$27K Payroll Error Eliminated: How Make™ Filtering and Mapping Unified HR Data Across Three Systems

HR data silos do not announce themselves. They accumulate quietly — one manual copy-paste between an ATS and an HRIS, one Excel export that gets reformatted before upload, one field that means something different in payroll than it does in your applicant tracking system. Then one day the damage surfaces, and it costs far more than the automation that would have prevented it. This case study examines exactly that scenario: how a mid-market manufacturer lost $27,000 to a single undetected field-mapping error, and what a Make™-powered filtering and mapping pipeline looks like as the structural fix. For the full methodology behind HR data filtering and mapping architecture, see our parent pillar on mastering data filtering and mapping in Make for HR automation.

Snapshot: Context, Constraints, Approach, and Outcomes

Context and Baseline: What the Manual Process Actually Looked Like

Before automation, David’s team managed three distinct systems that did not communicate with each other. Every time a candidate accepted an offer, the sequence was manual: export the accepted-offer record from the ATS, open the HRIS, locate the new employee stub record, and type in approximately 40 fields — name, start date, department, job title, compensation, employment type, manager ID, cost center, and more. Then a separate re-entry process transferred the HRIS compensation record into the payroll provider’s import template.

Two points of failure existed in that chain, and both were invisible until something went wrong.

The ATS stored compensation as a plain text string — “103000” — with no currency formatting and no validation. The HRIS required an annual salary field formatted as a numeric value with decimal precision. When David’s team manually transferred the record, a keystroke error transposed digits: the HRIS received “130000” instead of “103000.” The HRIS accepted the entry without complaint. Payroll processed accordingly. The discrepancy was not caught until a routine audit several months later. By then, $27,000 in overpayments had been processed. The employee, confronted with a payroll correction that reduced their salary, resigned.

Parseur’s Manual Data Entry Report places the average cost of a manual data entry employee at $28,500 per year when accounting for error correction and rework time alone — before accounting for downstream financial consequences like this one. SHRM research documents that the cost of replacing an employee who departs can reach 50 to 200 percent of annual salary. The $27,000 direct cost was only the beginning of the real loss.

Asana’s Anatomy of Work research finds that knowledge workers spend a significant portion of their time on work about work — status updates, data re-entry, and manual coordination — rather than skilled work. For HR teams, manual system-to-system data transfer is the most common and most dangerous form of that overhead.

Approach: Designing the Make™ Pipeline

The solution was not to replace the HR systems — they were functioning correctly within their individual domains. The solution was to build transformation logic between them that enforced data integrity at every handoff. That logic required two distinct capabilities: filtering to validate records before they advanced, and mapping to translate field formats between incompatible schemas.

Step 1 — Define the Field Map Before Building

Before a single Make™ module was configured, David’s team documented every field that needed to transfer between each system pair. For the ATS-to-HRIS handoff alone, the field map covered 43 fields with six distinct data type mismatches — text-to-numeric conversions, date format differences (MM/DD/YYYY vs. ISO 8601), and department names that used different naming conventions in each system.

This documentation step is not optional. Teams that skip it build mapping logic that handles the easy fields and quietly drops the edge cases. The field map becomes the specification the Make™ workflow is built against.

Step 2 — Build the Filtering Layer

The Make™ filtering module was configured as the first gate in the ATS-to-HRIS workflow. Any candidate record that triggered the automation was evaluated against a set of conditions before it was allowed to proceed:

• Offer status must equal “Accepted” (not “Pending” or “Declined”)
• Compensation field must be numeric and within a defined range (floor: $30,000; ceiling: $500,000)
• Start date must be a valid future date in ISO 8601 format
• Required fields (manager ID, cost center, job title) must be non-null
• Employment type must match an approved value set (Full-Time, Part-Time, Contract)

Records that failed any condition were not silently dropped. They were routed to an exception queue — a dedicated channel where David’s team received an alert with the specific condition that failed, the record in question, and a link to correct it in the source system. This meant that bad data was caught at the source, not discovered months later in a payroll audit.

For a deeper breakdown of filtering module configurations for recruitment data, see our satellite on essential Make filters for cleaner recruitment data.

Step 3 — Build the Mapping Layer

Records that passed the filter were then processed by the mapping module. The mapping logic performed the field-level translations the manual process had relied on human accuracy to handle:

• Compensation: ATS plain text string → HRIS numeric field with two decimal places (e.g., “103000” → 103000.00)
• Start date: ATS MM/DD/YYYY format → HRIS ISO 8601 (e.g., “03/15/2025” → “2025-03-15”)
• Department: ATS free-text department name → HRIS department code via lookup table
• Employment type: ATS value “FT” → HRIS value “Full-Time”
• Manager ID: ATS manager email address → HRIS manager employee ID via secondary lookup

The mapping module applied these transformations deterministically on every record. No human judgment required. No manual re-entry. The compensation field that caused the $27,000 error became a calculated output — not a typed value.

For complex resume data mapping scenarios involving ATS custom fields, see our companion satellite on mapping resume data to ATS custom fields.

Step 4 — Extend to the HRIS-to-Payroll Handoff

The second manual handoff — HRIS to payroll — received the same treatment. A separate Make™ scenario triggered whenever a new employee record reached “Active” status in the HRIS. The filtering layer validated compensation range, pay frequency, tax information completeness, and bank routing number format. The mapping layer translated HRIS field names and formats into the exact column structure required by the payroll provider’s import template, then delivered the file via secure transfer automatically.

