### 1.1 Business Data Landscape The dataset **sudalairajkumar/indian-startup-funding** (sourced from Kaggle) describes startup investment activity in India. It is organized as a single table, `startup_funding`, with 3,044 rows. Each row appears to be one funding record, so the core entities are the startups receiving capital and the funding transactions themselves. The data is compact at roughly 0.2 MB, which makes it a focused, easily explored reference asset rather than a sprawling data estate. Because everything sits in one flat structure, the picture is cohesive rather than fragmented. ### 1.2 Key Data Assets | Table | Role | Rows | % of Total | Size | |-------|------|-----:|----------:|-----:| | startup_funding | path | 3,044 | 100.0% | 0.2 MB | `startup_funding` is the dataset's only table, so it holds 100% of the rows by design, not through concentration. In business terms, it is the single source of truth for the funding events recorded, and any analysis of investment patterns will start and end here. ### 1.3 Data Integration Posture Nettle detected **0 relationships** (validated joins: none), which reflects a self-contained, single-table design rather than a siloed one. Cross-functional analytics are therefore limited to what the columns in `startup_funding` already capture, such as funding activity viewed across the dimensions it contains. The natural next step is enrichment and schema evolution: adding reference attributes or periodic updates to the existing table, rather than repairing connections that were never part of its structure.