The Data Migration Risk Register: What Padiso Tracks on Every BI Migration

Table of Contents

1. Why Data Migration Risk Registers Matter
2. The 18-Item Risk Register Framework
3. Technical Risks and Mitigations
4. Operational and Governance Risks
5. Security and Compliance Risks
6. User Adoption and Change Management Risks
7. Implementing Your Own Risk Register
8. Monitoring and Escalation Protocols
9. Post-Migration Validation
10. Next Steps: Building Your Migration Strategy

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Why Data Migration Risk Registers Matter

Data migrations are not optional activities—they’re existential events for any organisation modernising its technology stack. Whether you’re moving from legacy systems to cloud-native infrastructure, consolidating multiple data warehouses, or replacing outdated BI platforms, the stakes are absolute: downtime, data loss, regulatory violations, and user revolt.

At Padiso, we’ve shipped over 50+ successful data migrations across seed-stage startups through to enterprise rollouts. The difference between migrations that land cleanly and those that spiral into multi-month remediation efforts isn’t luck. It’s discipline. It’s a structured, repeatable risk register that captures every failure mode we’ve seen (or prevented) in the field.

This guide walks you through the exact 18-item risk register we deploy on every BI migration engagement. Each item includes the failure mode, why it matters, and the specific mitigations we’ve battle-tested in production environments.

The Cost of Getting Migration Wrong

A failed data migration isn’t just a technical embarrassment. It’s expensive. Industry benchmarks show that unplanned downtime during migrations costs organisations £5,600 per minute on average. Data integrity failures can trigger regulatory fines, customer churn, and loss of executive confidence. User adoption failures mean your new platform sits idle while teams revert to spreadsheets and tribal knowledge.

Our approach is simple: identify risks early, assign owners, track mitigations, and validate relentlessly. This document is your playbook.

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The 18-Item Risk Register Framework

The risk register we use is built on three principles:

1. Specificity: Each risk has a clear definition, not vague language like “data quality issues.”
2. Ownership: Every risk is assigned to a named person or team, with clear escalation paths.
3. Measurability: Mitigations have success criteria that can be verified before go-live.

Here’s the framework:

Risk Categories

We organise risks into five buckets:

• Technical risks (data loss, corruption, performance degradation)
• Operational risks (process breakdowns, schedule slippage, resource constraints)
• Governance risks (access control drift, audit trail gaps, metadata inconsistency)
• Security and compliance risks (encryption failures, breach vectors, regulatory gaps)
• Change management risks (user resistance, adoption failure, knowledge loss)

Each category contains multiple specific risks, and each risk has a defined severity level (critical, high, medium, low), a probability estimate, and a mitigation strategy with success criteria.

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Technical Risks and Mitigations

Technical risks are the most visible—they’re the ones that cause outages and angry Slack messages at 2 AM. But they’re also the most preventable with rigorous process discipline.

Risk 1: Data Loss During Extract-Transform-Load (ETL)

Definition: Source data is not fully extracted, or records are dropped during transformation, resulting in incomplete datasets in the target system.

Why It Matters: Even 0.1% data loss in a financial dataset can trigger audit failures and customer disputes. In a customer analytics platform, missing records mean blind spots in reporting and flawed business decisions.

Mitigation Strategy:

• Run row-count reconciliation at every stage of the ETL pipeline (source → staging → transformed → target). Document baseline counts before migration begins.
• Implement a “golden record” validation: pick 50–100 representative records from the source, manually verify them in the target, and confirm all fields are present and correct.
• Use checksums or hash comparisons on key datasets to detect even small deletions.
• Maintain a complete audit log of all ETL operations, including record counts, transformation rules applied, and any filters or exclusions.
• Run a dry-run migration 2–3 weeks before go-live to catch these issues in a safe environment.

Success Criteria: 100% row-count match between source and target for all critical tables. All golden records verified. Audit log complete and reviewed.

Risk 2: Data Corruption or Type Mismatches

Definition: Data types are incorrectly mapped during migration (e.g., dates stored as strings, numeric fields truncated), or data is corrupted during transformation, resulting in invalid or unusable data.

