I'll systematically check for data quality issues across the dataset. Let me start by examining the main pipeline table for outliers and impossible values. {"action": "tool_call", "reasoning": "Check for outliers and impossible values in close_value. From the column metadata, close_value ranges from 0 to 30,288. I need to see the distribution to identify outliers, and check if zero values are legitimate or data quality issues.", "tool": "query_sql", "args": {"sql": "SELECT MIN(close_value) as min_value, MAX(close_value) as max_value, AVG(close_value) as avg_value, MEDIAN(close_value) as median_value, STDDEV(close_value) as stddev_value, COUNT(*) as total_records, COUNT(close_value) as non_null_records, SUM(CASE WHEN close_value = 0 THEN 1 ELSE 0 END) as zero_values, SUM(CASE WHEN close_value < 0 THEN 1 ELSE 0 END) as negative_values FROM raw.b2b_sales_funnel_pipeline", "purpose": "Analyze close_value distribution for outliers and impossible values", "source": "raw"}}