today?
  • Does sum of inventory across ecommerce equal sum across ERP warehouses?
  • Are there customers in ecommerce without matching ERP records?

Run these daily. Alert on discrepancies above threshold. Small discrepancies often indicate integration bugs before they become visible.

The Observability Stack for Commerce Integrations

You don’t need to build from scratch. Here’s a typical stack:

Metrics: Prometheus + Grafana, or cloud-native (CloudWatch, Datadog, New Relic)

Logs: ELK stack (Elasticsearch, Logstash, Kibana), or managed (Datadog Logs, Splunk, Sumo Logic)

Traces: Jaeger, Zipkin, or cloud-native (AWS X-Ray, Google Cloud Trace)

Alerting: PagerDuty, Opsgenie, or built-in alerting from your metrics platform

Synthetic monitoring: Periodic health checks that exercise the integration (not just “is the API up?” but “does the full sync flow work?”)

For iPaaS platforms (Celigo, Boomi, Workato), use the platform’s built-in monitoring plus export to your centralized observability stack. Don’t rely solely on the iPaaS dashboard—you want single-pane visibility.

What to Observe: The Commerce Integration Checklist

Inventory Sync

  • Last successful sync timestamp
  • Sync duration trend
  • Records processed vs. records expected
  • Error count by error type
  • Inventory variance (sum across systems)

Order Flow

  • Orders placed vs. orders transmitted
  • Order transmission latency (time from placement to ERP receipt)
  • Transmission failures by error type
  • Order status update latency

Pricing Sync

  • Last pricing update timestamp
  • Cache hit/miss rate for customer pricing
  • Pricing API latency
  • Pricing errors (wrong price displayed vs. charged)

Customer Sync

  • New customers created today (both directions)
  • Customer data mismatches
  • Failed sync attempts

General Health

  • Queue depths for all message queues
  • Processing throughput (transactions/minute)
  • Integration service uptime
  • Dependency health (ERP available? WMS available?)

Building Observability Into Existing Integrations

If you have integrations running without observability, you can retrofit. Here’s how:

Week 1: Add Health Check Endpoints

Every integration service should expose a /health endpoint that returns:

  • Service name and version
  • Dependency status (can I reach the ERP? can I reach the message queue?)
  • Last successful processing time
  • Current queue depth (if applicable)

Wire these to your monitoring platform.

Week 2: Implement Structured Logging

Audit your integration code. Replace unstructured log messages with structured ones. Add transaction IDs, timing, and outcome data.

If you’re using an iPaaS, enable detailed logging and configure log export to your central platform.

Week 3: Build the Dashboard

Create a single dashboard that shows health status for all integrations. Start simple:

  • Green/red status for each integration
  • Last sync time
  • Error rate (last 24 hours)
  • Link to detailed logs

Week 4: Configure Alerting

Define thresholds and anomaly detection rules. Start with the most critical alerts:

  • Any integration down > 15 minutes
  • Error rate > 5% of transactions
  • Sync latency > 2x baseline

Week 5-6: Add Reconciliation

Build daily reconciliation checks. Compare counts and sums across systems. Alert on discrepancies.

Week 7+: Continuous Improvement

Review alert fatigue. Tune thresholds. Add missing coverage. This is ongoing.

The Business Case for Observability

Observability feels like infrastructure work. It doesn’t ship features. It doesn’t close deals.

But here’s what it prevents:

Customer-facing outages: You catch the inventory sync failure before customers order products you don’t have.

Revenue leakage: You detect pricing errors before you sell $10,000 at the wrong price.

Labor waste: Your team stops spending hours debugging problems you could have caught in minutes.

Customer trust erosion: You stop saying “we’re investigating” and start saying “we caught it early and it’s already fixed.”

Calculate the cost of your last integration incident. The labor to debug. The customer refunds. The trust damage. Now imagine catching it 2 hours earlier—or preventing it entirely.

That’s the ROI.

Ready to add observability to your integrations? We build observability into new integration projects and retrofit it into existing ones. Schedule a discovery call to discuss your stack and what monitoring should look like.

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