Data Conversion Checklist: What to Prepare Before Migration

Key Takeaways

Before diving into data migration, understand that preparation determines success. This checklist covers three critical preparation phases:

• Data inventory and cleanup (identifying what you have and fixing inconsistencies),
• System mapping and field alignment (ensuring your old data fits your new structure)
• Stakeholder coordination (getting the right people involved at the right time). 
• Organizations that follow this structured approach reduce migration delays by months and avoid costly post-go-live data correction.

Here's what most project teams discover three weeks before go-live: the employee addresses they're about to migrate contain fifteen different format variations, their job codes don't actually align with the organizational hierarchy they built in the new system, and nobody can explain why there are three different "department" fields with conflicting information.

Most organizations treat data conversion as a technical task scheduled for the final implementation phase a matter of running extraction scripts, mapping fields, and loading data into the new platform. At Align HCM, we believe conversion success is determined not by technical execution in the final weeks, but by strategic preparation that begins months earlier.

Data conversion preparation delivers value at three critical levels:

• Clean foundational data that eliminates costly rework
• Aligned system architecture that maximizes new platform capabilities
• Coordinated stakeholder engagement that prevents last-minute surprises.

Many implementation project teams still approach data conversion reactively, pulling together spreadsheets and conducting cleanup efforts only when the conversion deadline looms. This reactive approach creates data quality issues that persist for years, custom field misalignments that limit system functionality, and stakeholder conflicts that delay go-live dates. Moving to a proactive conversion preparation strategy resolves these tactical problems, but the strategic gain goes much deeper.

Beyond the Spreadsheet: Three Dimensions of Migration Readiness

1. How Data Inventory and Cleanup Enables Accurate System Foundation

When implementation teams delay data assessment until the conversion window opens, discovering critical data quality issues becomes a crisis rather than a manageable task. Consider the project team that found, ten days before conversion, that 40% of employee records had home addresses formatted as "123 Main" while another 40% used "123 Main Street" and the remaining 20% included apartment numbers inconsistently, all of which needed standardization before the new system's validation rules would accept them.

A comprehensive data inventory process begins 3-4 months before planned conversion, creating a systematic assessment of current data quality across every field you plan to migrate: employee demographics, organizational hierarchies, compensation history, benefits elections, time and attendance records, and performance data. This early assessment allows teams to identify which data elements need cleanup (inconsistent formats, missing required values, obvious errors), which need validation (conflicting information across systems), and which need business decisions (historical records worth keeping versus noise worth discarding). The process transforms data conversion from a technical task performed under deadline pressure into a strategic opportunity to improve information architecture.

With thorough data inventory, implementation teams can answer questions that directly impact migration success:

• Which data fields contain inconsistent formats, missing values, or obvious errors that will prevent successful mapping to the new platform's validation rules?
• What percentage of employee records have complete, validated information versus records requiring manual correction before they can load?
• Which historical data periods (last 2 years? 5 years? 10 years?) contain critical information worth migrating versus legacy data that adds complexity without business value?
• How do data quality issues in current systems correlate with specific departments, locations, or time periods that might reveal systemic problems?
• Which data cleanup tasks can be completed by current system administrators versus which require business stakeholder decisions?

According to Gartner research, poor data quality costs organizations an average of $12.9 million annually, with data cleansing and correction consuming 30-40% of data team resources. [Source: Gartner]

This systematic inventory process is the difference between discovering data problems during conversion chaos and resolving them methodically when there's time to fix them properly.

2. How System Mapping and Field Alignment Drives Platform Capability

When a manufacturing company migrated to a new HCM platform, their implementation team initially planned to recreate all 47 custom fields from their legacy system, including three separate "shift code" fields that had evolved over years of workarounds. Strategic mapping revealed that the new platform's standard shift management eliminated the need for two of those fields entirely, and the third could be consolidated into standard configuration. They eliminated 15 unnecessary custom fields, which reduced their ongoing maintenance burden and enabled standard reporting features that custom fields would have broken.

Strategic system mapping evaluates how current data fields align with new platform capabilities, identifies opportunities to consolidate redundant fields created by legacy system limitations, and determines which customizations represent genuine business requirements versus technical workarounds that the new system handles differently. This mapping process allows teams to move from simply transferring data to intentionally designing information architecture that leverages new system strengths—workflow engines, organizational hierarchies, reporting relationships, and analytics capabilities that weren't possible in older systems. The approach transforms conversion from data movement into platform optimization.

