The reality of data quality today
Data quality problems surface when it's too late — in executive dashboards, in ML model performance, in wrong business decisions: The typical experience:
Executives spot errors in dashboards before the data team does
Upstream schema changes break pipelines silently
Stale data gets used without anyone noticing
Null values and duplicates corrupt aggregations
No visibility into data freshness or completeness
Hours spent debugging issues instead of building value
How this flow looks in Triform
After each data pipeline run, Triform validates data quality. It checks for schema changes, null rates, duplicate keys, value distributions, and freshness. When issues are detected, it alerts the right team members and can pause downstream processes until issues are resolved.
Start from a single prompt
Describe your full workflow in one go and let Triform design the flow with you.
Example prompt for this automation
Where this automation fits
Data quality you can trust
Every pipeline checked automatically. Every anomaly flagged. Every stakeholder confident in the numbers. This means:
- Catch issues before they reach dashboards
- Build organizational trust in data
- Spend time on insights, not firefighting
From reactive firefighting to proactive monitoring
Connect to your data warehouse
Define quality rules for key tables
Set up alerting thresholds and channels
Configure dashboards for quality visibility