### 1.1 Business Data Landscape This dataset captures a B2B sales operation through five distinct tables that track opportunities, customer accounts, product offerings, and team assignments. The **b2b_sales_funnel_pipeline** table serves as the operational core, containing 8,800 individual sales opportunities that represent the full lifecycle from lead to close. Supporting this pipeline are **accounts** (85 customer organizations), **products** (7 distinct offerings), and **sales_teams** (35 team records), with a **data_dictionary** table providing field-level metadata. The structure reflects a typical commercial sales environment where opportunities flow through defined stages, assigned to specific teams and tied to customer accounts and product SKUs. ### 1.2 Key Data Assets | Table | Role | Rows | % of Total | Size | |-------|------|-----:|----------:|-----:| | b2b_sales_funnel_pipeline | dimension | 8,800 | 98.3% | 0.2 MB | | accounts | dimension | 85 | 0.9% | 0.0 MB | | sales_teams | dimension | 35 | 0.4% | 0.0 MB | | data_dictionary | dimension | 21 | 0.2% | 0.0 MB | | products | dimension | 7 | 0.1% | 0.0 MB | The **b2b_sales_funnel_pipeline** table dominates the dataset at 98.3% of total rows, functioning as the transactional record of every sales opportunity tracked by the organization. The remaining four tables are compact reference assets—customer accounts average roughly 104 opportunities each, while the seven products suggest a focused portfolio rather than a broad catalog. ### 1.3 Data Integration Posture Despite containing logically related entities (opportunities, accounts, products, teams), this dataset currently operates as five independent tables with zero validated relationships between them. This siloed structure means cross-functional questions—such as "Which accounts generate the highest-value pipeline for Product X?" or "How do close rates vary by sales team and product mix?"—require manual joins using business logic rather than enforced schema relationships. The 100% referential integrity score indicates data quality is strong within each table, but the absence of foreign key constraints leaves integration work to downstream analysts and limits the dataset's readiness for self-service exploration or automated reporting workflows.