Validate Your Data Before Trusting It to an AI Workflow

The problem

When an organization is preparing to go live with AI-powered workflows, dataset quality is the single biggest risk—but it's invisible until something breaks. Teams don't know which datasets have stale metadata, undocumented lineage, or field-level issues that will undermine AI outputs downstream. Without a structured validation process, the first production failure is the quality check.

What this app does

An AI readiness and data validation cockpit that scores datasets before they power AI workflows. The app runs a structured battery of quality checks—field completeness, null rates, cardinality anomalies, metadata coverage, lineage documentation—and rolls them into a Trust Score per dataset. Teams work through a guided validation queue, resolve flagged issues, and mark datasets as AI-ready with a timestamped audit trail. Stakeholders see the readiness posture across the full data catalog without pulling individual audits.

Key takeaways

• Trust Score gives a single, defensible quality signal per dataset
• Structured validation queue replaces ad hoc dataset audits
• Timestamped audit trail supports governance and compliance reviews
• Readiness posture visible across the full catalog without custom reporting

Who it's for

Data engineering, BI governance, and analytics leadership teams preparing datasets for AI workflow promotion in Snowflake-backed or enterprise cloud environments.

Validate your data before your AI workflow finds out the hard way

See how Domo can score your dataset readiness and validate quality before AI workflows go live. Request a tailored trust workbench for your data team.

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