I need to identify time dimensions in this dataset and analyze them for notable patterns. Let me start by examining the temporal axes mentioned in the card. ```json { "action": "tool_call", "reasoning": "The card lists three temporal axes: accounts.year_established (1979-2017), b2b_sales_funnel_pipeline.engage_date (2016-10-20 to 2017-12-27), and close_date (2017-03-01 to 2017-12-31). I'll start by analyzing the sales pipeline engage_date since it appears to be the most granular operational time series. I'll query for daily counts to identify spikes, dips, or breaks.", "tool": "query_sql", "args": { "sql": "SELECT DATE_TRUNC('day', engage_date) as day, COUNT(*) as deal_count, SUM(close_value) as total_value FROM raw.b2b_sales_funnel_pipeline WHERE engage_date IS NOT NULL GROUP BY DATE_TRUNC('day', engage_date) ORDER BY day", "purpose": "Get daily deal engagement counts and values to identify temporal patterns, spikes, and dips", "source": "raw" } } ```