Native Data Quality Advisor
The Native Data Quality Advisor is a conversational agent that helps you decide which data quality checks to apply to your data, and helps you build reusable data quality skills. It works in two modes.
- Recommendation mode — given a table and its columns, it proposes appropriate data quality checks for each column.
- Skill authoring mode — it helps you create, refine, and test reusable data quality skills through conversation.
How it works
Section titled “How it works”- In recommendation mode, the agent reads the table and column information you supply and outputs structured checks keyed by column.
- In skill authoring mode, the agent asks clarifying questions before generating or refining skill content, drawing on a catalog of data quality rules, domain skills, and standards.
Input parameters
Section titled “Input parameters”Required:
message(string): What you want help with (e.g., “recommend quality checks for this table” or “help me build a freshness skill”).
Optional:
table_id(string): A table to use as initial context, e.g."table.25".skill_ids(list): Restrict the agent to specific skills, useful for testing one skill in isolation.skill_content(string): Existing skill content you want the agent to improve.
Available tools
Section titled “Available tools”The agent draws on Alation’s data quality knowledge through internal tools that load the rules catalog, domain-specific skills, and data quality standards. These are configured on the agent by default.
Behavior notes
Section titled “Behavior notes”- In recommendation mode the agent outputs checks immediately rather than holding a conversation.
- In skill authoring mode it asks clarifying questions first.
- It loads the rules, skills, and standards catalogs once per session rather than re-fetching them repeatedly.