Every statement correction starts with bad data that nobody caught. The worst place to find a data problem is after the statement has been delivered — by then you're already negotiating with a frustrated rep, writing an apology email, and reprocessing a payout. Upstream data validation is the single highest-leverage ops investment because it prevents problems in a stage where fixing them is cheap.
This checklist walks through 12 specific data-readiness gates grouped into four categories: source integrity, reconciliation, coverage, and timing. Each item is weighted by its observed correlation with downstream statement corrections. The output gives you a readiness score, the specific gates you haven't passed, and tailored guidance on fixing each gap before next cycle.
The four categories — and why they're weighted differently
Source integrity (highest weight)
If your source data is wrong, nothing downstream can save you. These gates validate that the raw deal records coming out of CRM match what was actually signed — correct amounts, correct customer, correct rep assignment, correct close date. In Falcon's experience, ~60% of statement corrections trace back to source integrity issues because every downstream calculation amplifies the error. Higher weight reflects impact.
Reconciliation (high weight)
Your comp data must agree with finance's revenue numbers. When sales-recognized revenue doesn't match finance-recognized revenue, you're paying commission on a number that will be contested at audit. Reconciliation gates check booked-vs-billed, ARR-vs-TCV, and revenue-recognition alignment. Medium-high weight because issues here become audit problems, not just rep problems.
Coverage (medium weight)
Every deal must be assigned to a rep — and every rep's deals must be captured. Orphan deals (no rep) and missing deals (rep should have them but they're not in the feed) are both coverage problems. These are medium-weight because they typically surface quickly when the affected rep notices.
Timing (lower weight)
Data freshness and cycle-close alignment. Less impactful per-incident than the other categories but worth catching because late-arriving data causes out-of-cycle statement corrections, which are the most annoying type of correction to explain to reps.
12 gates, each assigned a weight from 1–3 based on downstream-correction correlation. Check the boxes for gates you've genuinely passed; leave unchecked for gates you haven't. Your score is the sum of passed weights divided by total possible weight, expressed as a percentage. Bands: Ready (≥90%), Mostly Ready (≥75%), Risky (≥50%), Not Ready (<50%). The output highlights unchecked gates ranked by weight so you fix the most damaging ones first.
Data Readiness Checklist
Check each gate you've genuinely passed. Unchecked = gap to close.
ℹ️ How this checklist works +
The question it answers: Is my comp data ready to calculate on this cycle, which specific gates am I failing, and how serious are those failures?
What to do:
- Walk through all 12 gates. Check a box only if your team has genuinely verified it this cycle — don't check aspirationally.
- Each gate has a visible weight (1 / 2 / 3) reflecting its downstream impact.
- Click Score Readiness when done.
Weight scale:
- Weight 3 — source integrity gates. A failure here cascades into most downstream errors.
- Weight 2 — reconciliation and coverage. Failures create finance-facing or rep-facing issues that must be resolved before close.
- Weight 1 — timing and freshness. Failures cause inconvenience but rarely cause incorrect payouts.
What you'll get back:
- A 0–100% readiness score with a band: Ready (≥90%) / Mostly Ready (≥75%) / Risky (≥50%) / Not Ready (<50%).
- A ranked list of unchecked gates — the ones you should close first to reduce downstream correction risk.
- Tailored guidance per gap on how to close it.
Run this checklist at the start of every close cycle. It takes 5 minutes to walk through and saves hours of statement corrections later.
How to act on your score
Ready (≥90%) — Proceed to calculation
All high-weight gates are passed. Expected statement-correction rate is <2%. You can run the calc cycle with high confidence. Even at this band, keep the checklist as a standing ritual — regression is easier than you think.
Mostly Ready (75–89%) — Address gaps, then proceed
A few medium-weight gates failed. Close them before running the official calc — it's cheaper to fix upstream (hours) than to issue corrections downstream (days). If you must proceed without closing all gaps, document the known risk in writing so reps aren't surprised by corrections later.
Risky (50–74%) — Stop and reconcile
Multiple high- or medium-weight gates are failing. Running the calc will produce statements with a 10–20% correction rate. Invest 1–2 days closing the top-ranked gaps before proceeding — the SalesOps time is fractional vs the correction work you'll avoid.
Not Ready (<50%) — Do not calculate
You don't have the data quality to produce defensible statements this cycle. Options: delay the cycle (costs: one-time rep frustration), issue preliminary statements with an explicit "draft" watermark (costs: reputation), or pull in additional resources to close gaps (costs: SalesOps overtime). Running the calc as-is is the worst choice — you'll pay in corrections for the next 3 cycles.
The only way this checklist fails is if someone checks boxes aspirationally. "We usually verify X" is not the same as "we verified X this cycle." Treat each checkbox as a statement under oath — either you passed the gate this cycle (check) or you didn't (leave blank). Partial credit is not a thing in data readiness.
Struggling with data readiness?
We help SalesOps teams build automated pre-cycle validation so the checklist becomes automatic. Book a 20-minute review of your current process.
Book a 20-minute consultation →FAQ
Yes — most can be automated as pre-cycle validation scripts. Source integrity checks map to SQL queries against your CRM data warehouse. Reconciliation checks can run against finance's closed-period ledger. Automation shifts the checklist from "5-minute ritual" to "green/red dashboard on cycle day" — the end state for mature teams.
Check it as passed (the gate is conceptually satisfied because it's not relevant). But before checking, verify the gate truly doesn't apply. Many teams dismiss gates that actually do apply but haven't been implemented yet — that's a failure, not a non-applicability.
The Cycle Time Estimator measures elapsed time across the cycle. This checklist measures data quality at one specific stage (pre-calculation). Use them together: the Estimator tells you how long your data-prep stage is taking; this Checklist tells you whether that stage is actually producing quality output.
12 covers the high-leverage gates without becoming a tickbox exercise. Teams we've worked with who had 30+ item checklists reported lower compliance rates ("nobody reads the whole list") and equal or worse data quality outcomes. The weight system means you can effectively add granularity to the highest-impact gates without adding checklist items.