CASE 160 · IVY · 2024
DORA metrics, computed from systems engineering already uses.
A fintech CTO wanted DORA metrics — deploy frequency, lead time, MTTR, change failure rate — without standing up a separate observability vendor. We built the dashboard from GitHub Actions, CloudWatch, and PagerDuty data the team was already producing.
Fintech
PLATFORM
2024
RESULTS
What changed, by the numbers.
METRICS PUBLISHED
4 DORA + 6 CUSTOM
DATA-PIPELINE COST
< $200 / mo
WEEKLY UPDATE LAG
< 1h
BENCHMARK POSITION
HIGH PERFORMER
HOW IT WENT
The CTO had been asked for DORA metrics by the board. The internal-platform team had been pricing observability vendors and finding the cheapest option at $24k/year — for metrics the company already had in raw form across three systems.
A Lambda pipeline pulled GitHub Actions workflow runs (deploys), CloudWatch metric data (production health), and PagerDuty incidents (MTTR), normalised them into a DynamoDB table, and rendered Grafana dashboards. Custom metrics extended the four DORA basics with team-level breakdowns the CTO valued.
Pipeline cost steady-state under $200/month. Metrics update inside an hour of the underlying event. The board got their dashboard; the company classifies as a "high performer" by the DORA Accelerate framework. The team’s engineering retros now reference the deploy-frequency trendline.
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