I'll systematically investigate data quality issues across this dataset by examining the quality_issues table and profiling key columns for outliers, impossible values, and inconsistencies. ```json { "action": "tool_call", "reasoning": "Start by checking the quality_issues table, which likely catalogs known data quality problems. This will give me a direct view of documented issues.", "tool": "query_sql", "args": { "sql": "SELECT issue_type, severity, COUNT(*) as issue_count, COUNT(DISTINCT table_name) as affected_tables FROM raw.quality_issues GROUP BY issue_type, severity ORDER BY issue_count DESC", "purpose": "Get overview of documented quality issues by type and severity" } } ```