How to Cleanse and Standardize Employee Data for HRIS Migration Using Make.com Filtering and Mapping
Migrating to a new Human Resources Information System (HRIS) is a significant undertaking that promises enhanced efficiency and better data insights. However, the success of this migration hinges critically on the quality of your existing employee data. Dirty, inconsistent, or poorly structured data can lead to serious headaches, from erroneous payrolls to compliance issues. This guide provides a practical, step-by-step approach using Make.com’s powerful filtering and mapping tools to cleanse and standardize your employee data, ensuring a smooth, accurate, and successful HRIS migration. By leveraging Make.com, even complex data transformation tasks become manageable, empowering your team to prepare data with precision and confidence.
Step 1: Define Your Data Requirements and Audit Existing Sources
Before any data transformation begins, a clear understanding of your target HRIS’s data schema and the current state of your source data is paramount. Start by obtaining the exact data specifications from your new HRIS vendor, noting required fields, data types, and acceptable formats (e.g., date formats, naming conventions for job titles, consistent department names). Simultaneously, conduct a thorough audit of your existing employee data across all current sources, identifying common inconsistencies, missing values, duplicates, and non-standard entries. This initial phase helps you pinpoint specific cleansing and standardization challenges you’ll need to address in Make.com, creating a roadmap for your data transformation workflow and ensuring no critical field is overlooked.
Step 2: Set Up Your Initial Make.com Scenario and Source Connections
With your data requirements defined, the next step is to initiate your data cleansing scenario within Make.com. Begin by creating a new scenario and integrating the modules necessary to connect to your source data. This might involve using modules like ‘Google Sheets’ if your data resides in spreadsheets, ‘Webhooks’ for pushing data, ‘Microsoft Excel’ for files, or even database connectors if applicable. Configure your read modules to pull all relevant employee data fields. It’s crucial to select all columns that might contain necessary information, even if they seem irrelevant at first, as they might be needed for lookups or derivations later in the standardization process. Ensure secure and efficient data retrieval by setting up proper authentication for each connection.
Step 3: Implement Filtering for Initial Data Cleansing
Make.com’s filtering feature is your first line of defense against dirty data. After pulling your raw data, insert a ‘Filter’ module to remove records that do not meet your basic criteria or are clearly invalid. For instance, you might filter out records with completely missing essential identifiers (like Employee ID), inactive employees no longer relevant for the migration, or entries with obviously erroneous data types (e.g., text in a numeric field). Apply conditions such as “Employee ID exists,” “Status is Active,” or “Date of Birth is before today.” This initial filtering stage drastically reduces the volume of data you need to process in subsequent steps, making the overall transformation more efficient and focusing your efforts on records that are genuinely worth standardizing.
Step 4: Utilize Mapping for Data Standardization and Transformation
Once your data is filtered, Make.com’s ‘Mapping’ functionality becomes essential for standardization. Insert modules like ‘Text Parser,’ ‘Number Formatter,’ ‘Date Formatter,’ or ‘Aggregator’ as needed. For example, use ‘Text Parser’ to trim whitespace, convert text to proper case for names, or standardize department names by mapping variations (e.g., “IT Dept,” “Information Technology,” “I.T.” all become “Information Technology”). You can also use ‘Set Multiple Variables’ or ‘Router’ modules to handle complex conditional mapping where different rules apply based on specific field values. This step is about ensuring every data point conforms precisely to the new HRIS’s specifications, addressing issues like inconsistent date formats, varying job titles, or non-standardized location names.
Step 5: Implement Lookups and Deduplication for Data Enrichment and Accuracy
To further enhance data quality, leverage Make.com for lookups and deduplication. If your HRIS requires specific codes for locations or departments that are not in your source data, use a ‘Lookup’ function against a separate reference table (e.g., in Google Sheets or a database) to enrich employee records with these necessary values. For deduplication, design a flow that identifies and merges or flags duplicate employee records based on unique identifiers like email addresses or combined name and birthdate. This might involve reading all records, grouping them by potential duplicates, and then selecting the most complete or recent record to proceed with. This step ensures data integrity and prevents redundant entries in the new HRIS, which can otherwise lead to significant administrative challenges post-migration.
Step 6: Final Validation and Export to HRIS-Ready Format
The final step in your Make.com scenario involves a comprehensive validation of the transformed data and its export into the format required by your new HRIS. Implement a ‘Filter’ module at the very end to catch any remaining anomalies that might have slipped through, such as records where critical fields are still unexpectedly blank after all transformations. Use modules like ‘CSV Creator,’ ‘JSON Creator,’ or a specific HRIS connector (if available) to format the cleansed and standardized data precisely as specified by your vendor. Before the actual migration, run a small test batch through your Make.com scenario and import it into a staging environment of your HRIS. This allows for a final review, ensuring all data points align perfectly and the migration process is truly seamless and error-free.
If you would like to read more, we recommend this article: The Automated Recruiter’s Edge: Clean Data Workflows with Make Filtering & Mapping
