Cross-System Data Reconciliation for Executive Reporting
Revenue booked in the CRM, revenue recognized in the ERP, and revenue reported to the board frequently disagree by some amount, because each system captures the number at a different point in the process and under slightly different rules — the CRM counts a deal the moment it's marked closed-won, the ERP recognizes it once invoicing rules are satisfied, and the gap between those two events is where discrepancies live. Reconciling this manually before every board or investor report means someone pulling exports from three systems and eyeballing differences under a tight deadline, and a real discrepancy that should have been caught sometimes slips through simply because there wasn't time to check every line.
STARTING PRICE
From €799
Complex tier · Multi-system orchestration, custom logic, and higher-volume or higher-risk processing.
Get a quote →Saves roughly 6-10 hrs per reporting cycle of manual cross-system checking plus fewer embarrassing number disagreements surfacing in front of the board.
How the automation works
We pull the same underlying figures — revenue, bookings, headcount, whichever metrics feed executive reporting — from each source system on a schedule ahead of reporting deadlines, and reconcile them against each other using known timing and rule differences between systems, so a discrepancy that's expected (the CRM-to-ERP recognition lag) doesn't get flagged the same way as one that's genuinely unexplained. Genuine mismatches get a likely root cause attached where the data supports it — a deal recorded in CRM but missing a corresponding ERP entry, a currency conversion applied inconsistently — and route to finance for resolution with enough lead time before the reporting deadline that a real error gets fixed rather than reported and then quietly corrected in the next cycle.
Process flow
- 01
Pull the same figures from each system integration
Revenue, bookings, and other reported metrics are pulled from CRM, ERP, and finance systems on a schedule ahead of the reporting deadline.
- 02
Normalize for known timing and rule differences ai
Known, expected differences between systems — recognition timing, currency conversion rules, booking versus invoicing triggers — are applied before comparison.
- 03
Compare and flag genuine discrepancies ai
Remaining differences after normalization are flagged as genuine discrepancies, distinct from expected timing gaps already accounted for.
- 04
Attach a likely root cause ai
Where the data supports it, each discrepancy gets a likely cause attached — a missing corresponding entry, an inconsistent currency conversion, a manual adjustment not reflected everywhere.
- 05
Route to finance with lead time before reporting output
Flagged discrepancies route to finance with enough lead time before the reporting deadline to resolve genuine errors rather than report and correct later.
Inputs
- CRM revenue and bookings data
- ERP financial and revenue recognition data
- Known cross-system timing/rule difference rules
- Executive reporting deadline calendar
Outputs
- Normalized cross-system comparison
- Flagged genuine discrepancy list with likely cause
- Pre-reporting reconciliation summary
- Discrepancy resolution audit trail
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Normalizing for 'known' timing differences requires those differences to actually be documented and current — if the CRM-to-ERP recognition rule changes and nobody updates the normalization logic, every reconciliation run starts producing false-positive discrepancies for a gap that's now expected, or worse, silently absorbs a genuine new discrepancy into a stale normalization rule.
- A discrepancy that looks like a data error might actually be a legitimate manual adjustment made in one system for a valid business reason (a negotiated credit applied in the ERP that has no CRM equivalent) — root-cause diagnosis needs to check for documented manual adjustments before assuming something is broken.
- Reconciling right before a hard reporting deadline leaves little time to actually investigate and fix anything nontrivial that gets flagged — the schedule needs enough lead time built in that a genuine, complex discrepancy can be traced and corrected, not just discovered and reported alongside a caveat.
- Currency conversion timing differences between systems that update exchange rates on different schedules create small, expected discrepancies on any multi-currency reconciliation, and treating every currency-driven gap as a real error rather than tolerance-banding for expected FX noise generates constant low-value flags that erode trust in the reconciliation.
Frequently asked questions
Does this replace our finance team's month-end close process?
No, it's specifically focused on reconciling figures across systems before they're reported to executives or the board, which is a narrower and more time-sensitive check than the full month-end close.
How does it distinguish an expected timing gap from a real error?
Known differences in how systems recognize and record figures — like CRM booking versus ERP revenue recognition timing — are normalized out first, so what's left flagged is a genuine, unexplained discrepancy rather than routine system-to-system lag.
What if a discrepancy is actually a legitimate manual adjustment?
Root-cause diagnosis checks for documented manual adjustments before treating a discrepancy as an error, since some gaps reflect valid business decisions applied in one system but not automatically reflected in another.
How far ahead of the reporting deadline does this run?
Early enough to leave real investigation time for anything nontrivial that's flagged, rather than running the check the morning of the deadline when there's no time left to fix a genuine problem.