I have already inspected all three main tables in this dataset. Let me compile the complete column inventory with their types and meanings. ```json { "action": "final", "content": "## Dataset Column Inventory\n\nThis B2B sales dataset contains **5 tables** with **44 total columns**. Here are the business-relevant columns from the three main tables:\n\n### B2B Sales Funnel Pipeline (8,800 rows)\n\n| Column | Data Type | Meaning |\n|--------|-----------|----------|\n| **opportunity_id** | VARCHAR | Unique identifier for each sales opportunity (e.g., \"0000I7AO\", \"ZZY4516R\") |\n| **sales_agent** | VARCHAR | Name of the sales representative handling the deal (26 agents: Anna Snelling through Zane Levy) |\n| **product** | VARCHAR | Product being sold (7 products: GTK 500 through MG Special) |\n| **account** | VARCHAR | Customer/company name (89 accounts) |\n| **deal_stage** | VARCHAR | Current stage in the sales funnel (4 stages: Engaging, Prospecting, Qualified, Won) |\n| **engage_date** | DATE | When the opportunity was first engaged (Oct 2016 - Dec 2017; 500 nulls) |\n| **close_date** | DATE | When the deal closed or is expected to close (Mar 2017 - Dec 2017; 2,089 nulls for open deals) |\n| **close_value** | BIGINT | Deal value in currency units (range: $0 - $30,288; null for open opportunities) |\n\n### Accounts (85 rows)\n\n| Column | Data Type | Meaning |\n|--------|-----------|----------|\n| **account** | VARCHAR | Company name (matches pipeline.account for joining) |\n| **sector** | VARCHAR | Industry sector (11 sectors: employment, finance, telecommunications, etc.) |\n| **year_established** | BIGINT | Year the company was founded (1979-2017) |\n| **revenue** | DOUBLE | Annual revenue in millions (range: $4.54M - $11,698M) |\n| **employees** | BIGINT | Number of employees (9 - 34,288) |\n| **office_location** | VARCHAR | Country where headquartered (13 countries: Belgium through United States) |\n| **subsidiary_of** | VARCHAR | Parent company name if applicable (9 parent companies; blank for independent firms) |\n\n### Sales Teams (35 rows)\n\n| Column | Data Type | Meaning |\n|--------|-----------|----------|\n| **sales_agent** | VARCHAR | Sales representative name (matches pipeline.sales_agent) |\n| **manager** | VARCHAR | Sales manager overseeing this agent (6 managers: Cara Losch through Summer Sewald) |\n| **regional_office** | VARCHAR | Geographic sales region (3 regions: Central, East, West) |\n\n### System Metadata Columns\n\nAll tables include these technical columns (not typically used for analysis):\n- **_ingestion_timestamp** (TIMESTAMP): When the data was loaded\n- **_batch_id** (VARCHAR): ETL batch identifier\n- **_source_file** (VARCHAR): Original CSV file path\n- **_source_system** (VARCHAR): Source system (\"api\")\n\n### Additional Tables\n\n- **products** (7 rows): Product catalog with pricing and specifications\n- **data_dictionary** (21 rows): Metadata documentation for the dataset fields\n\n**Note on Joins**: The dataset card indicates no verified foreign keys were detected. However, the `account` column appears in both pipeline and accounts tables, and `sales_agent` appears in both pipeline and sales_teams, suggesting these are the natural join keys for analysis." } ```