Zamunda Water

AI-Assisted Study Note

This page brings together public scenario links and AI-assisted research notes for study use. Start with the scenario brief, make your own attempt, and open the spoiler section only when you are ready to compare.

Scenario Snapshot

Field	Detail
Start here	Discovery index
Scenario source	Community scenario
Current status	Live
First public date	2021-01
Primary source	Open primary source
Coverage available	Scenario brief + Video or presentation + Discussion or analysis

Why This Scenario Matters

• This entry is included because it appears in the public CTA scenario corpus and has enough public evidence to track for study use.

Only Open If You Have Attempted the Scenario

The section below contains public follow-up links, board-call material, and AI-assisted notes compiled from those public sources.

Open follow-up links, Q&A, and analysis

Follow-Up Links

• Discovery index
• Analysis reference

Board Insights & Common Pitfalls

Generalized Judge Questions

• License Logic: “You chose Partner Licenses for Key Customers. Can you name three CRM features they need that justify the cost over Customer Community Plus?”
• LDV Usage Strategy: “With 3.6 million usage records created monthly, how will your solution perform in 2 years? What is your granular archiving policy?”
• ERP Decommissioning: “The legacy ERP is being retired for CRM. Which system is the Master for Billing Address vs. Service Address during the transition?”
• SSO Flow: “Explain the SSO flow between the LDAP directory and Salesforce. Is it SP-initiated or IdP-initiated? How are new customers provisioned?”
• City-Based Visibility: “How do you ensure a Team Lead in City A cannot see Wastewater Programs or Key Customers in City B?”

Common Mistakes

• Usage Data Bloat: Storing all monthly usage data in standard objects without an off-platform (Snowflake/Heroku) or Big Object strategy.
• Over-Engineering Automation: Using Batch Apex for all usage calculations. Processing should ideally happen at the integration layer or via Platform Events to avoid concurrency limits.
• Big Bang Migration: Failing to define a phased decommissioning strategy for the legacy water ERP, leading to data integrity risks.
• Flat Sharing Model: Relying on a flat role hierarchy or manual sharing for dynamic city-based management instead of Criteria-Based Sharing Rules.

Strong Patterns

• Middleware-Driven Logic: Using the ESB/ETL layer to handle the heavy lifting of usage calculations before pushing results to Salesforce.
• Hot/Cold Data Tiering: Keeping recent usage data in standard objects for billing queries and moving history to Big Objects or External Objects.
• Skinny Tables: Mandating skinny tables for the usage object to handle high-volume list view performance.

Strategic Insights

• The “Waste Program” Automation: Success often hinges on explaining when and where the heavy calculations happen (Salesforce vs. Middleware).
• Legacy Integrity: Success requires a clear System of Record (SoR) definition for each data element during the multi-year decommissioning phase.

Additional Notes

• Utility-themed scenario focusing on person accounts, billing history, and legacy ERP transformation.