Data migration projects are notoriously complex and can be full of surprises. Careful planning is key, whether you’re moving data to a new CRM or implementing a property management system like Yardi Voyager. Yardi Voyager is a powerful platform with a vast array of customization options, meaning a migration must be handled with precision. Unfortunately, many teams underestimate the data part of an implementation, treating it as an afterthought to be cleaned up at the end of a project. This approach often leads to avoidable mistakes that can derail timelines, inflate costs, or compromise data quality. Based on our years of practical experience, there are four common data migration pitfalls we look out for when working with our clients.
1. Missing or Misaligned System Configuration
What can go wrong?
Your migration can hit serious snags if the target system isn’t configured properly to receive the incoming data. Missing reference data (e.g., lookup codes, charts of accounts, and property records) or unconfigured modules mean the incoming data has no home in the new system. Often, teams discover missing migrated data only to find it was caused by misaligned configuration or unmapped fields in the target system. In short, if the new Voyager system’s settings don’t align with the source data, you risk data load errors or gaps that undermine the project.
How to avoid this common data migration pitfall:
- Prepare the target environment early. The first step of migration is assessing and configuring the receiving system’s architecture to handle incoming data, including objects, fields, record types, etc. Before moving any data, ensure the new Voyager system is fully configured to accommodate it.
- Leverage subject matter experts (SMEs). Engage experienced users or consultants who understand the target system’s configuration. Their knowledge can help catch misconfiguration issues that purely technical teams might miss. SMEs play a major role in data migration – they can validate data and spot configuration errors early so they can be fixed well before go-live.
- Perform trial runs and validation. Run test migrations into a QA or sandbox environment. This dry run will highlight any configuration gaps, such as missing picklist values or inactive modules, when you validate the loaded data. Any errors that surface can guide you in adjusting the system setup.
2. Incomplete Object and Field Mapping
What can go wrong?
One of the most common migration pitfalls is incomplete or inaccurate data mapping between the source and target systems. This occurs when certain fields or data objects in the old system aren’t mapped to the new Voyager system or are mapped incorrectly. The result? Data can be lost, corrupted, or end up in the wrong place.
Yardi’s data model will also have fields that didn’t exist in the legacy system. Each unmapped gap is a decision point. If a column required by Yardi isn’t present in your source data, it’s considered a gap that either must be filled from another source or consciously left blank. Skipping over such gaps or misaligned fields can cause inconsistencies and require manual fixes later. In short, bad mapping equals bad data migration.
How to avoid this common data migration pitfall:
- Map from the target backward. One approach is to start with the target schema. Identify Yardi’s mandatory fields and key tables first, then map backward to your source data. Ensuring every required Yardi field has an equivalent source field (or a plan to populate it) closes those gaps. This target-first mapping approach guarantees the new system’s needs drive the migration.
- Thoroughly document and review mappings. Create a detailed data mapping document listing each source field and its target field, including data type and transformation rules. Have both technical team members and business users review it. Don’t assume a field is non-essential – confirm with stakeholders to avoid dropping important data.
- Involve system experts. If possible, involve consultants or team members who deeply understand both the source and target systems’ data structures. These experts are more likely to spot subtle discrepancies, such as a many-to-one relationship that requires aggregating data or a code value that doesn’t directly translate. Their insight can identify potential mapping gaps others might overlook, ensuring a more complete migration.
- Test the mappings with sample data. Don’t wait until a full conversion cycle to verify your mappings. Use a subset of data to run through the mapping and import process. Then, compare the source and target. Are all records present? Do key fields match expected values? This test can reveal if anything was left out or transformed incorrectly.
With a careful mapping strategy and iterative validation, you can ensure no fields are left behind during your migration. The goal is a 1:1 or well-justified match for each data element, ensuring the new system accurately reflects the old system’s information.
3. Insufficient Data Validation
What can go wrong?
Even with perfect mapping, a migration can go astray if you don’t validate the data thoroughly before and after moving it. In many failed projects, data issues slip through because validation was rushed, done by the wrong people, or not done at all. Failing to validate data in migration can lead to undetected errors, missing records, or corrupted information in the target system. The repercussions are serious: You might only realize months later that financial figures are off or customer records are incomplete, leading to inaccurate reporting, compliance risks, or operational disruptions.
A big pitfall is relying solely on IT staff for validation. If they aren’t familiar with how the business uses the data, they might not recognize when something looks wrong. Using inappropriate resources or too few resources for data checking means critical errors go unnoticed until end-users are already frustrated.
