What’s the difference between data quality and trust?
Quality is whether the data meets defined expectations. Trust is whether users believe it does—because expectations are explicit, checks are visible, and issues are handled consistently.
Which quality checks should we start with?
Start with the checks that prevent the biggest business damage:
How do we avoid “alert fatigue”?
Alert only on actionable failures and route them to the accountable owner. Use severity levels, suppression windows, and escalation rules.
Do we need a commercial tool to do this well?
Not necessarily. Many teams start with open-source or built-in platform capabilities. The key is pipeline integration, visibility for users, and operational response.
What should go into SLAs with source systems?
Schema change notice, freshness expectations, data quality thresholds for critical fields, contact/owner, and response time for incidents.
How do we handle breaking changes without slowing teams down?
Use versioning and change communication. Provide migration windows and deprecation timelines. Prevent silent breaking changes where possible.
What does “certification” actually mean?
A visible signal that a data product meets defined standards (quality, documentation, ownership, reliability) and is safe to use for important decisions.
How do we rebuild trust after an incident?
Make root cause transparent, document what changed, and show improved checks. Trust comes back when users see reliability improving over time.