### Fit-for-Purpose Verdict **Verdict: fit for single-table, time-based analysis; not yet fit for cross-table analysis.** The 95% overall score is earned on completeness (95%), but it says nothing about whether the five tables can be combined reliably. **What it supports well** - **Time-series analysis on temporal columns.** Each of the three large tables (raw_kaggle at 237,205 rows, return_kaggle at 237,196, serve_kaggle at 237,185) has enough volume for trend, seasonality, and period-over-period work, run one table at a time. **What it cannot support, and why** - **Cross-table aggregation.** No validated joins were detected, so any figure that combines the tables, or attaches players_man_ (462 rows) or players_tournament_man_ (6,422 rows) to the match-level tables, rests on unverified assumptions. Totals could be duplicated or dropped without any signal. - **Reading the 100% referential integrity score as assurance.** It is vacuous. No foreign key constraints exist to violate, so the score reflects the absence of declared relationships, not confirmed consistency between tables. - **Treating the three large tables as interchangeable.** Their row counts are close but not identical (a spread of 20 rows between raw_kaggle and serve_kaggle). Without a validated key, there is no basis for assuming they describe the same records. **Top remediation steps** 1. **Establish and validate join relationships.** No FK relationships were detected, so define candidate keys and test them before relying on any multi-table result. This is the single change that unlocks cross-table use. 2. **Reconcile the small row-count gaps among raw_kaggle, return_kaggle, and serve_kaggle.** Once keys are confirmed, identify which records appear in one table but not the others. 3. **Connect the two player dimensions (players_man_ and players_tournament_man_) to the large tables.** Do this only after step 1 succeeds. Until then, player-level rollups should be treated as unverified.