01
Analytics Debt Ledger
Paid proof for a dashboard, metric, forecast, workflow, SQL sample, or AI-assisted number that may be unsafe.
About Forecast Integrity Partners
Independent verification for numbers executives cannot afford to get wrong.
Forecast Integrity Partners is led by Stephen McDaniel, a 20-year veteran across data science, analytics, forecasting, data architecture, BI, software leadership, and executive decision systems.
The resume supports the offer. The offer is decision confidence, not analytics labor.
Why the background matters
The work sits where finance, analytics, architecture, forecasting, BI, and executive decisions collide.
Most companies do not have one clean analytics problem. They have definitions, systems, incentives, workflows, forecasts, dashboards, vendors, and AI tools producing numbers that leadership must still use.
FIP exists to identify where that system creates cost, risk, false confidence, or decision-quality failure.
Relevant background
- 20 years across data science, analytics, forecasting, BI, and data architecture
- Experience across Netflix, Yahoo, SAS, Oracle, and analytics software leadership
- Built data warehouses, advanced analytics systems, CLV systems, dashboards, forecasts, and executive decision infrastructure
- Worked across Tableau, SAS, Microsoft, Oracle, Snowflake, PostgreSQL, ClickHouse, BigQuery, Python, SQL, Informatica, and R
- Taught Tableau, R, LTV, and forecasting through professional education channels
Diagnostic artifact example
What the review turns into executive language.
This is the kind of finding a buyer should expect: a concrete artifact, a specific break, a severity rating, and a financial recommendation.
| Artifact | Discovered Error | Severity | Financial Recommendation |
|---|---|---|---|
| ARR dashboard joined CRM opportunity records to subscription records at mixed account and contract grain. | Expansion revenue was counted once at opportunity close and again when the renewed subscription became active. The dashboard looked clean, but the grain mismatch inflated board-facing recurring revenue. | 5 Board, valuation, acquisition, capital allocation, or major forecast risk. |
Freeze use of the dashboard for valuation or hiring decisions. Reconcile ARR from contract-level source records, isolate expansion logic, and reissue the board metric with owner, definition, and test evidence. |
Q1 public offers
The public site stays narrow.
FIP exposes only the offers that map to urgent buyer pain in Q1.
02
Analytics and AI Spend Rationalization Review
For CFOs, CEOs, and boards dealing with analytics and AI cost without matching decision confidence.
03
Analytics QoE / Live-Deal Diligence
For PE, M&A, and corporate development teams relying on target-company metrics and forecasts.
What FIP is not
This is not a commodity data shop.
FIP is built for executive decision risk. The public offer should not drift into generic dashboards, AI consulting, hourly advisory, fractional CDO language, or implementation labor.
- Not a Tableau dashboard shop
- Not a Snowflake implementation vendor
- Not a generic AI consultant
- Not a staff augmentation business
- Not a low-cost analytics audit provider
Next step
Use FIP when the number affects money, trust, board confidence, or a deal.
If the problem is real, the next step is a short fit call that routes to the smallest useful paid review.