Financial Performance Dashboard

Understanding how revenue, costs, cash flow, and forecast accuracy shape the health of a mid-sized company.

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🎯 Project Overview

This project brings together GL actuals, budget and forecast data, AR, AP, and employee expense claims into one Power BI model.

The goal is to show how different areas of a business contribute to overall performance and where operational pressure points appear.

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⚠️ Note on the Synthetic Data

• The dataset was generated independently across several tables, so financial values do not reconcile the way real accounting systems do.

• This means the dollar amounts should not be taken literally.

• The focus is on modeling structure, analytical reasoning, and the types of insights a real dashboard would reveal.

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📈 Executive Summary

Across the 2023 to mid-2025 period, the company shows steady revenue growth, stable margins, and predictable cost behavior.

Even though the dollar amounts are synthetic, the patterns act like those you would see in a real multi-division operation: clear revenue hierarchy, consistent margin performance, and cost structures that follow expected division roles.

Supporting pages highlight how the business handles forecasting, cash conversion, overdue balances, and employee spending. Together, the dashboards demonstrate how finance leaders interpret performance, diagnose issues, and guide decisions.

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🧭 Key Insights

1. Performance is stable and predictable across divisions

All six divisions follow almost the same revenue and margin shape over time. The synthetic generator gives them different absolute values, but the patterns across periods are nearly identical.

Insight:

The business behaves like a well-synchronized multi-channel operation with consistent seasonality and cost structure. The value here is not comparing divisions to each other, but seeing how predictable the overall business model is quarter after quarter.

2. Margin stability suggests disciplined cost control, not division-level differences

Operating margin holds between roughly 30 and 45 percent almost every quarter even when revenue moves.

Insight:

Instead of pointing to divisions, the more accurate takeaway is that the business maintains strong unit economics regardless of volume, showing a stable cost structure and controlled spending.

3. Forecast variance happens quarter-to-quarter, not by division personality

The heatmap does NOT show any division that consistently over- or under-forecasts. Every division has:

• Some green quarters

• Some red quarters

• Some neutral quarters

Insight:

Variance patterns are temporal, not structural. This is what you’d expect when planning uncertainty is driven by market timing, seasonality, or one-time events, not by a division being fundamentally “good” or “bad” at forecasting.

4. Cash flow timing is the clearest operational signal

Cash In > Cash Out in nearly every period, and DSO (44 days) consistently exceeds DPO (30 days).

Insight:

This is a business that reliably converts revenue to cash, but routinely floats customers longer than vendors, a classic mid-market operational pattern. Even with synthetic inputs, the cash flow relationship behaves correctly and gives you something real to interpret.

5. AR/AP overdue percentages are low and not indicative of structural problems

Overdue balances appear only in Q2 2025, which matches the generation script and not a behavioral issue.

Insight:

The business likely has clean credit control, and the overdue spike is a data artifact, not a meaningful signal. This reinforces why you interpret patterns, not absolute values, in synthetic data.

6. Expense reimbursement behavior is the only area with a truly directional signal

Even though category amounts are uniform, two things stand out:

• Reimbursement time is consistent at ~14 days

• Rejection rate is unusually high at ~27 percent

Insight:

Process discipline exists (fast payment), but policy clarity or pre-approval behavior likely needs improvement. These two metrics create some of the most actionable real-world signal in the entire dataset.

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🧾 Conclusion

This dashboard outlines the core financial story of a company and shows exactly how leadership would use these insights to guide decisions.

With richer and more varied real-world data, this model could evolve into a full performance management system supporting planning, forecasting, and operational review.