Critical Data Migration Strategies

Why This Matters

When cultural heritage institutions move their digital collections—whether upgrading databases, merging archives, or transitioning to new platforms—the stakes couldn't be higher. A botched migration can mean lost provenance records, corrupted image files, or severed connections between objects and their historical context. You're being tested on understanding how data integrity, metadata standards, interoperability, and ethical stewardship intersect during these critical transitions. Migration isn't just an IT problem; it's a preservation problem with direct implications for scholarship, access, and institutional memory.

The strategies below represent the complete lifecycle of responsible data migration in cultural heritage contexts. Don't just memorize the steps—understand what each strategy protects against and why the sequence matters. Exam questions often present scenarios where something goes wrong, and you'll need to identify which strategy was skipped or poorly executed. Think of these as interconnected safeguards: preparation strategies assess what you have, protection strategies keep it safe during transfer, and verification strategies confirm nothing was lost.

---

Preparation: Understanding What You Have

Before moving any data, institutions must thoroughly understand their existing systems and data structures. This diagnostic phase prevents costly surprises mid-migration and establishes the baseline against which success will be measured.

Legacy System Analysis and Documentation

• Assess functionality and limitations of existing systems—understanding what the old system could and couldn't do reveals hidden data structures and workarounds staff may have created
• Document data structures, workflows, and dependencies—this creates an institutional record that survives staff turnover and informs future migrations
• Identify legacy-specific risks including proprietary formats, outdated encoding schemes, and undocumented customizations that may not transfer cleanly

Data Mapping and Transformation

• Map relationships between source and target structures—this crosswalk document becomes the migration's blueprint, showing exactly how each field translates
• Define transformation rules for converting formats, such as date standardization or controlled vocabulary alignment
• Document the mapping process to maintain transparency and create an audit trail for future reference or troubleshooting

---

Quality Control: Ensuring Data Fitness

Migration offers a rare opportunity to improve data quality—but only if institutions actively clean and validate before transfer. Migrating "dirty" data simply moves problems to a new location while adding new ones.

Data Cleansing and Validation

• Remove duplicates, incomplete records, and inaccuracies before migration—this is often the only time institutions systematically review entire collections
• Implement validation rules that check data against predefined standards, such as required fields, acceptable value ranges, and format specifications
• Conduct regular audits throughout migration to catch issues early when they're easier and cheaper to fix

Metadata Preservation and Standardization

• Preserve metadata context during migration—losing the connection between an object and its descriptive, administrative, or structural metadata destroys scholarly value
• Adopt standardized schemas like Dublin Core, VRA Core, or CIDOC-CRM to ensure interoperability with other institutions and future systems
• Document all metadata transformations so future users understand how current records relate to original cataloging decisions

---

Protection: Safeguarding During Transfer

The migration process itself introduces vulnerabilities—data in transit can be corrupted, intercepted, or lost. These strategies create safety nets and security boundaries around the transfer process.

Data Integrity and Security Measures

• Implement encryption and access controls to protect sensitive materials, particularly for collections involving personal data, sacred objects, or donor restrictions
• Maintain regular backups throughout migration—if something fails, you need recovery options that don't require starting over
• Monitor integrity using checksums to detect file corruption or unauthorized alterations during transfer

Phased Migration Approach

• Break migration into manageable phases to reduce risk—smaller batches mean smaller problems if something goes wrong
• Prioritize critical data for early migration to ensure core operations continue while less essential materials wait
• Evaluate each phase's outcomes before proceeding, using pilot phases to identify systemic issues before they affect the entire collection

---

Verification: Confirming Success

Migration isn't complete when files finish transferring—it's complete when institutions have verified that everything arrived intact and functions correctly. This phase catches problems while fixes are still possible.

Testing and Quality Assurance

• Develop comprehensive testing plans that validate both data accuracy and system functionality—can users actually find and use the migrated materials?
• Conduct user acceptance testing (UAT) with actual staff and researchers to ensure migrated data meets real-world needs, not just technical specifications
• Document all testing results and resolve identified issues before declaring migration complete

Post-Migration Data Verification and Reconciliation

• Conduct systematic checks comparing migrated data against source records—automated tools can flag discrepancies for human review
• Reconcile record counts and checksums to confirm nothing was dropped, duplicated, or corrupted during transfer
• Establish ongoing monitoring because some problems only emerge through use, such as broken links or search indexing failures

---

Governance: Managing Human and Legal Dimensions

Technical success means nothing if migrations violate legal requirements or alienate stakeholders. These strategies ensure migrations remain ethical, compliant, and supported by the people who use and manage the data.

Stakeholder Engagement and Communication

• Involve stakeholders early to gather requirements and surface concerns before technical decisions lock in approaches that don't meet user needs
• Maintain consistent communication throughout the project—surprises erode trust and make future migrations harder to support
• Provide training and transition support so users can effectively work with the new system, reducing shadow workarounds that undermine data quality

Legal and Ethical Considerations

• Ensure regulatory compliance with data protection laws like GDPR, NAGPRA, or institutional policies governing sensitive materials
• Address ethical concerns around data ownership, privacy, and access rights—particularly for indigenous materials, personal archives, or contested cultural property
• Document permissions and agreements to create clear records of what can be shared, with whom, and under what conditions

---

Quick Reference Table

---

Self-Check Questions

1. A museum discovers six months after migration that hundreds of object records are missing their provenance fields. Which two strategies most likely failed, and how are they related?

2. Compare and contrast data cleansing and metadata standardization—both improve data quality, but what different problems does each address?

3. An institution migrating indigenous cultural materials faces community concerns about increased online access. Which strategy category should guide their response, and what specific actions might they take?

4. Why does a phased migration approach reduce risk more effectively than migrating everything at once? What verification opportunities does phasing create?

5. If an FRQ presents a scenario where migrated data is technically accurate but staff refuse to use the new system, which overlooked strategy best explains the failure?