Effective field alignment enables critical decisions:

• Which custom fields from the legacy system represent true business requirements that must be recreated versus workarounds for old system limitations that the new platform handles through standard functionality?
• How can we consolidate overlapping data elements (multiple "department" fields, redundant employee identifiers, duplicate organizational hierarchies) into cleaner structure without losing critical information?
• Which data relationships in the new platform (organizational hierarchies, reporting structures, workflow routing, security roles) require mapping decisions that go beyond simple field-to-field transfers?
• What standard platform fields should we populate to enable advanced features (predictive analytics, automated workflows, employee self-service portals, mobile access) even if we didn't track that data consistently before?
• Which legacy custom reports depend on fields we're considering eliminating, and do those reports represent ongoing business needs or outdated processes?

Based on our analysis of 200+ HCM implementations, organizations that conduct strategic field mapping during preparation reduce post-go-live support tickets by 35% and enable adoption of advanced platform features 6 months faster than organizations that default to one-to-one field replication. [Source: Align HCM proprietary data]

This architectural thinking is the difference between migrating data and building a platform that scales with business growth.

3. Why Stakeholder Coordination Determines Conversion Timeline Success

When a healthcare organization's implementation team discovered that their planned data conversion would simplify 10 years of complex departmental reorganization history into a single "current state" structure, department heads reacted strongly but this discovery happened during the conversion testing phase, just three weeks before go-live. Redesigning the approach to preserve critical historical reporting relationships required an eight-week delay and significant rework that could have been avoided with earlier stakeholder input.

A structured stakeholder engagement model identifies who needs to be involved in conversion decisions (data owners who understand current systems, process experts who know how data is used, executive sponsors who approve trade-offs), when their input is critical (inventory review to validate data quality assessments, mapping validation to ensure business needs are met, testing approval to confirm results match expectations), and how to communicate progress and issues proactively through regular updates and clear escalation paths. This coordination allows teams to move from surprise-driven escalations that threaten timelines to informed decision-making where trade-offs are understood and accepted before conversion begins. The process transforms potentially contentious conversion issues into collaborative problem-solving where business leaders become advocates rather than obstacles.

Strategic stakeholder coordination answers:

• Who has authority to approve data cleanup decisions that might delete records, standardize inconsistent information, or modify historical data in ways that affect reporting?
• Which business leaders need advance notice of what historical data will and won't migrate so they can set appropriate expectations with their teams about reporting limitations?
• How should we prioritize competing conversion requirements when different departments have conflicting needs for historical data depth, custom field migration, or data structure changes?
• What approval gates and sign-offs are required before beginning conversion activities (test conversions, production conversion, historical data archiving) that can't be easily reversed?
• Which stakeholders should participate in conversion testing to validate that migrated data meets business requirements before committing to production?

According to the Project Management Institute, projects with highly engaged stakeholders are 2.5 times more likely to succeed, while inadequate stakeholder engagement is cited as the primary cause of project failure 39% of the time. [Source: PMI Pulse of the Profession]

This proactive communication is the difference between conversion delays caused by late-stage stakeholder objections and smooth migrations where everyone understands and supports the plan.

Building Migration Readiness Into Implementation Strategy

The decision to prepare thoroughly for data conversion requires discipline and foresight, especially when project timelines feel aggressive and conversion seems months away. Most implementation plans treat conversion as a discrete event happening in the final phase—a technical milestone to be executed once everything else is ready.

But migration readiness and the clean, strategically aligned data foundation it creates—is built through consistent preparation that begins when implementation planning starts. Starting data inventory during requirements gathering, conducting field mapping while configuring the new system, and engaging stakeholders throughout design decisions transforms data conversion from a high-risk technical event into a natural progression where teams know exactly what data they're moving, where it's going, and why it matters.

At Align HCM, our vendor-agnostic implementation approach focuses on helping project teams build conversion readiness into every project phase rather than treating it as a final deadline. We work with you to assess data quality during discovery, map fields strategically during design, and coordinate stakeholders proactively throughout implementation so that when conversion windows open, you're executing a well-understood plan rather than improvising under pressure. The result is not just successful data migration but a stronger foundation for system value, user adoption, and ongoing data quality from day one.

Ready to assess your migration readiness before conversion deadlines create pressure? We'll analyze your current data environment, review your implementation timeline, and identify the preparation steps that matter most for your specific situation. Request a conversion readiness assessment below.