Data downtime
Any period when data is missing, stale, wrong or otherwise can't be trusted — the data equivalent of a service outage.
The vocabulary of keeping data honest — defined in plain language, for anyone working with data, whether or not you have a data team.
Any period when data is missing, stale, wrong or otherwise can't be trusted — the data equivalent of a service outage.
How recently a table was updated, measured against how recently it should have been.
How fit your data is for the decisions made on it — accurate, complete, fresh and consistent.
The share of rows where a column is empty — a jump usually means an upstream field stopped being populated.
Automatically spotting values that break from the expected pattern, instead of relying on fixed manual thresholds.
The learned 'normal' for a table or metric — the range against which anomalies are judged.
A gradual shift in the distribution of a column's values over time, away from what it used to be.
When the number of rows a table gains (or loses) breaks from its normal pattern — too many, too few, or none.
A defined, agreed-upon number that tracks the business — revenue, active users, churn — with one canonical calculation.
An organized inventory of your data — tables, columns and metrics — with definitions, so people can find and trust what exists.
A single place where business metrics are defined, so every tool and team reads the same numbers the same way.
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