I need to systematically examine data quality issues across both tables. Let me start by checking the fighters table for anomalies, then move to popular_matches. ```json { "action": "tool_call", "reasoning": "The inspect_columns output shows several red flags in the fighters table: 'height' has values like '13 Dec 1916' (a date, not a height), 'country' has values like '75.20 inches (191 cm)' (a measurement, not a country), and 'age' includes 'Unknown'. I need to query the actual data to see the extent of these column misalignments and format inconsistencies.", "tool": "query_sql", "args": { "sql": "SELECT height, COUNT(*) as count FROM raw.fighters WHERE height NOT LIKE '%ft%' AND height NOT LIKE '%cm%' AND height != 'Unknown' GROUP BY height ORDER BY count DESC LIMIT 20", "purpose": "Identify non-height values in the height column (dates and other anomalies)", "source": "raw" } } ```