Why It Matters: Corrupted data breaks downstream analytics, causes formula errors in BI tools, and destroys trust in reporting. A date field stored as text breaks every time-series analysis in your dashboard.

Mitigation Strategy:

• Create a comprehensive data type mapping document before any code is written. Map every source column to its target type and document any transformations (e.g., “YYYY-MM-DD string → DATE type”).
• Run automated data quality checks post-migration: validate that date fields parse correctly, numeric fields fall within expected ranges, and categorical fields contain only expected values.
• Use tools like Alation’s data governance platform to profile source data and identify anomalies before migration.
• Test edge cases: leap years, null values, negative numbers, special characters in text fields, and maximum field lengths.
• Implement a “data quality scorecard” for each table, tracking completeness, validity, and consistency. Target 99%+ on all metrics.

Success Criteria: All data type mappings documented and peer-reviewed. Data quality scorecard shows 99%+ validity across all critical tables. Edge case tests pass.

Risk 3: Performance Degradation Post-Migration

Definition: Queries that ran in seconds on the old system now take minutes on the new system, or the new platform struggles under normal query load.

Why It Matters: Slow analytics means users abandon the platform and revert to manual processes. Executive dashboards that take 5 minutes to load are worthless. Performance issues also signal deeper problems: missing indexes, poorly optimised queries, or undersized infrastructure.

Mitigation Strategy:

• Establish performance baselines on the legacy system before migration. Document query execution times, data refresh rates, and concurrent user capacity.
• Load-test the new environment with realistic query patterns and concurrent users before go-live. Use tools like Rivery’s migration framework to simulate real-world usage.
• Create indexes proactively on all frequently queried columns. Profile query execution plans and identify bottlenecks.
• Set up monitoring and alerting for query performance, data refresh times, and infrastructure utilisation (CPU, memory, I/O).
• Define acceptable performance thresholds: e.g., “95% of queries complete in <5 seconds.” Track these metrics post-go-live and escalate if thresholds are breached.
• Plan for capacity: if you’re migrating 10 years of historical data and your old system had 2 years, your new system needs to handle 5x the data volume.

Success Criteria: Performance baselines established. Load tests show acceptable query times under peak load. Indexes created and optimised. Monitoring and alerting configured.

Risk 4: Schema Drift or Metadata Inconsistency

Definition: Column names, data types, or table structures diverge between source and target, or metadata (descriptions, lineage, ownership) is lost or becomes inconsistent during migration.

Why It Matters: Schema drift causes downstream systems to break. If a column is renamed or moved, dependent dashboards and reports fail silently. Metadata loss means nobody knows what a field represents, who owns it, or where it came from—this is semantic drift, and it’s fatal for governance.

Mitigation Strategy:

• Maintain a “data dictionary” that maps every source column to its target equivalent. Include descriptions, ownership, and lineage information.
• Use automated tools like Fivetran’s data migration guide to validate that schemas match between source and target.
• Implement a metadata layer (e.g., dbt, Looker, or a custom metadata service) that documents all transformations and maintains a single source of truth for field definitions.
• Before go-live, run a schema comparison report: list every table, column, data type, and constraint in both systems and confirm they match.
• Document any intentional schema changes (e.g., “we renamed this column for clarity”) and communicate them to all downstream users.

Success Criteria: Data dictionary complete and peer-reviewed. Schema comparison report shows 100% match (or documents all intentional changes). Metadata layer configured and validated.

Risk 5: Incremental Load or Refresh Failures

Definition: After the initial migration, incremental loads (daily, hourly, or real-time syncs) fail to capture new or changed data, or refreshes become unreliable.

Why It Matters: A successful cut-over is just the beginning. If incremental loads fail, your new system becomes stale within days. Users lose confidence and revert to the old system. You end up maintaining two parallel systems indefinitely.

