Data Loader
Accelerate bulk updates and document-to-record workflows without custom ETL scripting.
Why teams use this agent
- Reduce manual data entry from spreadsheets and unstructured files.
- Standardize file ingestion workflows with repeatable mapping rules.
- Improve speed for onboarding, migration, and back-office operations.
What it handles
- CSV/flat-file style data import workflows.
- Document extraction into structured fields.
- Mapping validation before write operations.
- Error capture for row-level correction and reprocessing.
Inputs and prerequisites
- Source files with consistent headers and encoding.
- Target object and field mappings in Salesforce.
- Required-field and validation rule awareness for target objects.
- Appropriate create/update permissions for processing users.
Setup and configuration
- Install and grant access to admins and operations users handling data ingestion.
- Define ingestion profiles:
- Object target
- Upsert keys or matching strategy
- Required fields and default values
- Configure transformation rules:
- Field type normalization
- Date/number parsing
- Controlled value mapping
- Configure validation and exception handling:
- Reject, skip, or queue invalid rows
- Error report destination for remediation
- Pilot with historical sample files, then move to production ingestion runs.
Recommended operating model
- Maintain versioned ingestion profiles per business process.
- Use pre-flight validation before large loads.
- Schedule recurring ingestion windows to reduce contention with peak business hours.
- Track failed-record patterns and refine mapping rules continuously.
Governance and controls
- Restrict profile editing and bulk-run permissions.
- Keep audit logs for file processing events and record write actions.
- Apply data handling controls for sensitive fields and regulated objects.
- Validate source provenance before processing external files.
Success metrics
- Time-to-load compared with manual or script-based methods.
- Successful row processing rate on first pass.
- Reduction in post-load data correction effort.
- Throughput for recurring ingestion operations.
Next steps
- Use Checklist Builder to generate remediation plans for repeated data-quality failure patterns.
- Add Account Intelligence after load operations to enrich newly created account records.