Why data integrity matters in Flows
Salesforce Flows are powerful automation tools, but with great power comes responsibility. Ensuring data integrity prevents bad data, enforces business rules, and avoids cascading errors across records and integrations. When designing Flows, aim for predictable, auditable, and secure changes.
Core principles to follow
Apply these principles consistently:
– Validate early: stop invalid inputs before they persist.
– Fail fast and clearly: surface meaningful errors to users and logs.
– Preserve atomicity: ensure a Flow’s transaction either fully succeeds or cleanly rolls back.
– Respect security and sharing: follow FLS, CRUD, and sharing rules.
– Design for bulk: handle collections and avoid row-by-row operations.
Techniques to ensure data integrity
1) Use Entry Criteria and Record Filters
Prevent inappropriate records from triggering the Flow by setting precise entry criteria on Record-Triggered Flows or using Get Records with strict filters.
2) Prefer Before-Save Flows for fast validation and updates
Before-Save (fast field updates) Flows run before DML and are more efficient for simple field updates and validations. Use them to normalize values, set default fields, or block bad values by throwing a flow error.
3) Validate with Decision elements and custom error messages
Use Decision elements to check business rules and the Fault path to throw clear errors. For UI-triggered Flows, you can surface user-friendly messages so users can correct inputs immediately.
4) Use Validation Rules and Apex for complex rules
Where possible, push core business validations into platform Validation Rules or Apex (for complex logic). Validation Rules are enforced consistently across UI, API, and Flows.
5) Implement Fault Paths and Error Handling
Always add Fault paths on elements that perform DML or callouts. Capture errors to custom objects or a “Flow_Error_Log” object so admins can review and act. Example pseudo-logic:
// Fault path handler
Create record on Flow_Error_Log with fields: FlowName, RecordId, ErrorMessage, Context
6) Maintain Transaction Control
Flows run in a single transaction; if an unhandled exception occurs, Salesforce rolls back the transaction. Use this behavior to preserve atomicity, but catch and log errors where you need partial success or compensating actions.
7) Prevent race conditions and locking issues
Be careful with simultaneous updates to the same records (e.g., multiple record-triggered Flows and integrations). Strategies:
– Reduce conflicting updates by moving logic to before-save flows when only field updates are needed.
– Use Platform Events or scheduled Flows for asynchronous processing to avoid record locks.
– Batch updates using Collections and a single Update Records element to minimize DML calls.
8) Respect Security: CRUD & FLS
Flows can run as System or Run As User. If your Flow is exposed to users, make sure it respects CRUD/FLS or explicitly checks permissions using the Get Records and Decision elements or use the “Run Flow as System” only when appropriate and secure.
9) Bulkify and limit DML/Queries
Process collections instead of looping single records with DML inside loops. Use Aggregate and optimized queries to avoid hitting governor limits and to keep data consistent during bulk operations.
10) Test, Monitor and Audit
– Use Flow Debug and Debug Logs for development testing.
– Use the Flow Interviews and Paused Flow interviews monitoring to inspect failed runs.
– Build an audit trail: create change-log records or use a custom object to store before/after snapshots for critical updates.
– Add automated tests (Apex tests) if using Apex invocable actions to ensure end-to-end behavior.
Sample: Simple validation in a Record-Triggered Flow
Use a before-save Record-Triggered Flow on Opportunity to ensure Discount__c is not greater than 50%:
// Decision: Is Discount__c > 0.5?
// Fault: Use a Flow Error to stop the save with message: "Discount cannot exceed 50%"
Checklist for production readiness
– Entry criteria defined and minimal.
– Fault paths for all DML and external actions.
– Bulk-safe logic (no DML inside loops).
– Validation Rules/Apex for cross-object or complex checks.
– Logging and monitoring in place.
– Proper permission model and sharing tested.
– Regression-tested with bulk records and concurrent updates.
Conclusion
Data integrity in Flows is a combination of design patterns, platform features, and operational processes. Use before-save flows where possible, add robust validation and fault handling, respect security, bulkify your logic, and monitor Flow runs in production. These practices will keep your Salesforce data reliable and predictable.