IA-05
Issue Age vs Business Value — Aging High-BV Items
Issue Age
Default severity: medium
Product BacklogPrioritizationRiskEffort
What it detects
High business value items that have been open for a disproportionately long time — value that was identified and prioritised but never delivered. The intersection of high BV and high age is the most critical planning failure.
Detection formula
IF bv_field configured: high_bv_threshold = config.ia.high_bv_min // default: BV >= 4 FOR each_issue I WHERE bv(I) >= high_bv_threshold AND status NOT IN done_statuses: IF I.age_days > config.ia.high_bv_age_threshold // default: 60d THEN FLAG aging_high_bv_item IF I.age_days > UCL THEN CRITICAL Report: ranked by (bv * age_days) desc — highest combined value-time waste firstExamples in practice
- A team shows high business value items that have been open for a disproportionately long time while high bv min is set to BV >= 4.
- Example signal: High business value items that have been open for a disproportionately long time — value that was identified and prioritised but never delivered.
Suggested response
Age down old items: shorten time-in-status, clear blockers, and focus high-value ageing work.
Coaching playbook
Symptom
High business value items that have been open for a disproportionately long time — value that was identified and prioritised but never delivered. The intersection of high BV and high age is the most critical planning failure.
Why it matters
When "Issue Age vs Business Value — Aging High-BV Items" keeps appearing, the team is signalling a repeatable process gap. Left unexamined, the pattern hides where work really stalls and makes improvement metrics harder to trust.
What you can achieve
Age down old items: shorten time-in-status, clear blockers, and focus high-value ageing work.
Facilitation questions
- What system change would stop "Issue Age vs Business Value — Aging High-BV Items" from firing again?
- What do the cited issues have in common — same root cause or same workaround?
- Who owns the two-week experiment and how will we verify on the next import?
Run this rule against your own tracker data with Flow Analyzer.