Phase 1: Understanding the Data
Before building any analytical framework, I needed to understand the data landscape. This exploratory analysis validates data quality, examines distribution patterns, and establishes confidence thresholds for reliable insights.
Key Question: Do we have enough data per BD-Sales pairing to draw meaningful conclusions?
Dataset Overview
Comprehensive examination of opportunity data to ensure quality and completeness
Data Quality Validation
- No missing values: Complete data across all required fields
- Date integrity: All closed dates occur after created dates
- Value validation: All deal values are positive and within expected ranges
- Outcome tracking: Clear classification of Won, Lost, and Open opportunities
Opportunity Distribution Analysis
Understanding data coverage across pairings and establishing confidence thresholds
Key Findings
- Confidence threshold established at 7 opportunities (75th percentile) - provides stable estimates
- Minimum threshold of 3 opportunities - pairs below this excluded from analysis due to unreliability
- Uneven distribution is expected: Some pairings naturally work together more frequently
- Most pairs have sufficient data: Majority of pairings exceed confidence threshold for meaningful analysis
Why Confidence Thresholds Matter
Example: A pairing with only 1 opportunity that closed won would show 100% win rate. This is statistically unreliable. By setting thresholds (3 minimum, 7 for full confidence), we ensure our recommendations are based on patterns, not random chance.
Next Steps
With validated, high-quality data, we're ready to proceed to methodology development
✓ Data Quality Confirmed: No missing values, valid date ranges, complete tracking
✓ Confidence Framework Established: Clear thresholds for reliable analysis (3 min, 7 full confidence)
✓ Sample Sizes Validated: Sufficient data across most pairings for meaningful insights