### Fit-for-Purpose Verdict **Verdict: fit for single-table time-series analysis, not for cross-table aggregation.** **What it supports well** `semi_conductor_se` holds 422,729 rows, and its temporal columns support time-series analysis. Trend, seasonality, and period-over-period comparisons can be run directly against this table. The 100% completeness score means those analyses won't be distorted by missing values. **What it cannot support, and why** No validated joins were detected, so cross-table aggregation is unreliable. Because `semi_conductor_se` is the only table in the dataset (100% largest-table share), there is nothing to aggregate across. Any metric that needs a second source, such as a dimension lookup or a related fact table, cannot be built from this dataset as it stands. **Reading the headline scores correctly** The 100% overall score and the 100% referential integrity score do not mean the same thing. Completeness is a measured property of the data. Referential integrity is vacuous: no foreign key relationships exist, so there are no constraints to violate. Treat the overall score as reflecting a clean, complete single table, not a verified relational model. **Top remediation steps** 1. **Don't cite referential integrity as evidence of quality.** No FK relationships were detected, so the 100% score reflects the absence of constraints, not validated relationships. Qualify it wherever the scorecard is shared. 2. **Scope reporting to `semi_conductor_se`.** Frame conclusions as single-table, time-based findings. Cross-table claims are out of scope until additional tables are added to the dataset.