As a senior content writer and schema specialist writing in your voice, Jeff Arnold, here is a CMS-ready “How-To” guide.

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## How to Prepare Your ATS & HRIS Data for an Upcoming System Migration

Transitioning to a new Applicant Tracking System (ATS) or Human Resources Information System (HRIS) can be a game-changer for your organization, unlocking new levels of efficiency and enabling advanced AI-driven capabilities. However, the success of any system migration hinges entirely on one critical factor: the quality and preparation of your existing data. As I often emphasize, “garbage in, garbage out” applies emphatically to HR automation and AI. This guide will walk you through the essential steps to ready your HR data, ensuring a smooth transition that maximizes the potential of your new system and sets the stage for intelligent automation, rather than inheriting old problems in a shiny new interface.

### 1. Assess Your Current Data Landscape

Before you move a single byte, you need a clear understanding of what data you currently possess. This isn’t just about identifying where your data lives (ATS, HRIS, spreadsheets, shared drives, legacy systems) but also scrutinizing its quality. What types of data do you have—candidate profiles, employee records, performance reviews, compensation details, training logs? How old is it? Is it complete, accurate, and consistent? My advice: conduct a thorough data audit. Pinpoint redundancies, identify missing fields, and flag any inconsistencies in formats or naming conventions. This initial assessment is the diagnostic phase, revealing the scope of work ahead and laying the groundwork for a successful migration and future AI initiatives.

### 2. Define Your Target System’s Data Requirements

Your new ATS or HRIS isn’t just a different interface; it likely has a different data model, field structures, and validation rules. To avoid forcing a square peg into a round hole, you must work closely with your vendor or implementation team to understand the target system’s exact data requirements. What are the mandatory fields? What formats does it accept for dates, names, or addresses? Are there specific picklist values it expects? Understanding these specifications early helps you identify necessary data transformations and potential gaps in your current data. This step is crucial for ensuring that the data you migrate can be fully utilized by the new system, paving the way for the automation and analytics capabilities you’re investing in.

### 3. Cleanse and Standardize Existing Data

This is arguably the most critical step, and one that directly impacts the efficacy of any HR automation or AI tool you plan to leverage. Clean data is the fuel for intelligent systems. Go through your identified data sets and eliminate duplicates, correct typos, and standardize formats across the board. Ensure job titles, department names, skill sets, and location data are consistent. Address incomplete records by either filling in gaps or deciding which incomplete records can be archived or purged. As I discuss in *The Automated Recruiter*, the integrity of your data directly correlates with the accuracy and insights generated by AI – clean, standardized data ensures that automated matching, predictive analytics, and other AI functionalities deliver reliable results, rather than perpetuating errors.

### 4. Map Old Data Fields to New System Fields

With clean data in hand, the next step is to create a comprehensive mapping document that translates your existing data fields to their corresponding fields in the new system. This isn’t always a simple one-to-one match. You might need to merge multiple old fields into a single new one (e.g., “First Name” and “Last Name” into “Full Name”) or split one old field into several new ones (e.g., “Full Address” into “Street,” “City,” “State,” “Zip”). Clearly document any necessary data transformations. Involve key HR stakeholders and your IT team in this process to ensure accuracy and to anticipate how the new data structure will impact reporting, analytics, and existing workflows. A well-executed mapping prevents data loss and ensures continuity of critical HR information.

### 5. Develop a Data Migration Strategy and Timeline

Planning the actual migration is just as important as the preparation. Will you execute a “big bang” migration, moving all data at once, or opt for a phased approach, migrating specific data sets over time? Outline the specific tools or scripts you’ll use, identify the individuals responsible for each segment of the migration, and establish a clear timeline with key milestones. Don’t forget to build in buffer time for unexpected challenges. Crucially, define your data freeze period—the window during which no new data can be entered into the old system to ensure a complete and accurate snapshot for migration. Also, have a robust backup and recovery plan in place; safeguarding your original data is non-negotiable.

### 6. Perform Test Migrations and Validate Data

Never go live without multiple rounds of testing. Conduct small-scale test migrations using a representative subset of your data. After each test, meticulously validate the migrated data in the new system. Check for accuracy (does the data match the source?), completeness (is all the data there?), and integrity (are relationships between records maintained?). Involve end-users in this validation process, as they are best positioned to identify discrepancies that might impact their daily work. These test migrations will help you identify and resolve issues with your mapping, scripts, or processes, allowing you to refine your approach until you’re confident in a successful full migration. This iterative testing is key to a smooth launch and user acceptance.

### 7. Plan for Post-Migration Data Governance

A successful migration isn’t the finish line; it’s the beginning of a new era for your data. To sustain the benefits of your new system and the AI capabilities it enables, you need a robust post-migration data governance plan. Establish clear policies and procedures for ongoing data entry, updates, and maintenance. Define roles for data stewards responsible for ensuring data quality within the new system. Implement automated data validation checks where possible, and regularly review data for accuracy and consistency. By committing to continuous data governance, you ensure that your investment in a new ATS or HRIS continues to deliver value, driving accurate insights and empowering the intelligent automation that fuels modern HR.

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If you’re looking for a speaker who doesn’t just talk theory but shows what’s actually working inside HR today, I’d love to be part of your event. I’m available for keynotes, workshops, breakout sessions, panel discussions, and virtual webinars or masterclasses. Contact me today!