Data QA/QC
Built an automated QA/QC pipeline for a client’s real-time sensor data feeds, catching bad readings before they reached downstream users.
The Challenge
The client relied on real-time buoy and sensor feeds for operational decisions and model validation, but bad or drifting sensor data was going undetected until it caused downstream problems.
The Approach
Built an automated pipeline of range, spike, flatline, and climatological checks applied to incoming data in near-real time, flagging suspect records and generating daily QA reports.
The Result
Meaningfully cut bad-data incidents and manual review time, giving the client confidence in the feed without manual spot-checking.
Thumbnail: NOAA weather buoy — public domain (NOAA), via Wikimedia Commons.