### Fit-for-Purpose Verdict **Verdict:** Fit for single-table, time-based analysis of Social_media_impact_on_life. Not fit for anything that depends on combining it with other tables. **What it supports well** - **Time-series analysis on temporal columns.** Social_media_impact_on_life (4,500 rows) is the only table in scope, and its temporal columns support trend, seasonality, and period-over-period views. With completeness at 100%, those series should not have gaps from missing values. **What it cannot support, and why** - **Cross-table aggregation.** No validated joins were detected, so any rollup that blends this table with another source would be unreliable. With only one table in the dataset, this is a scope boundary rather than a defect in the table itself. - **Reading the 100% referential integrity score as a quality signal.** The score is vacuous: no foreign key constraints exist, so none can be violated. It says nothing about whether the data is consistent with any other source. Likewise, the 100% largest-table share only reflects that the dataset has one table. **Top remediation steps** 1. **Treat referential integrity as not applicable.** No FK relationships were detected, so the 100% score reflects the absence of constraints, not verified consistency. Exclude it from any assurance statement about this dataset. 2. **Keep analysis scoped to Social_media_impact_on_life alone.** Run time-series work on its temporal columns, and don't publish cross-table aggregates until a second table is actually brought into scope and its joins are validated.