Policies and Exception Workflows

Policies and Exception Workflows

Policies set the default; the exception process makes the default survive contact with reality.

Why it matters

Without an exception process, teams quietly route around the policy and you lose visibility entirely.

Going deeper

Effective exception workflows share three traits:

1. Pre-defined SLA — 'Exceptions will be reviewed in 24 hours.' If it's slower, shadow-IT is guaranteed.
2. Time-bound validity — An exception is never 'forever'. It's granted for a specific window (e.g. 60 days) to bridge a gap, then it expires and alerts the owner.
3. Visible risk registry — Every open aggregate exception is a signal of where your platform's paved road is failing the business. If 40% of teams request an exception to a schema rule, the rule is wrong, not the teams.

Data policies are like safety limits on a factory floor, and exceptions are the manual overrides.

If a conveyer belt normally stops when a sensor fails, that's a good default policy. But if a technician needs to test the belt while fixing the sensor, they need a key to override it. If you don't give them a key, they'll just wedge a piece of cardboard into the sensor to trick it. Now you have zero visibility into when the safety system is bypassed.

In data governance, if your policy says 'no one can query this raw table', but an ML team urgently needs it for a critical model, and your exception process takes 3 weeks, they will simply ask a DB admin for a backdoor dump. A fast exception workflow keeps the override visible and logged.