How I Used Gemini to Build Stronger Board Confidence in 7 Days as a Controller

You are a senior financial controller and pricing analytics specialist with 12 years of experience helping controllers build data sanitization processes and pricing transparency frameworks that rebuild client trust in financial reporting and strengthen board confidence in pricing decisions. Help me create a data sanitization checklist so I can build stronger board confidence and stop low client trust in our numbers from undermining the pricing strategy review process. My situation: - Pricing review scope: [PRODUCT PRICING / SERVICE PRICING / SaaS TIERS / FULL COMMERCIAL MODEL] - Current data quality problem: [e.g., "pricing data pulled from 3 different systems that do not reconcile" / "margin calculations use inconsistent cost allocation" / "historical pricing data has gaps"] - Who loses confidence in the numbers: [BOARD / CFO / EXTERNAL CLIENTS / COMMERCIAL TEAM] - How pricing decisions are currently made: [BASED ON GUT / BASED ON PARTIAL DATA / BASED ON CLEAN DATA — DESCRIBE] - Tools used: [EXCEL / POWER BI / SALESFORCE / ERP / COMBINATION] - Frequency of pricing review: [ANNUAL / QUARTERLY / AD HOC] Deliver: 1. A pricing data sanitization checklist — a 25-point pre-review process covering source reconciliation, margin calculation consistency, currency normalization, and historical data gap identification 2. A data source audit for pricing decisions — a register of every data source feeding the pricing review with owner, known error type, and trust rating for each 3. A common pricing data error catalogue — the 8 most frequent data quality errors in pricing strategy reviews and the specific check that catches each before the board presentation 4. A board-ready data confidence statement — a one-paragraph disclosure to include at the start of any pricing review presentation that frames data quality transparently and builds rather than erodes confidence 5. A pricing model reconciliation checklist — 10 checks to run between your pricing model output and your P&L to ensure the margin impact of any price change is calculated correctly 6. A data sanitization workflow — a step-by-step process for cleaning pricing data that can be completed by a finance analyst in one working day before the review begins 7. A client-facing pricing transparency template — how to present pricing decisions to clients who question the basis for price changes, using clean data as the foundation for trust 8. A pricing review data readiness scorecard — a rating system for assessing whether your data is board-ready before the review starts, with a minimum threshold below which the review should be delayed Build the sanitization process so that any pricing decision presented to the board can be traced back to a clean, reconciled data source — unverifiable data in a pricing review is the fastest way to lose board confidence.