ERP Import Risk & Data Quality Dashboard
Project dataset screening for category validity, budget anomalies, phase complexity, and numeric risk concentration
The project portfolio is concentrated in a small set of categories, with It alone accounting for 34.5% of all 4,000 records. That concentration raises the impact of any category-mapping issue during ERP import.
- 72 budget records sit above the upper IQR fence, making outlier review a meaningful pre-import cleanup task.
- Closure has the highest average complexity score at 6.24, although phase averages remain tightly clustered overall.
- 1,798 projects (45.0%) are tagged High or Critical risk, concentrating review effort on larger-budget, longer-timeline records.
| Project Type | Count | Share |
|---|---|---|
| It | 1,381 | 34.5% |
| Construction | 797 | 19.9% |
| R&D | 588 | 14.7% |
| Manufacturing | 418 | 10.4% |
| Marketing | 418 | 10.4% |
| Healthcare | 398 | 10.0% |
Average complexity levels remain close across phases, so the richer signal comes from how budget and timeline cluster across risk levels. The scatter plot below uses portfolio averages as reference lines to show where higher-exposure records concentrate.
| Risk Level | Count | Share |
|---|---|---|
| Low | 806 | 20.2% |
| Medium | 1,396 | 34.9% |
| High | 1,036 | 25.9% |
| Critical | 762 | 19.1% |
The portfolio average sits at 17.1 months and $1.14M. Projects above both averages form the key upper-right concentration zone because they combine longer delivery windows with larger financial exposure.
- Medium risk is the largest plotted segment at 34.9% of the portfolio.
- High and Critical projects together account for 45.0% of all plotted records.
- Budget outliers become more material when they also sit on the long-timeline side of the scatter.
The heatmap summarizes how the main numeric risk factors move together. Strong positive coefficients suggest that import checks should treat these fields as linked rather than as isolated anomalies.
| Factor Pair | Correlation |
|---|---|
| Stakeholder_Count ↔ Project_Budget_USD | 0.89 |
| Project_Budget_USD ↔ Team_Size | 0.89 |
| Estimated_Timeline_Months ↔ Project_Budget_USD | 0.86 |
| Estimated_Timeline_Months ↔ Team_Size | 0.86 |
| Complexity_Score ↔ Estimated_Timeline_Months | 0.79 |
The strongest relationship is between Stakeholder_Count and Project_Budget_USD, with a correlation of 0.89. Timeline length also moves closely with both budget and team size, reinforcing that risk concentration is multi-factor rather than driven by a single field.