Decision Velocity Audit: Where time goes

Flow

Five taps → score /100, tier, modeled time/cost bands, benchmark table, optional email for the PDF.

What each question measures

• Q1 — Decision cycle: Elapsed time from “we need data” to “decision made” (proxy for speed).
• Q2 — Data sources: How many systems you pull from (fragmentation / reconciliation load).
• Q3 — Team size: Full-time + regular contractors (scales cost of rework via a coordination factor). It does not change your velocity score — a large org can still score 100 if signals are healthy.
• Q4 — Post-decision discrepancies: How often bad numbers surface after a decision (rework frequency + score).
• Q5 — Leadership timeliness: How recently leadership got a same-day answer when asking for a metric (signal freshness).

Score (max 100)

Points are fixed per answer; best answers on Q1, Q2, Q4, and Q5 sum to 100. Q3 is excluded from the score.

Question	Topic	Points (best → worst)
Q1	Decision cycle	35 / 28 / 20 / 10 / 0
Q2	Data sources	25 / 18 / 10 / 0
Q4	Discrepancies after decision	25 / 18 / 10 / 0
Q5	Same-day metric for leadership	15 / 10 / 5 / 0

Diagnosis tiers

Tier	Score	Meaning (headline)
Strong	80–100	Velocity signals look healthy; estimates show residual friction at your team size.
Mixed	55–79	Real friction — cycles, sources, or post-decision surprises; cost scales with team size.
At risk	0–54	Heavy decision tax; high rework and leadership lag; especially costly as you scale.

Time & money model (illustrative)

Q4 → monthly friction-event count. Per event, person-hours = (decision cycle capped at 6 active hours + small overhead per extra source) × team factor (headcount^1.4). Annual hours capped, × $50/hr, shown as ±25%. Recovery ≈ 60% less chasing once data is centralized. Illustrative only.