Mitigation Strategy:

• Design and test the incremental load process weeks before go-live. Don’t assume it will “just work” after the initial migration.
• Use change-data-capture (CDC) or timestamp-based logic to identify new and modified records. Test this logic against the source system to ensure it captures everything.
• Build in redundancy: if an incremental load fails, the system should alert immediately and not silently skip records.
• Run incremental loads in parallel with the legacy system for 2–4 weeks post-go-live. Compare results daily to catch discrepancies early.
• Document the incremental load schedule, SLAs (e.g., “new data available within 2 hours”), and escalation procedures.
• Monitor incremental load performance: track how long each load takes, how many records are processed, and how many errors occur.

Success Criteria: Incremental load process designed and tested. CDC or refresh logic validated. Parallel run completed with zero discrepancies. Monitoring and alerting configured.

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Operational and Governance Risks

Operational risks are less dramatic than technical failures, but they’re often more damaging because they’re harder to fix retroactively.

Risk 6: Schedule Slippage and Timeline Blowout

Definition: The migration takes longer than planned, causing delays to go-live, extended parallel-run periods, or missed business deadlines.

Why It Matters: Every week of delay costs money (extended resource allocation, delayed business benefits realisation, opportunity cost). It also erodes stakeholder confidence and can trigger project cancellation.

Mitigation Strategy:

• Build a detailed project plan with clear phases: discovery, design, build, test, UAT, go-live, and stabilisation. Include buffer time (typically 15–20% of total duration).
• Use a proven methodology like Agile or Waterfall (depending on project size) and track progress weekly against the plan.
• Identify critical path items early: dependencies that, if delayed, push out the entire timeline. Monitor these ruthlessly.
• Assign a full-time project manager to track risks, issues, and blockers. Hold weekly steering committee meetings to escalate risks early.
• Plan for resource constraints: if your best data engineer is also supporting production, you’re at risk. Hire contractors or backfill other work.
• Define go-live decision criteria upfront: e.g., “we only go live if UAT pass rate is >95% and all critical risks are mitigated.” Stick to these criteria, even if it means delaying.

Success Criteria: Detailed project plan with critical path identified. Weekly progress tracking shows on-time or early delivery. No blockers older than 3 days. Go-live decision criteria met before cutover.

Risk 7: Insufficient Resource Allocation

Definition: The team assigned to the migration is too small, lacks necessary skills, or is pulled away to support production fires, leaving the migration under-resourced and at risk of failure.

Why It Matters: Under-resourced migrations cut corners, skip testing, and produce technical debt. The team burns out. Quality suffers. You end up with a migration that technically “succeeded” but left behind a fragile, poorly documented system.

Mitigation Strategy:

• Right-size the team upfront. A typical enterprise BI migration requires: 1 project manager, 1 data architect, 2–3 ETL engineers, 1 QA engineer, 1 BI analyst, and 1 business analyst. Adjust for project scope.
• Define roles and responsibilities clearly. Who owns data quality? Who owns performance? Who owns user communication? Avoid ambiguity.
• Protect the team from production support during the migration. If the team is constantly pulled away to fight fires, the migration will fail. Hire contractors to backfill production support if needed.
• Plan for knowledge transfer. At least two team members should understand each component of the new system. Don’t let critical knowledge live in one person’s head.
• Budget for training: team members need time to learn new tools, platforms, and methodologies. This isn’t “nice to have”—it’s essential.

Success Criteria: Team fully staffed and protected from production distractions. Roles and responsibilities documented. Knowledge transfer plan in place. Training budget allocated and delivered.

Risk 8: Inadequate Testing Coverage

Definition: Testing is rushed or incomplete. Critical test scenarios are skipped. Edge cases are not tested. UAT is superficial or doesn’t reflect real-world usage patterns.

Why It Matters: Testing is the last line of defence before go-live. Inadequate testing means problems that could have been caught in a safe environment blow up in production. You end up rolling back, losing user trust, and delaying business benefits.

