### Fit-for-Purpose Verdict **Verdict:** `startup_funding` (3,044 rows) is fit for single-table temporal analysis. It is not a basis for any analysis that needs data from other tables. **What it supports well** - **Time-series analysis on temporal columns.** This is the dataset's strength. All 3,044 rows sit in one table, so trends, period-over-period comparisons, and seasonality checks can run on a single consistent source with no join logic. - **Analysis that stays inside `startup_funding`.** The 100% completeness score means the table has no missing values to work around. **What it cannot support, and why** - **Cross-table aggregation.** No validated joins exist, and `startup_funding` is the only table (100% largest-table share). There is nothing to combine it with, so any analysis that depends on other entities or reference data is out of scope until further tables are supplied and their joins validated. - **Any claim resting on the 100% referential integrity score.** That score is vacuous. No foreign key relationships were detected, so there are no constraints to violate. It says nothing about how well the data would connect to other sources. The same caution applies to the 100% overall score, which this figure helps lift. **Top remediation steps** 1. **Treat the 100% referential integrity score as non-informative.** With no FK relationships detected, the score reflects the absence of constraints, not verified linkage. Do not cite it as evidence of relational quality in downstream reporting. 2. **Keep time-series work scoped to `startup_funding` alone.** Until additional tables are introduced and their joins validated, report findings as single-table results and do not present them as cross-table aggregates.