A major US healthcare provider moved its HR and payroll operation off Paycom onto Oracle Fusion HCM and Payroll Cloud. Syntra ETL delivered the migration on a purpose-built Paycom → Oracle HDL accelerator, run through a four-phase accuracy ladder — foundational load, iterative refinement, pre-production readiness, then production cutover — taking data accuracy from a 60%+ first load to 99%+ in the live environment, with payroll and timecard reconciliation and hypercare through go-live.
Paycom is a closed, all-in-one HCM suite. Its data is straightforward to work with inside Paycom and considerably less so on the way out — there is no Oracle-shaped export, no HDL awareness, and the objects a Fusion implementation needs (Worker, Assignment, Payroll, Benefits) do not come out in the shape Oracle expects them.
For a healthcare provider, that gap is not a technical inconvenience — it is a payroll risk. Clinical staffing runs on shift patterns, differentials and premium time, so timecard and payroll data has to reconcile exactly, not approximately. And every record in flight is employee PII inside a HIPAA-regulated organisation, so extraction, staging and validation all have to be handled accordingly.
Rather than treat the migration as a single big-bang conversion, the programme was built around a staged accuracy ladder: get a foundational load in early to expose the real mapping problems, then drive accuracy up cycle by cycle until the business could sign off on a production cutover.
Two extraction routes were assessed and used according to what each data set supported.
Paycom exposes RESTful endpoints covering the objects a Fusion migration needs:
For data sets delivered as scheduled files rather than through the API — PTO requests, employee change files and similar — scripted SFTP download and processing pulls the extracts down and feeds them into the same staging and transformation pipeline. Using both routes meant no object had to be re-keyed by hand because one interface didn't cover it.
More on this: Paycom data extraction tool · Paycom ↔ Oracle Fusion integration · Paycom connector overview.
The core of the delivery was a purpose-built accelerator that takes Paycom extracts in and produces Oracle HDL-ready files out — so each migration cycle was a repeatable run rather than a fresh round of spreadsheet work.
Paycom extracts are accepted as CSV through a web interface and loaded into a staging database, so every subsequent step operates on structured, queryable data rather than on files.
Mappings are applied per Oracle object — Worker, Assignment, Payroll, Benefits — so each object carries its own rule set instead of one generic transform being stretched across all of them.
Dates, codes, country and state values and currencies are normalised on the way through — the class of mismatch that otherwise surfaces one HDL error at a time, late, during load.
Paycom values are resolved to their Oracle equivalents through crosswalk tables, which keeps the mapping decisions as data the client can review and amend — not as logic buried in code.
Required fields are checked as populated before anything is exported, and derived values — hire-date formats, person numbers — are generated rather than expected from the source.
Output is Oracle HDL-ready for direct upload, in both full-load and incremental (delta) modes — delta runs being what makes repeated test cycles and a short cutover window affordable.
See Paycom → Oracle Fusion data mapping and load automation for how the accelerator is applied outside this engagement.
Each phase had a stated accuracy target and had to clear it before the next began. Accuracy was the gate, not the calendar.
Objective: establish the ETL framework and validate mappings for the core objects.
Activities: build and test the HDL templates, then run the first extraction and validation in the Fusion test environment.
Outcome: foundational data migration achieved and — the real point of the phase — the key mapping issues identified early, while they were still cheap to fix.
Objective: improve transformation logic and validation rules on the back of what Phase 1 exposed.
Activities: apply corrections, enrich data quality and execute multiple test cycles.
Outcome: accuracy and consistency lifted past 90%, with the major business scenarios validated.
Objective: reach near-production quality — high accuracy and, just as important, stability across runs.
Activities: full-scale data loads, payroll and timecard reconciliation, and migration of attachments and documents.
Outcome: 98%+ accuracy, with formal business sign-off to proceed to cutover.
Objective: final production migration and cutover to Oracle Fusion.
Activities: execute the production ETL, run the reconciliation checks and provide hypercare support through stabilisation.
Outcome: 99%+ accuracy in the live environment, with smooth operations through the first production cycles.
The four phases ran across roughly six months from initial load to go-live. See our ETL migration cutover plan and Paycom migration cutover guidance.
