While working at Pratt & Whitney, I was tasked with analyzing discrepancies in engine performance data across multiple aircraft models. My analysis revealed that a recent spike in fuel inefficiency was due to a combination of sensor calibration drift and anomalies in flight telemetry—an issue that required both statistical validation and a deep understanding of engine data logging.
The challenge was explaining these findings to a fleet operations executive, whose expertise was in logistics and scheduling—not data science or engineering.
To bridge the gap:
- I simplified the technical language by using relatable analogies—for example, comparing sensor drift to a bathroom scale slowly giving incorrect readings over time.
- I created a visual storyline using clear charts that showed the fuel inefficiency trend alongside sensor performance over time.
- I focused the conversation on business impact, emphasizing how undetected anomalies could lead to increased fuel costs and unnecessary maintenance events.
By shifting the focus from technical jargon to business consequences and visual cues, the executive understood the urgency and approved a cross-team initiative to recalibrate sensors and refine our anomaly detection thresholds.
As a result, we improved operational accuracy and reduced false maintenance alerts by over 20%, while building stronger alignment between engineering and operations.