I'll systematically check for data quality issues across the dataset. Let me start by examining the key measure columns for outliers and impossible values. ```json { "action": "tool_call", "reasoning": "Check the accounts table for outliers in revenue and employees, and verify year_established is reasonable", "tool": "query_sql", "args": { "sql": "SELECT MIN(revenue) as min_revenue, MAX(revenue) as max_revenue, AVG(revenue) as avg_revenue, MEDIAN(revenue) as median_revenue, STDDEV(revenue) as stddev_revenue, MIN(employees) as min_employees, MAX(employees) as max_employees, AVG(employees) as avg_employees, MEDIAN(employees) as median_employees, MIN(year_established) as min_year, MAX(year_established) as max_year, COUNT(*) as total_accounts, COUNT(revenue) as revenue_count, COUNT(employees) as employees_count, COUNT(year_established) as year_count FROM raw.accounts", "purpose": "Profile accounts table measures for outliers and missing values" } } ```