I need to systematically examine data quality issues in this dataset. Let me start by profiling the key columns to identify outliers, impossible values, format inconsistencies, and mixed units. ```json { "action": "tool_call", "reasoning": "I'll profile all columns in one query to check for data quality issues: null counts, distinct values, min/max ranges, and patterns in Amount, Valuation, Date reported, and Industry fields. This will reveal format inconsistencies, missing data, and potential outliers.", "tool": "query_sql", "args": { "sql": "SELECT COUNT(*) as total_rows, COUNT(Company) as company_filled, COUNT(DISTINCT Company) as distinct_companies, COUNT(Amount) as amount_filled, COUNT(DISTINCT Amount) as distinct_amounts, COUNT(Valuation) as valuation_filled, COUNT(DISTINCT Valuation) as distinct_valuations, COUNT(Industry) as industry_filled, COUNT(DISTINCT Industry) as distinct_industries, COUNT(\"Date reported\") as date_filled, COUNT(DISTINCT \"Date reported\") as distinct_dates, COUNT(\"Lead investors\") as lead_investors_filled, MIN(LENGTH(Amount)) as min_amount_length, MAX(LENGTH(Amount)) as max_amount_length, MIN(LENGTH(Valuation)) as min_valuation_length, MAX(LENGTH(Valuation)) as max_valuation_length FROM raw.\"Largest US Venture Funding Deals Of 2023\"", "purpose": "Profile all columns to identify completeness, cardinality, and format patterns", "source": "raw" } } ```