In large-scale IT systems, especially those processing billions of raw data records, even a tiny error rate can translate into millions of issues – potentially affecting reporting, billing, compliance, or even revenue recognition.
This raises a critical question:
How do you handle data processing errors at scale?
Some organizations rely heavily on manual validation. It works… until it doesn’t. What happens when there are millions of errors and just hours to fix them?
Others invest in:
- 🔁 Automated error handling and reprocessing frameworks
- 🧠 AI-assisted anomaly detection
- 🧩 Segmentation of error codes tied to pre-defined actions
- 📊 Impact-based prioritization to focus on what’s critical
But there’s always a trade-off: Automation vs. Control
Too much automation can risk missing critical nuances. Too much manual control and you lose scalability and time. Some of our industry peers are seriously struggling – facing millions of data errors daily without a scalable or reliable error-handling mechanism in place.
I’m curious to hear from others in data-intensive domains:
- How do you balance automation with human oversight?
- Do you segment error types? Use AI? Prioritize based on business impact?
- Any successful automated error handling implementation for non-trivial scenarios?