### Fit-for-Purpose Verdict **What this dataset supports well** The single-table structure of `polling_on_ai_usage_jul_2026` (105 rows) is immediately usable for time-series analysis on temporal columns. With 100% completeness and no missing values, every row contributes to trend detection, period-over-period comparisons, and longitudinal studies of AI usage sentiment. Analysts can confidently segment responses by demographic attributes, calculate response distributions, and track opinion shifts across the survey timeline without data-quality friction. **What this dataset cannot support and why** Cross-table aggregation, dimensional enrichment, and multi-source correlation are impossible. The dataset contains no validated joins—referential integrity scores 100% only because no foreign-key constraints exist to test. Any attempt to link polling responses to external customer records, product usage logs, or demographic master tables will fail without manual key mapping and unverified assumptions about identifier overlap. Multi-dimensional drill-downs (e.g., "show AI sentiment by customer segment and purchase history") require data not present in this single table. **Top remediation steps** 1. **Establish foreign-key relationships** if `polling_on_ai_usage_jul_2026` contains identifier columns (respondent IDs, account keys) that should link to customer, transaction, or demographic tables; document the join logic and validate referential integrity against those targets. 2. **Append dimension tables** for any categorical fields (industry codes, geographic regions, user cohorts) currently stored as raw strings; normalize these into lookup tables with surrogate keys to enable consistent grouping and reduce storage redundancy. 3. **Ingest complementary datasets** (CRM records, product telemetry, prior survey waves) and define explicit join paths so polling sentiment can be correlated with behavior, outcomes, and historical context—transforming isolated survey responses into actionable intelligence.