Mitigation Strategy:

• Build a comprehensive test plan that covers: functional testing (does the system do what it’s supposed to do?), regression testing (did we break anything?), performance testing (is it fast enough?), and user acceptance testing (do users accept it?).
• Use the “test pyramid” approach: many unit tests, fewer integration tests, even fewer end-to-end tests. This balances speed and coverage.
• Create test data that mirrors production: same data volumes, same data distributions, same edge cases. Don’t test with toy datasets.
• Document test cases in a traceability matrix: every requirement maps to at least one test case. Every test case maps back to a requirement. This ensures coverage.
• Run UAT with real users, not just IT staff. Real users will find edge cases and usability issues that IT won’t.
• Plan for regression testing: even after UAT passes, regression tests should run daily as the system evolves. Use automated regression test suites to catch regressions early.

Success Criteria: Test plan documented with coverage targets. Test data created and validated. Traceability matrix complete. UAT completed with >95% pass rate. Automated regression tests configured.

Risk 9: Metadata and Documentation Gaps

Definition: The new system lacks clear documentation of data lineage, transformation logic, table schemas, or operational procedures. Knowledge is scattered or tribal.

Why It Matters: Without documentation, the system becomes a black box. When problems arise, nobody knows how to troubleshoot. When team members leave, critical knowledge walks out the door. Onboarding new team members takes weeks instead of days.

Mitigation Strategy:

• Create a “runbook” for the new system: how to run daily jobs, how to handle failures, how to troubleshoot common issues, escalation contacts, etc. Make this accessible and keep it updated.
• Document all transformation logic: why each transformation exists, what business rule it implements, and how to modify it. Use code comments and external documentation (e.g., a wiki or Confluence page).
• Maintain a data lineage diagram: show how data flows from source systems through transformations to final reports. Use tools like Datafold’s data lineage features to automate this.
• Create a data dictionary: every table, column, and field has a clear description, owner, and usage notes.
• Record video walkthroughs: show how to use the new system, how to run common reports, and how to troubleshoot issues. Videos are often more helpful than written docs.
• Assign an “owner” to each component (data source, transformation, report, dashboard). They’re responsible for keeping documentation up to date.

Success Criteria: Runbook complete and reviewed. Transformation logic documented. Data lineage diagram created. Data dictionary complete. Video walkthroughs recorded. Component owners assigned.

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Security and Compliance Risks

Security and compliance risks are non-negotiable. A single breach or compliance violation can derail a migration and trigger regulatory action.

Risk 10: Encryption and Data Protection Failures

Definition: Data is not encrypted in transit or at rest. Encryption keys are not properly managed. Sensitive data (PII, financial records) is exposed or accessible to unauthorised users.

Why It Matters: Unencrypted data is a breach waiting to happen. If your migration involves moving PII or financial data, encryption is not optional—it’s a legal requirement. Non-compliance triggers fines, regulatory action, and reputational damage.

Mitigation Strategy:

• Encrypt all data in transit: use TLS 1.2+ for all network connections, including ETL pipelines, API calls, and user connections.
• Encrypt all data at rest: use AES-256 or equivalent for database encryption, storage encryption, and backup encryption.
• Implement key management: use a dedicated key management service (e.g., AWS KMS, Azure Key Vault) to generate, rotate, and audit encryption keys. Never hardcode keys in code or config files.
• Audit encryption: before go-live, verify that all data is encrypted by running network sniffers and checking storage encryption settings.
• Plan for key rotation: encryption keys should be rotated regularly (e.g., annually). Document the rotation process and test it.
• If you’re subject to SOC 2 or ISO 27001 compliance (common for startups and enterprises), ensure your migration approach aligns with these frameworks. Padiso’s Security Audit service can help you assess readiness via Vanta and close gaps before migration.

Success Criteria: All data encrypted in transit and at rest. Encryption audit completed and passed. Key management system configured. Key rotation plan documented and tested. Compliance assessment completed (if applicable).

Risk 11: Access Control and Permission Drift

Definition: User access permissions are not correctly migrated. Users have too much access (over-provisioned) or too little (under-provisioned). Access control lists (ACLs) are lost or inconsistent between old and new systems.

Why It Matters: Over-provisioned access is a security risk: users can access data they shouldn’t see. Under-provisioned access breaks workflows: users can’t do their jobs. Both scenarios create audit findings and potential compliance violations.

