Executive Delivery Dashboard — One View to Rule Them All
Built a unified executive delivery dashboard pulling from Jira, GitHub, and CI/CD pipelines, replacing 5 separate weekly reports. Reduced reporting overhead from 10 hours/week to fully automated and measurably improved leadership decision-making speed.
Challenge
Senior leadership receiving 5 different weekly reports from 5 teams with inconsistent metrics, no unified view of delivery health, and no way to spot cross-team patterns.
Solution
Designed and delivered a unified delivery dashboard pulling from Jira, GitHub, and CI/CD — surfacing velocity, cycle time, defect rate, and deployment frequency in a single view.
Result
Reporting time reduced from 10 hours/week to automated, leadership decision-making speed improved measurably.
The Problem
I was managing delivery across five product teams for a global enterprise's digital division. Every Monday morning, each team lead spent 1-2 hours compiling a weekly status report for the VP of Engineering. The five reports used different formats, different metrics, and different definitions of "on track." One team reported velocity in story points, another in tickets completed. Cycle time was measured differently across teams. Deployment frequency was not tracked at all by two teams.
The VP would spend another hour trying to synthesise these reports into a coherent picture for the CTO. Important patterns — like one team's cycle time steadily increasing over six weeks — were invisible because no one was looking across teams. Decisions about resource allocation, priority shifts, and risk mitigation were made on gut feel rather than data. The VP asked me to fix this, with a clear mandate: one view, automated, and useful enough that people actually look at it.
What We Built
I started by defining the metrics that mattered at the executive level. After conversations with the VP, CTO, and team leads, we settled on four: sprint velocity (normalised), cycle time, defect escape rate, and deployment frequency. These covered delivery throughput, efficiency, quality, and operational maturity.
Next, I mapped the data sources. Velocity and cycle time came from Jira. Deployment frequency came from the CI/CD pipelines (GitHub Actions and Jenkins depending on the team). Defect escape rate was calculated from a combination of Jira defect tickets tagged as production issues and deployment logs.
I worked with a data engineer to build automated extraction pipelines. Every night, data was pulled from Jira's API, GitHub's API, and CI/CD webhook logs into a lightweight data warehouse. We chose a simple architecture intentionally — a scheduled Python ETL job feeding a PostgreSQL database — because sustainability mattered more than sophistication.
The dashboard itself was built in Grafana, chosen because it was already in the organisation's toolstack. I designed the layout with three levels of detail: an executive summary showing all five teams at a glance with colour-coded health indicators, a team-level drill-down showing trend lines over the past 8 sprints, and a detail level showing individual sprint data.
I also added a "signals" panel that automatically flagged anomalies — a team whose cycle time jumped more than 20% sprint-over-sprint, or a defect escape rate above threshold. This turned the dashboard from a passive report into an active early-warning system.
The Outcome
The five separate weekly reports were eliminated entirely, saving approximately 10 hours per week of combined team lead and VP time. The dashboard updated automatically every morning, and the Monday leadership meeting shifted from "what happened last week" to "what should we do about what the data is telling us." Within the first month, the anomaly detection surfaced a growing bottleneck in one team's code review process that had gone unnoticed for weeks — it was resolved before it impacted delivery commitments. The CTO later adopted the same dashboard format for three other divisions.