PagerDuty Incident Metrics to Power BI

Overview

This flow builds a governed reliability dashboard from PagerDuty. On a schedule (every 6 hours), it reads a stored watermark, pulls incidents via the PagerDuty connector, keeps only the resolved incidents whose resolution time is newer than the watermark, computes MTTA and MTTR plus responder/escalation context, and upserts each into a Dataverse reliability fact table. It then advances the watermark and triggers a Power BI dataset refresh so the dashboard reflects the latest metrics.

Why it matters: PagerDuty Analytics is useful but siloed. Landing the facts in Dataverse lets reliability metrics (MTTA/MTTR trends by service) sit in Power BI alongside the rest of the business with consistent governance and history. The watermark makes each run an incremental load, and the per-incident upsert makes re-loads idempotent.

Status: built as a demo (Off). Going live requires only authorizing the three connections and setting the two Power BI environment-variable values - no logic changes.

Use Case

Leadership and SRE want MTTA/MTTR trends, incident volume by service, and escalation/responder context in Power BI, refreshed automatically from PagerDuty without anyone exporting CSVs. A scheduled, watermark-driven export keeps a governed, historical reliability record in Dataverse and a live dashboard in Power BI.

Flow Architecture

Environment Variables

Connectors & Connections

Connector	API name	Actions used
PagerDuty	shared_pagerduty	GetIncidents
Microsoft Dataverse	shared_commondataserviceforapps	ListRecords CreateRecord UpdateRecord
Power BI	shared_powerbi	RefreshDataset

Note — All connections are referenced as solution connection references; the flow is portable between environments as long as a connection is mapped at import time.

Customization Guide

Almost every realistic variant of this flow can be implemented by changing environment variable values. A few cases require small edits inside the flow definition — those are called out explicitly below.

Schedule

Change the Recurrence frequency/interval (e.g. hourly for near-real-time, or daily at 02:00). The watermark guarantees no gaps regardless of cadence.

First-run window

Adjust flowlibs_ReliabilityDefaultLookbackDays to backfill more or less history on the very first run.

Responder load / burnout

Add per-responder incident counts (group by assignee) for fairness and burnout analysis.

Noisy services

Rank services by incident volume in Power BI to target alert tuning.

Deeper escalation analytics

Add a per-incident log_entries HTTP enrichment to capture true escalation steps and timings.

Fabric

Point the dataset refresh at (or also land into) a Microsoft Fabric lakehouse for larger histories.

Key Expressions

The flow is intentionally light on Power Fx / WDL gymnastics — the heaviest expressions are the branch-name concatenation and the approval outcome check. They are listed below in the order they appear in the flow.

@div(sub(ticks(items('Apply_To_Each_Incident')?['last_status_change_at']), ticks(items('Apply_To_Each_Incident')?['created_at'])), 600000000)

@if(empty(coalesce(items('Apply_To_Each_Incident')?['acknowledgements'], json('[]'))), 0, div(sub(ticks(first(items('Apply_To_Each_Incident')?['acknowledgements'])?['at']), ticks(items('Apply_To_Each_Incident')?['created_at'])), 600000000))

@coalesce(first(outputs('Get_Sync_Watermark')?['body/value'])?['flowlibs_watermarkutc'], addDays(variables('varRunStartUtc'), mul(-1, variables('varDefaultLookbackDays'))))

@and(equals(item()?['status'], 'resolved'), greater(ticks(item()?['last_status_change_at']), ticks(variables('varPreviousWatermark'))))

@length(coalesce(items('Apply_To_Each_Incident')?['assignments'], json('[]')))

Customize & download