Cycle time is creeping up. Predictability is collapsing. Nobody can say why.
Why your delivery behaves the way it does, traced from the data, ranked by influence on the outcomes you commit to.
- System dynamics
- Leading indicators
- Sensemaking
The problem in depth
Most delivery dashboards count activity, not health. Tickets moved, sprints closed, story points pointed. They tell you what happened, never why, and they pile up faster than anyone reads them.
Meanwhile cycle time drifts upward, predictability collapses, and senior leaders make capacity decisions on instinct. The signals that would inform those decisions exist, but they live in different tools, on different cadences, owned by different people.
A Delivery Health Obeya is the room (physical or virtual) where those signals come together, ranked by the influence they actually have on the outcomes you commit to.
"A healthy delivery engine delivers value quickly and predictably. That means value flows to your users quickly, and you can confidently forecast when to expect it."
From the delivery health data model
What I deliver
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Causal map
A system-dynamics diagram of 23 leading indicators flowing through 4 intermediate measures into 4 outcome metrics, traced from your data.
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Correlation table
Each leading indicator ranked by its measured influence on cycle time and predictability, with confidence bands and the months of data behind it.
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Agile maturity grid
A 1-to-5 read of practice strength across estimation, sizing, capacity-informed commitment, dependency clarity, and progress tracking.
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Experiment roadmap
A quarterly plan of interventions sequenced by expected impact and difficulty, framed as testable bets with clear success measures.
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Quarterly sensemaking
Workshops, interviews, and narrative analysis that turn the numbers into a story your teams agree with.
How I work
A quarterly rhythm: pull the data, build the diagnostic, run sensemaking sessions with the teams whose work is in scope, agree the experiments worth running, then revisit a quarter later to compare the story to the data. The Obeya is the artefact, the cadence is the value.
Worked example
A 1,200-person agile org, 9 tribes, worsening predictability. The diagnosis surfaced WIP-per-engineer and approval-wait-time as the two highest-correlation drivers of cycle time. Six experiments were sequenced for the next two quarters; predictability recovered over the following six months without a structural reorg.
Discuss a delivery health obeya engagement.
A 30-minute conversation is enough to know if we should keep talking.