### 2.1 Integration Assessment The `global_sports_footwear_sales_2018_2026` dataset operates as a fully self-contained, flat table containing 30,000 records. Because it does not rely on internal cross-table relationships, it achieves a flawless 100% referential integrity score with no broken links to resolve. To maximize its strategic value, this foundational sales and consumer behavior data should be enriched by integrating external reference datasets to provide broader market context. ### 2.2 Recommended Actions * **Incorporate Marketing Spend Data:** Link these historical and projected sales records to regional advertising budgets to calculate Return on Ad Spend (ROAS) for specific sports footwear categories. * **Integrate Macroeconomic Indicators:** Enrich the dataset with regional inflation rates or consumer confidence indexes spanning the 2018–2026 timeline to understand how economic shifts impact purchasing behaviors. * **Connect Loyalty Program Metrics:** Transition toward a more robust analytical model by joining external customer relationship management (CRM) data, enabling deeper segmentation of consumer brand loyalty and lifetime value.