# Data Quality & Project Performance Dashboard Report 2026
> Explore key insights on workload analysis, defect rate monitoring, and risk identification across 10 major projects using BI analytics.

Tags: data-quality, project-management, bi-analytics, defect-rate, workload-analysis, kpi-dashboard, business-intelligence
## Slide 1: Data Quality & Project Performance Dashboard
- Focus areas: Workload analysis, defect rate monitoring, and risk identification.
- Report Date: April 2026.

## Slide 2: Raw Dataset Overview
- Database covers 10 projects/customers.
- Metrics include API, Parts, BOM, Subscribers, and SE DB.
- Defect rates range from 0.2% (Agilent) to 100% (Internal Test).

## Slide 3: Focus Area Summary
- SE DB represents the highest volume with 7.7M+ parts reviewed.
- BOM shows high efficiency with a 0.7% defect rate.
- Customer Part List identified as most consistent in quality.

## Slide 4: Key Performance Indicators (KPIs)
- Total Reviewed Parts: 10,603,380.
- Total Corrected Cells: 451,060.
- Average Defect Rate: 11.4% (drops to 2.1% excluding Internal Test).

## Slide 5: Defect Rate by Project
- Visual breakdown of quality outliers.
- Risk Thresholds: High (>4%), Moderate (1-4%), Low (<1%).

## Slide 6: Workload Distribution
- 'Top Issued Product Lines' accounts for 7,000,000 reviewed parts.
- 'Proactive Plans' accounts for 2,000,000 reviewed parts.

## Slide 7: Volume vs. Defect Rate Correlation
- Finding: No strong positive correlation between high workload and high defect rates.
- Quality is driven by process discipline rather than volume.

## Slide 8: Distribution by Review Frequency
- Project breakdown by review cycle: Weekly (40%), Monthly (30%), Daily (30%), Quarterly (10%).

## Slide 9: Key Insights & Business Recommendations
- **Internal Test:** Immediate investigation needed due to 100% anomaly.
- **Top Issued Product Lines:** High risk; requires priority resource allocation.
- **Proactive Plans:** Model for scalable quality control (0.7% defect rate).
- **High Quality:** Agilent and Honeywell recognized for best-in-class accuracy.
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