Infor Lawson Data Validation — Post-Load Reconciliation to the Cent

Infor lawson data validation is the structured verification that data migrated from Lawson S3 into Oracle Fusion is complete, accurate, and reconciles to the source. It covers four layers: structural validation (every Lawson record produced a Fusion record, no rows dropped silently), value validation (every Lawson field value maps to a defensible Fusion value, no silent type conversions corrupting data), business-rule validation (Fusion trial balance matches Lawson trial balance per AU per period, AP aging matches, AR aging matches, HRPER worker count matches HCM Worker count, INVMASTER quantity matches Fusion on-hand), and audit validation (every Lawson record's lineage is traceable end-to-end into Fusion with hash-signed evidence for HIPAA and SOX). Syntra ETL ships infor lawson data validation as a built-in capability — every load produces a validation pack as standard output.

Testing is what the project team does before go-live to confirm the migration works on sample data. Validation is what gets run on production data during cutover (and replayed for ongoing audit evidence) to confirm the actual loaded data reconciles to the source. Testing might run on a 10% sample of GLMASTER. Validation runs on 100% of GLMASTER, reconciles every AU per period to the cent, and produces signed evidence that finance can hand to auditors. Syntra ETL automates both: testing runs on every change to the mapping or extractor configuration, validation runs on every production load. Both produce structured outputs that feed the SOX, HIPAA and CMS evidence requirements.

Six categories. (1) Record counts: Lawson source count vs Fusion loaded count per entity per process level per period. Variance threshold zero. (2) Sum totals: GL trial balance per AU per period, AP open per supplier, AR open per customer, payroll gross per pay period, inventory value per warehouse — Lawson vs Fusion. (3) Hash totals: each Lawson record content-hashed at source, re-hashed post-Fusion-load. (4) Business rules: Fusion intercompany balancing, AP three-way match consistency, HR effective-date continuity, benefits enrollment validity. (5) Referential integrity: every Fusion AP invoice has a valid supplier, every Fusion HCM assignment has a valid worker, every Fusion journal has valid COA combinations. (6) Audit lineage: every Fusion record traces back to Lawson source with full hash chain.

After GLMASTER, APINVOICE and ARCUST data lands in Fusion GL, Payables and Receivables, the reconciliation engine pulls the Fusion trial balance per AU per period via the Fusion REST API, pulls the Lawson trial balance per AU per period from the source archive, and compares to the cent per Lawson process level per period. AP aging is reconciled per supplier, AR aging per customer, journal counts per period per AU. Variances surface immediately with drill-down to the specific journal, invoice or customer record that failed to reconcile. The output is a finance-grade reconciliation pack: Lawson trial balance vs Fusion trial balance side-by-side, signed and timestamped, drillable to source document. CFO signs off period-by-period before parallel-run exits.

HR validation focuses on three dimensions. Worker population: HRPER active worker count vs Fusion HCM active worker count, terminated worker count per fiscal year vs Fusion HCM terminated count, contingent worker count if applicable. Assignment continuity: every HRPER worker's complete effective-dated assignment history present in Fusion HCM with no gaps, no overlapping segments, no missing dates. Compensation continuity: payroll gross per pay period per worker per process level reconciled Lawson vs Fusion, base salary effective-dated history preserved, EMDEDMASTR benefit deductions reconciled per pay period per worker. Healthcare-specific extensions (licensure, certifications, continuing education credits) validated per worker. Higher-ed-specific extensions (student employment flag, grant-funded position attribution) validated per worker. HR Director signs off.

Mongoose-built custom tables that survive migration (typically routed to VBCS extension tables or Fusion FND lookups) are validated like any other domain. Row counts per custom table reconciled source vs target. Key-field uniqueness validated. Cross-table referential integrity validated (a Mongoose-built nursing certification table referencing HRPER must continue to reference valid HCM Worker records post-migration). For Mongoose tables retired during migration, the historical data is preserved in the long-term Lawson archive with full hash-signed lineage so audit queries can still trace certifications, equipment maintenance history or department-specific approval queue history when needed.

Three evidence artifacts. (1) Lineage pack: every PHI-containing Fusion record (HCM worker, dependent, benefits enrollment) traces back to its Lawson HRPER / EMDEDMASTR source record with hash-signed chain. (2) Access log: every read-access to PHI-containing data during the migration logged with timestamp, user identity and purpose — meets HIPAA Access Audit Log requirements. (3) Reconciliation pack: signed, timestamped reconciliation showing every Lawson PHI record was preserved into Fusion (or routed to retained archive) with no silent dropouts. CMS auditors receive the equivalent reconciliation for revenue-cycle data (Lawson AR linked to patient accounting), with proof that no patient charge was lost in translation. Audit teams routinely sign off on these packs without follow-up requests.

Yes — and this is often the first time customers see their actual data quality clearly. Lawson estates running for 20 years accumulate dirty data: duplicate vendor records under slightly different names, customer addresses missing state codes, HRPER records with invalid SSNs, EMDEDMASTR records with future-dated termination but no termination event in PAEMPLOYEE, inventory items with negative on-hand that nobody reconciled. The validation engine surfaces every quality issue during the test loads, with proposed cleanup rules. Customers commonly clean up 5–15% of records before final cutover — meaning Fusion starts life with materially cleaner data than Lawson ever had. The cleanup decisions are logged so auditors understand exactly what changed and why.