Lesson ProgressPhase 3 of 6
Phase 3Guided Practice
Guided Practice: Integration & Presentation: Decision‑Ready Dashboard

Build Sarah’s café dashboard with scenario controls, linked charts, and validation

🛠️ Phase 3: Guided Practice

Build Sarah’s Integration Dashboard

Implement scenario controls with a driver table, link outputs to visuals, and install validation that catches missing names, stale dates, and out‑of‑range rates.

Professional Reliability Checklist
  • No hard‑coded values in outputs; all driven by inputs.
  • Scenario switches by exact name; IFNA/IFERROR protects demos.
  • Charts bind to outputs that use Table[Column] references.
  • Audit panel counts: missing keys, stale AsOfDate, rate bounds.
  • Executive summary states the decision and the why.
Café Operations Dashboard
Comprehensive financial overview and key metrics

Weekend Revenue

$107,700

+18.5%

Café Profit

$35,200

+24.3%

Food Waste

2.8%

-1.2%

Customer Traffic

1,240/day

+15.6%
Revenue vs Expenses
Monthly financial performance comparison
Account Balances
Current account balances distribution
Cash Flow Analysis
Monthly cash flow and profit tracking
Profit Margin Analysis
Monthly profit margin percentage
Financial Summary
Quick overview of key financial metrics
$116,000
Total Revenue (6 months)
$91,000
Total Expenses (6 months)
$25,000
Net Profit (6 months)
Validation Builder (Practice)

Use the validation tool to practice writing rules like “Scenario name must not be blank,” “Rates between 0% and 100%,” and “AsOfDate within 30 days.” Transfer these patterns into your spreadsheet model.

🛡️

Error Checking System Builder

🔍

Master conditional formatting and data validation rules for business automation. Build the Excel skills needed for Unit 2's Month-End Wizard - where error checking reduces closing time from days to hours.

Payroll Data ValidationMedium
Detect overtime violations, missing employee data, and calculation errors in payroll processing.

Business Context:

Unit 5 PayDay Simulator: Automated payroll error checking prevents costly mistakes and compliance issues.

Validation Rules:

📊
Overtime Hours Detection
High

Highlight employees working more than 40 hours per week

Missing Employee ID
High

Flag blank or missing employee identification numbers

🧠
Gross Pay Calculation Error
High

Detect discrepancies between calculated and entered gross pay

Missing Department Assignment
Medium

Identify employees without department assignments

Sample Data & Validation Results
Select a validation rule to test it against the sample data

Select a validation rule to test it

The rule will highlight errors in the sample data using conditional formatting colors

Month-End Wizard Connection

These error checking skills are essential for Unit 2's Month-End Wizard, where students learn to:

  • • Build automated validation systems that catch posting errors before they impact financial statements
  • • Use conditional formatting to visually highlight discrepancies and missing data
  • • Create Excel formulas that validate complex business logic automatically
  • • Reduce month-end closing time from days to hours through systematic error prevention
  • • Design validation rules that improve data quality and reduce manual checking

Next Step: Apply these validation concepts to build your own Month-End Wizard with automated error checking systems.

Chart Linking Simulator

Practice linking charts to data using structured references, then build recommendations based on KPI thresholds.

Chart Linking Practice
Select the correct formula to link charts to data using structured references
Progress: 0/40%

Link Revenue chart to the Sales Table using structured references.

Data range: SalesTable[Revenue]

Recommendation Builder
Choose the right business action based on KPI thresholds
Progress: 0/40%
Base Case

Profit Margin: 12% (threshold: 10%)

Key Takeaways
  • Use Table[Column] structured references for stable charts
  • Charts break when they point to fixed ranges—structured refs auto-expand
  • Recommendations must cite specific KPIs and thresholds
  • Never make recommendations from hard-coded values—use model outputs