I'll systematically check for data quality issues across the dataset. Let me start by examining the column metadata to understand what we're working with, then look for specific problems. ```json { "action": "tool_call", "reasoning": "First, I need to understand the data types and basic statistics across all tables to identify potential quality issues. I'll start with the sales_pipeline table as it's the largest and has multiple date and numeric fields where quality issues commonly occur.", "tool": "inspect_columns", "args": { "table": "sales_pipeline" } } ```