Avoid Common Failure Points

Every great hero knows that preparation is the key to triumph. Data migrations may not involve capes or cosmic battles, but the stakes are just as high. Most failures don’t arise from insurmountable technical challenges—they stem from avoidable mistakes that derail timelines, inflate costs, and erode trust.

The good news? Many of these issues can be mitigated with proactive planning and the right tools.

Let’s talk about practical strategies for avoiding the top six failure points in data migrations.

1. The villain of mistrust: Misaligned stakeholder expectations

Stakeholders often expect the new system to deliver immediate improvements post-migration, unaware that lift-and-shift migrations typically preserve inefficiencies temporarily. If the goals and limitations of the migration aren’t clearly communicated, stakeholders may grow frustrated when inefficiencies persist or optimizations take time to materialize.

To avoid this, define the scope of the migration early and communicate whether the priority is parity or refactoring with optimization. Use visual validation reports to demonstrate progress and parity, helping stakeholders understand how the migration is unfolding.

Incorporating regular stakeholder checkpoints throughout the process can also align expectations and foster collaboration.

2. The nemesis of surprise: Underestimating edge cases

Edge cases, like time zone mismatches or rare data formats, often go unnoticed during planning but can wreak havoc during or after migration. Legacy systems frequently contain undocumented behaviors that are difficult to translate to modern platforms. These issues can disrupt workflows or create discrepancies that are hard to trace.

Data profiling tools can help identify anomalies and outliers before migration begins. Explicitly validating edge cases during parallel runs ensures these scenarios don’t slip through the cracks. Building a catalog of known edge cases and simulating them in test environments can provide an added layer of assurance and prevent post-migration surprises.

3. The overlooked battle: Inadequate parallel runs

Parallel runs, where both systems operate simultaneously, are critical for catching discrepancies before the cutover. However, many teams rush through this phase, treating it as a formality. This often leads to undetected data drift or mismatches, which only become apparent after the migration is complete.

Thorough planning for a robust parallel run phase is essential. Extending the dual-system period gives teams the time needed to monitor for drift and validate workflows comprehensively. Business-critical workflows, such as revenue calculations or customer reporting, should take priority during validation to minimize risk in high-impact areas.

4. The vagueness villain: Poorly defined success metrics

When teams focus solely on technical parity—ensuring that data matches between systems—they often overlook the broader business outcomes stakeholders care about. Without clear metrics for success, even a technically flawless migration can feel incomplete or unsatisfying to stakeholders.

Initial success metrics should include data parity; as you mature your new platform, metrics around performance improvements, such as faster query times or operational readiness are also appropriate . Defining these metrics upfront and measuring progress against them throughout the migration ensures alignment with stakeholder priorities. Including post-migration metrics to assess ongoing improvements, like cost reductions or better reporting accuracy, provides a fuller picture of success.

5. The forgotten finale: Neglecting post-migration optimizations

Many teams see migration as the final step, leaving optimization and technical debt cleanup indefinitely postponed. Over time, inefficiencies carried over during the migration can undermine the value of the new system. Without a roadmap, these deferred tasks often fall by the wayside.

A clear post-migration roadmap is essential. This should outline tasks like query optimization, cost reduction, and workflow modernization. By allocating dedicated resources to these efforts, teams can ensure they’re prioritized even amidst competing demands. Using validation reports to identify high-impact areas for improvement post-migration can make this process more focused and effective.

6. The silent threat: Overlooking data governance and security

Lack of data governance and security can create significant risks, from regulatory non-compliance to security breaches. Policies like access controls, encryption, and PII masking must be consistently applied in the new system.

A pre-migration governance audit can identify gaps and ensure compliance policies are documented and implemented. Post-migration, automated governance checks help enforce these policies, ensuring the system operates securely from day one. Neglecting this step risks introducing vulnerabilities that could have been avoided.

Proactive planning is key

Avoiding failure points isn’t about achieving perfection; it’s about anticipating challenges and resolving them before they escalate. By leveraging tools like Datafold’s Migration Agent, teams can validate data parity with precision, catch edge cases early, monitor for drift iteratively, and build stakeholder trust with clear validation reports.

The most successful migrations aren’t just technically sound—they’re strategically executed to avoid costly missteps while delivering long-term value.