### 2.1 Integration Assessment The dataset is a single, self-contained table, **startup_funding** (3,044 rows), so no inter-table relationships exist or are needed. This reflects its flat structure, not a data quality gap, and the 100% referential integrity score confirms nothing is broken. The main integration opportunity is enriching this funding record with external reference data to broaden the analysis. ### 2.2 Recommended Actions - **Add geographic reference data:** Link city or location values in startup_funding to a standardized Indian city and state reference list to enable regional funding comparisons. - **Standardize investor and startup identifiers:** Build a lookup of canonical company and investor names so the same entity can be matched reliably to outside sources such as company registries or public investor directories. - **Bring in sector and market benchmarks:** Join industry classification or market-size data to put funding amounts in context and identify under-served verticals. - **Plan for schema evolution:** If new funding rounds are appended over time, consider separating investors, startups, and rounds into distinct tables so the model scales beyond the current flat design.