Qualytics User Guide
Control the context behind every decision.
Qualytics applies data quality as a shared control layer for your teams and AI. It profiles how data behaves, infers and maintains quality checks, and surfaces anomalies, while your teams add business context and govern what trusted means.
Start with your goal
Choose the path that best matches what you want to accomplish.
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Get to your first quality signals
Connect a source, understand the core operations, and review your first quality results.
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Define what trusted data means
Combine AI Managed and Authored checks to define what trusted data means for your organization.
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Monitor data health
Review scores, profiles, checks, and anomalies across the datastores you can access.
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Investigate and resolve issues
Review failed-check evidence, assign ownership, and document resolutions.
How Qualytics works
Qualytics builds trusted context through a governed lifecycle:
- Profile and understand Learn distributions, patterns, and expected structures from observed data behavior.
- Define and maintain coverage Use AI Managed checks for broad coverage and Authored checks for business expectations.
- Monitor quality Evaluate checks and review the resulting quality scores and anomalies.
- Investigate and resolve Review evidence, assign ownership, and document resolutions.
Built for humans and AI
One governed foundation
Quality checks capture what good data means. Scores and anomalies show how current data compares with those expectations, while ownership and resolution history preserve your team's decisions. Flows, integrations, the API, and MCP make these signals available to the people and systems that use your data.
Explore capabilities
Tools and updates
See it in action
Take a short walkthrough of the platform and see how its core capabilities work together.