HRIS migration fails when dirty data enters the new system. Make.com’s filtering and mapping tools let HR teams cleanse, standardize, and transform employee records before migration day — no developer required. The six steps below cover audit, scenario setup, filtering, field mapping, validation, and final export for a clean cutover.

Step 1: Audit Your Source Data Before Building Anything in Make

Before you open Make.com, get the exact field specifications from your new HRIS vendor — required fields, data types, acceptable date formats, and naming conventions for departments and job titles. Then pull a full export of your current employee data and inventory what you have versus what the new system expects.

Common gaps found at this stage:

• Inconsistent date formats (MM/DD/YYYY vs. YYYY-MM-DD)
• Department names entered three different ways across records
• Missing Employee IDs on legacy records
• Terminated employees mixed in with active headcount

Document every mismatch. That list becomes the build spec for your Make scenario. Skipping this step is the single biggest reason HRIS migrations require a second cleanup pass after go-live.

Related: HRIS Required Fields vs Manual Data Validation: Which Is Safer for Small HR Teams?

Step 2: Connect Your Source Data to a Make Scenario

Create a new Make scenario and add the module that connects to your source data. Most HR teams work from Google Sheets, Microsoft Excel, or a CSV export from their legacy HRIS. Make has native modules for all three.

Configuration checklist for this step:

• Pull all columns — fields that look irrelevant at setup regularly surface as required for lookups or derivations in downstream steps
• Set up OAuth or API key authentication for each connection
• Run the module once to confirm the field list matches your audit inventory
• Name the module clearly (e.g., “Pull Employee Master from Google Sheets”) — not “Module 1”

If your source data lives in multiple systems — separate spreadsheets for benefits, payroll, and headcount — use an Iterator or Aggregator module to merge them into a single record stream before filtering.

Step 3: Filter Out Invalid and Inactive Records First

Add a Filter module immediately after your source connection. Filtering early reduces the volume of records flowing through every downstream step, which saves operations and speeds up the scenario.

Standard filter conditions for an HRIS migration:

• Employee ID exists — exclude blank or null IDs
• Status = Active — exclude terminated or on-leave employees not going into the new system
• Date of Hire is a valid date — catch text entered in a date field
• Email is not blank — required field for most modern HRIS platforms

Records that fail the filter don’t disappear — route them to a separate Google Sheet labeled “Exceptions.” Your team reviews that list manually before cutover. This is faster than trying to fix bad data inside the transformation logic.

Step 4: Map and Standardize Fields to HRIS Specifications

After filtering, the mapping step transforms what you have into what the new HRIS expects. Make’s built-in functions handle the most common transformations without custom code.

Transformations by data type:

• Text fields (names, departments, titles): Use trim() to remove leading/trailing spaces, upper() or lower() to normalize case, and replace() to standardize variants — “IT Dept,” “I.T.,” and “Information Technology” all become “Information Technology”
• Date fields: Use formatDate() to convert any incoming format to the exact format your HRIS requires
• Phone numbers: Strip non-numeric characters with replace(), then reformat to your target pattern
• Compensation fields: Use parseNumber() to strip currency symbols and commas before passing to a numeric field

For department name standardization at scale, build a Make data store that maps every legacy variant to the correct canonical value. Your scenario does a lookup against the data store instead of hardcoding dozens of if() conditions. This also makes the mapping table editable without touching the scenario.

Related: How a Non-Technical HR Team Started Building Their Own Automations With Make + AI

Step 5: Validate Transformed Records With a Test Run Before Full Export

Before running your full dataset through the scenario, execute a test run on a representative sample — 50 to 100 records that include edge cases: part-time employees, executives with non-standard titles, employees hired across multiple locations.

Validation checklist:

• Spot-check 10 records manually against the original source data
• Confirm date formats match the HRIS import spec exactly
• Verify department names match the HRIS master list
• Check that no required fields are blank in the output
• Review the Exceptions sheet — confirm the right records land there

Add an error handler to every external API or HTTP module in the scenario (Break with 3 retry attempts at 60-second intervals). If the destination HRIS rejects a record during import, the error surfaces in your Make execution log with the specific record ID — not silently dropped.

Related: How to Set Up Routed Error Handling in Make With AI Assistance

Step 6: Run the Full Export and Confirm Record Counts

Once your test run passes, execute the full scenario. After it completes, run two reconciliation checks:

1. Record count: Total records in your source minus filtered exceptions should equal records in the output file. Any discrepancy means records were dropped somewhere in the scenario.
2. Field completeness: For each required HRIS field, confirm zero blank values in the output. A quick pivot table on your output Google Sheet catches this in under two minutes.

Export the clean data in the exact format your HRIS vendor requires — CSV, XLSX, or direct API push — and hand it to your implementation team with the Exceptions sheet attached. The exceptions document becomes your remediation backlog, not a surprise on go-live day.

Related: 6 Ways the Make MCP Changes Automation Work for HR Teams

Frequently Asked Questions

How long does it take to build this Make scenario?

A straightforward HRIS migration scenario — one source, standard field mapping, filter plus exception routing — takes 4 to 8 hours to build and test. Scenarios with multiple source systems or complex department hierarchies run 12 to 20 hours. The audit in Step 1 is the variable: a clean source cuts build time; a messy source extends it.

Do I need a developer to set this up in Make?

No. Make’s visual builder handles everything in this guide without code. The most technically demanding step is building the data store lookup for department standardization, which is a form-based configuration in Make — no scripting required. A non-technical HR administrator familiar with basic Make flows can execute this workflow.

What if my HRIS vendor provides an API instead of a file import?

Use Make’s HTTP module to push records directly to the HRIS API endpoint instead of exporting to a file. The filtering and mapping steps stay identical — only the final delivery module changes. Add an error handler on the HTTP module to catch rejected records and route them to your Exceptions sheet automatically.

What is OpsMap and when does it apply to HRIS migration?

OpsMap™ is 4Spot’s pre-automation discovery process that maps every process, handoff, and data source before a single scenario gets built. For HRIS migrations, an OpsMap™ engagement surfaces data quality issues, source system gaps, and field-mapping conflicts before the build starts — cutting scenario rework and implementation delays. Learn more: What Is OpsMap? The Discovery Step That Prevents Automation Mistakes.