How do you implement effective market data quality controls in a risk management environment?

External question — Quantitative Finance Stack Exchange Author: la_fonction_capetienne Original post: https://quant.stackexchange.com/questions/85874 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I work with market data used for risk calculations (interest rates, FX, equity prices, volatilities, etc.) and I'm looking for best practices to control data quality before it is consumed by downstream systems. Typical controls I am considering include: Completeness checks (missing instruments, missing fields) Freshness checks (stale market data) Threshold / tolerance checks against previous values Cross-source validation Outlier detection Curve consistency checks What controls are commonly implemented in banks, asset managers, or trading firms? How are thresholds usually calibrated to avoid both false positives and missed data issues?