Guide

Data freshness & SLA monitoring: flag stale data

Data freshness monitoring flags records that are older than your SLA — a feed that didn't update, a date that's gone stale — so you catch late or missing data before it feeds a decision. Set the timeliness window and the engine checks it on every run.

How it works

What you get

• Freshness / SLA checks — flag records older than your timeliness window.
• Any window — 24h, 7d, month-end — set the age that matters.
• Feed-anomaly detection — compare today's inbound feed vs yesterday for gaps and spikes.
• Scheduled — run on a cadence and email an alert on breach.
• Part of data quality — freshness is one of the 10 scored DQ dimensions.
• Auditable — timestamped, exportable evidence for every check.

Why stale data slips through manual checks

Manual freshness checks are forgotten exactly when a feed silently stops. Automated freshness & SLA monitoring flags every record past its window on every run and can alert you on breach — so late or missing data is caught before it's trusted.

This is one of 20+ checks in AI data validation — the same engine also runs reconciliation, data quality, profiling, document compare and governance. For sensitive data, the full dataset never leaves your server; see the security page.

FAQ