I'll systematically check for data quality issues across this Olympic table tennis dataset. Let me start by examining the structure and basic statistics of the data. ```json { "action": "tool_call", "reasoning": "First, I need to understand the data structure and get basic statistics across all columns to identify potential quality issues like nulls, outliers, or impossible values. I'll query for comprehensive column profiling in one pass.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(DISTINCT year) as distinct_years, MIN(year) as min_year, MAX(year) as max_year, COUNT(DISTINCT event) as distinct_events, COUNT(DISTINCT medal) as distinct_medals, COUNT(DISTINCT country) as distinct_countries, COUNT(DISTINCT medalist) as distinct_medalists, COUNT(DISTINCT event_type) as distinct_event_types, COUNT(DISTINCT gender) as distinct_genders, COUNT(DISTINCT era) as distinct_eras, SUM(CASE WHEN year IS NULL THEN 1 ELSE 0 END) as null_years, SUM(CASE WHEN event IS NULL THEN 1 ELSE 0 END) as null_events, SUM(CASE WHEN medal IS NULL THEN 1 ELSE 0 END) as null_medals, SUM(CASE WHEN country IS NULL THEN 1 ELSE 0 END) as null_countries, SUM(CASE WHEN medalist IS NULL THEN 1 ELSE 0 END) as null_medalists, SUM(CASE WHEN medal_value IS NULL THEN 1 ELSE 0 END) as null_medal_values, SUM(CASE WHEN is_china IS NULL THEN 1 ELSE 0 END) as null_is_china, MIN(medal_value) as min_medal_value, MAX(medal_value) as max_medal_value, AVG(medal_value) as avg_medal_value FROM raw.olympic_table_tennis_medals_1988_2024", "purpose": "Get comprehensive statistics to identify nulls, value ranges, and cardinality across all key columns" } } ```