This eliminated the second manual re-entry point entirely — the one that had historically been a secondary opportunity for the same type of transcription error.

For the broader architecture of connecting HR systems in Make, see our satellite on connecting ATS, HRIS, and payroll in Make.

Implementation: What the Build Actually Required

The pipeline was built without a dedicated IT resource. David’s team worked with an automation partner to scope the field maps, configure the Make™ scenarios, and test with synthetic records before going live with real employee data.

The HRIS in this environment did not have a native Make™ connector. The team used Make™’s HTTP module to interface with the HRIS’s REST API, which required obtaining API credentials and documenting the endpoint structure — a one-time setup task. The ATS had a native Make™ connector, which simplified the trigger configuration significantly.

Key implementation decisions that affected the outcome:

• Exception routing over silent failure: Every filter rejection generated an alert. This was a deliberate choice. Silent drops would have left records in a limbo state — accepted by the ATS but never created in the HRIS — which is as dangerous as the wrong data going through.
• Lookup tables for code translation: Department codes and employment type mappings were stored in a Make™ data store rather than hard-coded into the mapping module. This meant that when the organization added a new department, the lookup table was updated in one place — not in the workflow logic itself.
• Range validation on compensation: The specific filter condition that would have caught the $27K error — compensation must fall between $30,000 and $500,000 — was built from the field map documentation, not added as an afterthought.

For the error-handling architecture that makes this type of pipeline resilient over time, see our satellite on error handling in Make for resilient HR workflows.

Total manual re-entry was eliminated across both handoffs. The team also configured a third scenario — onboarding task triggers — that fired automatically when the HRIS record reached “Active” status, provisioning accounts and notifying the hiring manager without any additional manual steps. For more on eliminating manual entry across the HR data lifecycle, see our satellite on eliminating manual HR data entry with Make.

Results: Before and After

McKinsey Global Institute research on automation and workforce productivity finds that data collection and processing activities are among the highest-ROI targets for automation — precisely because they are high-frequency, low-judgment tasks where deterministic rules outperform human execution at scale. The filtering and mapping pipeline David’s team deployed is a direct instance of that principle applied to HR operations.

Deloitte’s human capital research consistently identifies data fragmentation as a top barrier to strategic HR — not a lack of data, but a lack of trustworthy, unified data that leadership can act on. Gartner similarly cites poor data quality as the leading obstacle to HR analytics adoption. The pipeline addressed both: records that reach the HRIS and payroll now meet a validated schema, which means reporting and analytics downstream reflect reality.

Lessons Learned: What We Would Do Differently

Three decisions made this implementation more durable than a typical first-attempt automation build — and one decision we would change in retrospect.

What worked:

• Field map documentation before build: Teams that skip this step build workflows that work for the first ten records and then surface exceptions no one anticipated. The 43-field documentation exercise felt slow at the time and saved weeks of rework.
• Exception routing from day one: Building the alert queue into the initial design meant the team had visibility into every exception from the first live record. Many teams add error handling after something goes wrong. Building it first means you catch the first edge case, not the tenth.
• Data store for lookup tables: Externalizing the code translation tables from the mapping logic made the workflow maintainable by a non-developer. When HR reorganized two departments six months after go-live, the update took four minutes in the data store, not a workflow rebuild.

What we would change:

• Test with a synthetic full-cycle run earlier: The team tested individual modules in isolation before integrating them end-to-end. A full synthetic run — simulating an offer acceptance through to payroll import — would have surfaced two edge cases earlier in the build cycle. End-to-end testing is not optional; it should be the first test, not the last.

Compliance Considerations

HR data pipelines that touch compensation, health benefits, or personally identifiable information carry regulatory surface area. The filtering layer in this pipeline served a dual purpose: data integrity and scope control. Fields that downstream systems were not authorized to receive — health-related fields from benefits enrollment, for example — were explicitly excluded from the mapping configuration before records were passed to general HRIS or analytics systems.

For organizations operating under GDPR or similar frameworks, this filtering-as-compliance-control approach is practical and auditable: the workflow log shows which fields were included and excluded on every record, producing a traceable data lineage. For a dedicated treatment of compliance filtering architecture, see our satellite on GDPR-compliant HR data filtering with Make.

The Structural Takeaway

The $27,000 payroll error was not caused by carelessness. It was caused by a process design that placed 100 percent of the data integrity burden on human attention during a 40-field manual transfer performed under time pressure. That is a structural failure — and structural failures require structural fixes, not additional vigilance.

Make™ filtering and mapping modules provide the structural fix. Filters enforce data validity before records advance. Mapping modules enforce field-level translation accuracy before records are written. Together, they move the integrity burden from human memory to deterministic logic — the only place where that burden can be reliably held at scale.

Harvard Business Review research on data-driven decision-making documents that organizations with reliable data infrastructure make faster and more accurate decisions — not because they have better analysts, but because the analysts are not spending their time questioning whether the underlying data is correct. That is the compounding return on data-integrity automation: every downstream process that depends on clean HR records becomes more trustworthy the moment the pipeline enforcing that cleanliness is in place.

For organizations ready to build their own unified HR data pipeline, the methodology starts with the parent pillar: production-grade HR automation starts with clean data. Build the filters and mapping logic first. The AI-layer enhancements — and the strategic analytics that leadership wants — are only as reliable as the data foundation underneath them.