AI HRIS Data Quality Agent

How Cassidy automates header using AI

Step 1: Trigger on schedule or data event

The Workflow activates on a daily schedule or in response to change data capture events from Workday, SuccessFactors EC, Oracle HCM, or payroll systems—ensuring validation runs continuously or at your preferred cadence.

Step 2: Ingest and harmonize HR data

Cassidy extracts worker, job, position, compensation, and organization data via native integrations (RaaS, OData, HCM Extracts, REST APIs), then maps vendor schemas to your canonical data model with effective-dated handling.

Step 3: Execute data quality rules

The Workflow runs your rule engine across dimensions—checking for overlapping assignments, orphaned positions, grade-to-job misalignments, currency inconsistencies, and cross-system variances like headcount parity between HRIS and payroll.

Step 4: Apply safe auto-fixes

Cassidy writes deterministic corrections back to source systems—normalizing codes via EIB, patching records through REST, or generating HDL files for bulk updates—with dry-run validation and full audit logging.

Step 5: Route exceptions to stewards

Non-autofixable issues flow to exception queues in Slack, Teams, or ServiceNow, enriched with lineage, suggested resolutions, and click-to-fix actions so data stewards resolve issues fast.

Step 6: Certify and publish analytics datasets

Once quality thresholds are met, Cassidy promotes validated datasets to your lakehouse or BI layer—complete with DQ scorecards, reconciliation reports, and the provenance signals analysts need to trust the data.