Financial Data Foundation
Canonical monthly actuals, source mapping, tie outs, and Excel ready outputs.
Focused diagnostic
Your financial KPIs break quietly.
Manual rebuilds. Drifting definitions. Forecasts no one fully trusts. KPI Audit fixes the financial metric foundation behind reporting and planning.
Symptoms
You likely need this if reporting trust is already eroding.
The problem often shows up as reconciliation work, meeting friction, manual forecast rebuilds, and KPIs that change meaning depending on the file.
Common failure points
- Forecasts require manual rebuilds.
- Reports reconcile until they suddenly do not.
- Revenue, margin, churn, CAC, utilization, or other KPIs mean different things in different places.
- Leadership meetings turn into tie out debates.
Workflow warning signs
- Excel is still the operating layer, but the workflow is fragile.
- Data refreshes succeed but the numbers are wrong.
- Metric ownership is unclear.
- Planning assumptions are hidden inside workbook logic.
What it fixes
Three layers behind trusted financial reporting.
KPI Audit starts with the data foundation, then adds forecast continuity and planning risk only when the basics hold.
Forecast Continuity
Driver definitions, versioning, variance bridges, and automated recomputation.
Planning Risk
Monte Carlo ranges, confidence bands, probability of miss, and early warning signals.
Engagement plan
A phased diagnostic, with Phase 1 as the natural starting point.
The full roadmap can run as a 12 week plan, but most teams should start by fixing the actuals and KPI foundation.
1
Financial Data Foundation
Ingest GL, revenue, payroll, or operating exports. Standardize entity, account, time, and KPI logic. Build deterministic validation and tie outs.
Primary output: trusted monthly actuals and KPI definitions.
2
Forecast Automation and Continuity
Standardize drivers, refresh baseline forecasts, version assumptions, and create variance bridges from actuals to plan.
Primary output: forecasts that refresh instead of being rebuilt.
3
Probabilistic Forecasting and Signals
Define driver ranges, test scenarios, run simulations, and surface probability of miss or early warning indicators.
Primary output: planning ranges that show risk clearly.
Deliverables
Concrete outputs, not vague advisory notes.
- Canonical monthly actuals table.
- COA, entity, product, or department mapping.
- Deterministic tie outs and validation checks.
- Excel ready extracts and refreshable outputs.
- KPI and driver definitions.
- Forecast versioning and variance bridges.
- Monte Carlo outputs where useful.
What this is not
Clear boundaries reduce wasted effort.
- Not a dashboard redesign.
- Not a tool migration.
- Not a black box model.
- Not an open ended consulting program.
- Not a replacement for finance ownership.
Best fit
Built for the people responsible for reporting trust.
KPI Audit is most useful for a CFO, VP Finance, FP&A leader, controller, analytics leader supporting finance, or founder with reporting trust issues.
CFO or Finance Leader
You need fewer surprises, better KPI ownership, and more reliable planning outputs.
FP&A or Controller
You need actuals, forecast versions, and variance logic that are easier to defend.
Analytics Leader
You support finance, but source data and definitions keep creating rework.
Request a Phase 1 scope.
Share the KPI or forecast problem, the source systems involved, the reporting audience, and the current workflow. A short note is enough to start.