How to avoid this common data migration pitfall:
- Validate at multiple stages. Incorporate validation checks at key points, including after data extraction, after transformation, and after loading into the new system. This could include record counts, spot checks of random records, and running reports to compare totals, such as the total A/R balance in the old vs. new system. Post-migration validation is essential to confirm data transferred correctly. Don’t consider the migration done until these checks pass.
- Involve the right people. Data validation shouldn’t fall solely on the shoulders of the IT or migration team. Involve business users and Subject Matter Experts in reviewing the migrated data. They have the contextual knowledge to catch anomalies. Your internal team’s knowledge is vital to confirm data accuracy – they know what “right” looks like and can spot mistakes early. In practice, set up a review committee with representatives from each functional area to sign off on their data.
- Automate and spot-check. Where possible, use scripts or data quality tools to run integrity checks, like ensuring every tenant has a lease and every property has an assigned manager. Automated data validation can catch issues across the whole dataset. However, manual spot checks should also be performed on critical records – sometimes, the human eye will catch a pattern that a script doesn’t. A combination of data integrity rules and human review yields the best results.
- Don’t rush the validation process. Build enough time into the project plan for thorough validation and defect fixing. If anomalies are found, trace them back to the source (was the source data bad, or did something go wrong in migration?) and address the root cause. It might mean doing an extra extraction of cleaner data or adjusting a transformation rule and reloading a subset. It’s better to take a little extra time now than to have end-users uncover issues in production.
By implementing a robust data validation regimen with the right expertise involved, you can be confident that your migrated data is accurate, complete, and ready for business use. This due diligence protects you from unpleasant surprises after go-live.
4. Lack of Functional UAT on Migrated Data
What can go wrong?
Migrating the data is only part of the battle. You also need to ensure the new system works correctly with that data. A common pitfall is skipping or skimping on User Acceptance Testing (UAT) using the migrated data. Without functional testing, you risk going live with a system that doesn’t truly meet user needs or, worse, has hidden bugs triggered by the real data. For example, a workflow might fail because a required field was blank in the migrated dataset.
UAT is often the first chance for end-users to interact with legacy data inside the new system. If you don’t give them that chance, you might discover too late that business processes don’t work as expected. Lease calculations might be off, or tenant portals might display incorrect info. A lack of UAT can lead to low user confidence and urgent fixes after go-live when they’re much costlier.
How to avoid this common data migration pitfall:
- Plan a dedicated UAT phase. Treat UAT as a non-negotiable part of the migration plan that must be conducted on migrated data. After data is loaded into a staging or test instance, have end-users (not just IT) go through day-in-the-life scenarios. This allows the user community to interact with the legacy data in the new system before production and confirm everything functions properly.
- Define test scenarios covering critical functions. Work with business stakeholders to create a list of key functions and reports that must be tested. Pay special attention to reports, workflows, interfaces, and calculations that rely on the migrated data, as these often reveal subtle issues.
- Iterate and fix. Use UAT findings to improve the migration and configuration. It’s common to find some data that was migrated as designed but still causes an application error or odd behavior. When UAT surfaces these problems, have the project team adjust the data, mapping, or configuration and then rerun the migration for those affected areas if needed. Conduct a retest on any fixes to ensure the problem is resolved before go-live.
- Get formal sign-off. Require stakeholders to sign off that the system is acceptable after UAT. This ensures everyone agrees the data and system functionality are ready for prime time. If any department cannot sign off, you likely need another cycle of fixes and testing. While this may extend the timeline slightly, it’s far better than having that department unable to do their work on day one of go-live.
A successful migration isn’t just about moving data; it’s about making sure the business can operate on that data in the new system. UAT is how you validate that in a real-world simulation, so never skip it. It gives users confidence in the new system and greatly reduces post-implementation issues.
Conclusion
Data migration is often the make-or-break phase of a system implementation. These common data migration pitfalls have been responsible for too many projects gone wrong. The good news is that each pitfall can be mitigated with the right approach. By planning thoroughly, involving the right expertise, and testing relentlessly, you set your migration up for success.
Organizations that treat data migration as a priority component of the project tend to go-live on time with accurate, reliable data. A smooth migration isn’t just IT success; it’s business success, laying the groundwork for your team to leverage the new platform immediately and effectively. Contact 33Floors to avoid these common data migration pitfalls and ensure your data migration and implementation needs are met.
