The Translation Layer
You don't need expensive Business Intelligence (BI) tools to track business health. You can use your existing observability stack — Prometheus, Datadog, Splunk, Grafana — to track Business KPIs (Key Performance Indicators). A KPI is a measurable value that indicates how effectively a company is achieving a key business objective.
The key insight is that every business event leaves a technical footprint. A completed purchase is an HTTP POST to /checkout that returns 200. A new user is a row inserted into the users table. A subscription renewal is a successful call to the payment gateway. You just need to map those technical events to business value.
| Technical Event | Business KPI |
|---|---|
| HTTP POST /checkout (200 OK) | Order Placed |
Sum of cart_total field in logs |
Revenue per Minute |
| HTTP POST /signup (200 OK) | New User Acquisition |
| Payment Gateway 4xx Error | Revenue at Risk |
| HTTP GET /search returning 0 results | Search Failure Rate |
| Session ended without purchase | Cart Abandonment Rate |
Why Track Business Metrics in Your Observability Tool?
Because correlation is everything. If you track Revenue per Minute in Datadog alongside deployment events and CPU usage, you can instantly see that revenue dropped 30 seconds after a deployment at 14:32. That correlation — visible on a single dashboard — reduces Mean Time to Detect (MTTD) for business-critical issues from hours to seconds.
Without this, the sequence looks like this: a product manager notices sales are down in the BI dashboard (lag: 1 hour), files a ticket, an engineer investigates logs (lag: another 30 minutes), and eventually correlates it to a deployment. With business observability, the on-call engineer sees the revenue graph drop in real time, alongside the deployment marker, and rolls back within minutes.
Choosing the Right Granularity
Business metrics need to be tracked at the right time resolution. Aggregating revenue hourly is fine for reporting but useless for incident detection. For alerting purposes, track business metrics at 1-minute or 5-minute resolution — fine-grained enough to catch a problem before it compounds, but not so granular that noise becomes a problem.
A common SRE interview question is: "How would you detect a broken checkout flow that doesn't produce any errors?" The answer is business-level instrumentation: track orders_per_minute as a metric derived from successful checkout events. A sudden drop — even with zero HTTP errors — immediately signals a problem. This demonstrates you think beyond infrastructure health.