A payroll migration is judged on whether the numbers agree, so reconciliation was a phase activity in its own right rather than a closing check.
Payroll and timecard data reconciled at full scale during pre-production readiness — the phase that had to clear 98% before anyone discussed a cutover date.
Employee attachments and documents migrated alongside the structured objects, so the Fusion record is the complete record rather than the tabular half of one.
Cutover was authorised by business sign-off against a measured accuracy figure — not by a date on a plan. Post-cutover reconciliation checks and hypercare covered the first live cycles.
Related: Paycom payroll data migration · Paycom data validation · Oracle Fusion payroll migration.
Healthcare workforce data is employee PII held inside a HIPAA-regulated organisation, and healthcare payroll carries pay structures that punish approximate migration.
See also Oracle Fusion for Healthcare and our healthcare archival work for Catholic Healthcare Trust and Huggins Hospital.
Each phase of the accuracy ladder leaned on a different part of the platform.
The engine behind the accelerator — API and SFTP extraction from Paycom, staging, object-aware rule-based transformation, crosswalk resolution, HDL generation, and full and delta loads into Oracle Fusion.
Lineage, reconciliation and governance — the payroll and timecard reconciliation evidence, the crosswalk decisions as reviewable data, and the audit record behind each phase sign-off.
Migration analytics — the data-quality, open-exception and cutover-readiness view that turned "are we at 90% yet?" into a measured number the business could sign off against.
A deliberately small core team — the accelerator did the volume work, so the people were there for judgement calls, not keystrokes.
Drove requirements, validations and UAT — the people who decided whether a scenario was actually correct.
Handled the ETL builds, HDL templates and defect fixes across every migration cycle.
Owned architecture, integrations and overall governance across the four phases.
Stage-by-stage detail on moving HR, payroll and benefits data off Paycom onto Oracle Fusion.
Two routes, used according to what each data set supports. Paycom's RESTful API covers employee data (personal details, department, job information), payroll information and reports, time and attendance including punch history, and custom fields and audit logs. For data sets delivered as scheduled files instead — PTO requests, employee change files — scripted SFTP download pulls the extracts and feeds them into the same staging pipeline.
A purpose-built pipeline that takes Paycom CSV extracts, loads them into a staging database, applies object-specific mapping rules for Worker, Assignment, Payroll and Benefits, standardises dates, codes, country/state values and currencies, resolves Paycom values to Oracle values through crosswalk tables, validates that required fields are populated, adds derived fields such as hire-date formats and person numbers, and exports Oracle HDL-ready files — in both full and incremental (delta) modes.
Because the mapping problems you cannot predict are the ones that decide the timeline. Phase 1 deliberately targets a modest 60%+ accuracy to surface those issues early in a test environment. Phase 2 lifts accuracy past 90% through corrected transformation logic and repeated cycles, Phase 3 reaches 98%+ with full-scale loads and payroll reconciliation, and Phase 4 executes production cutover at 99%+. Each phase is gated on its accuracy target rather than on a date.
Through payroll and timecard reconciliation at full scale during pre-production readiness, followed by reconciliation checks against the production load at cutover. Business sign-off against the measured accuracy position was the gate for proceeding to go-live, and hypercare covered the first live cycles.
Yes — attachments and documents were migrated during the pre-production readiness phase alongside the full-scale structured data loads, so the Oracle Fusion employee record is complete rather than tabular-only.
All three. DataMove ran extraction, staging, transformation and the HDL loads. DataVault held lineage, the crosswalk decisions and the reconciliation evidence behind each phase sign-off. DataLens provided the data-quality, exception and cutover-readiness analytics that made the accuracy position a measured number rather than an opinion.
History that does not belong in Oracle Fusion can be preserved in a read-only archive rather than kept on a live Paycom subscription — see Paycom data archival, decommissioning Paycom and payroll & HR data archival.
Tell us your Paycom footprint and target Fusion modules — Core HR, Payroll, Benefits, Time & Absence — and we'll scope a phased, accuracy-gated migration on the Syntra ETL platform, with reconciliation and archival built in.
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