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In this session, we return to Scenario 1 and complete the SAQL implementation after introducing a real-world condition: 👉 A single Opportunity influenced by multiple partners (example now added: O1 → P3 in Opportunity_Partner_Map). This change forces CRM Analytics to treat Partner data as a multivalue relationship, requiring explicit handling in SAQL 🧠 What We Complete in This Video We finalize Scenario 1 using mv_to_string() to correctly interpret multiple partner values stored within the dataset and present them in a usable analytical format. This ensures: ✔ No partner influence is lost ✔ The opportunity grain remains intact ✔ Aggregations (Stage_Count, Activity_Count) remain accurate ✔ Partner attribution reflects true many-to-many influence After implementing the change, we evaluate its impact across all previously built scenarios. 🔑 Key Insight from This Session Not every schema change affects every analytical question. Structural changes only matter where that structure is referenced. If a scenario asks “Was a partner involved?” → multivalue is unaffected. If a scenario asks “Which partner?” → must handle multivalue logic explicitly. This distinction is critical when designing scalable lifecycle analytics. 📐 Modeling Principle Reinforced 👉 CRM Analytics preserves relationship truth — it does not guess which partner to keep. 👉 Multivalue fields must be interpreted deliberately in SAQL . 👉 Business questions determine whether structural complexity surfaces in analysis. 👉 Always reassess downstream scenarios after changing relationship grain. ➡️ What Comes Next We will now adapt Scenario 5 to correctly interpret and present partner names when multiple partners influence the same deal. 💬 Comment "GRAIN" if this helped clarify how relationship grain impacts SAQL design — this is one of the most misunderstood areas in CRM Analytics. 🔔Subscribe Techie Avinash YouTube Channel for tips and examples on SAQL. #SalesforceCRMAnalytics #SalesforceEinsteinAnalytics #SalesforceCRMA #SAQL #SAQLCogroup #MultivalueFields #DataGrain #BI #SalesOpportunityLifecycle #AnalyticsEngineering #BusinessDrivenAnalytics #DataModeling #LearningByExample