### 2.1 Integration Assessment The dataset consists of a single self-contained table, AI_Usage_and_Impact_on_Students_and_Professionals (300 rows), so no inter-table relationships exist or are needed. Referential integrity scores 100% because there is nothing to break. Its value can grow by linking to external reference sources that add context to the existing records. ### 2.2 Recommended Actions - **Add benchmark context:** Link respondent profiles to published education and labor-force statistics to test whether AI usage patterns in this 300-row sample reflect broader populations. - **Introduce a shared key:** Add a standardized identifier or normalized category fields (e.g., role, field of study) so future survey waves or external sources can be joined reliably. - **Plan for longitudinal growth:** Append new collection periods under a consistent schema to track change in AI adoption and impact over time, rather than relying on a single snapshot. - **Validate before enriching:** Given the small sample size, confirm that categories align with any external source before joining, so enrichment adds insight rather than noise.