Mitigation Strategy:

• Create an access control matrix before migration: document who should have access to what (tables, reports, dashboards). Base this on job roles and business requirements, not on what users have in the old system.
• Implement role-based access control (RBAC): define roles (e.g., “Analyst,” “Manager,” “Executive”) and assign permissions to roles, not individuals. This scales better than individual ACLs.
• Use automated provisioning: when a user is hired or changes roles, their access is automatically updated. Don’t rely on manual processes.
• Audit access post-migration: run a report showing every user and their permissions. Compare this to the access control matrix and fix discrepancies.
• Implement the principle of least privilege: users should have the minimum access needed to do their jobs, nothing more.
• Set up access reviews: quarterly, managers should review and approve their team’s access. Remove access for users who have left or changed roles.

Success Criteria: Access control matrix documented and peer-reviewed. RBAC implemented. Automated provisioning configured. Post-migration access audit completed with 100% accuracy. Access review process defined.

Risk 12: Audit Trail and Compliance Gaps

Definition: The new system does not maintain complete audit trails of who accessed what data, when, and what changes were made. Compliance requirements (e.g., HIPAA, GDPR, SOX) are not met.

Why It Matters: Audit trails are essential for compliance, security investigations, and forensics. If you can’t prove who accessed sensitive data and when, you fail compliance audits. You also can’t investigate security incidents effectively.

Mitigation Strategy:

• Implement comprehensive audit logging: log all data access (queries, exports, downloads), all configuration changes, all user actions, and all system events. Include timestamp, user, action, and result.
• Store audit logs in a tamper-proof location (e.g., a separate, immutable storage system). Don’t store them in the same database as the data they’re auditing—if the database is compromised, so are the logs.
• Retain audit logs for the required period (e.g., 7 years for financial data). Document retention policies.
• Set up monitoring and alerting: if suspicious activity is detected (e.g., bulk data export, access outside business hours), alert security immediately.
• For regulated industries, ensure the audit trail meets specific requirements. For example, if you’re pursuing SOC 2 compliance via Vanta, audit trails are a core control.
• Test audit trail completeness: verify that all actions are logged, that logs are immutable, and that logs can be searched and exported for compliance reviews.

Success Criteria: Comprehensive audit logging implemented. Audit logs stored in tamper-proof location. Retention policies documented. Monitoring and alerting configured. Audit trail tested and validated. Compliance requirements met (if applicable).

Risk 13: Data Residency and Regulatory Compliance

Definition: Data is stored in the wrong geographic location or jurisdiction, violating data residency requirements (e.g., GDPR, Australian Privacy Act). Data is not backed up or disaster recovery is not in place.

Why It Matters: Data residency violations trigger regulatory fines and potential criminal liability. If your new system doesn’t have disaster recovery, a single hardware failure means total data loss.

Mitigation Strategy:

• Understand your data residency requirements: where must data be stored? Australia-based organisations often have specific requirements around data stored in Australia or within the Asia-Pacific region.
• Choose a cloud provider or data centre that meets your requirements. If you’re using AWS, ensure you’re in the correct region (e.g., ap-southeast-2 for Australia).
• Document data residency: create a map showing where each dataset is stored and why. Verify this before go-live.
• Implement disaster recovery: back up data daily to a geographically separate location. Test disaster recovery procedures quarterly (not just annually). Document RTO (recovery time objective) and RPO (recovery point objective).
• For critical systems, implement high availability: active-active replication across multiple data centres so that a single failure doesn’t cause downtime.
• If you’re subject to Australian Privacy Act or other data protection regulations, ensure your migration approach aligns. Document how you’re complying with each requirement.

Success Criteria: Data residency requirements documented. Data centre/region choice justified. Data residency map created and verified. Disaster recovery plan documented and tested. RTO and RPO defined and achievable. Compliance documentation complete.

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User Adoption and Change Management Risks

Technical success means nothing if users don’t adopt the new system. Change management risks are often underestimated and frequently cause migrations to fail.

Risk 14: User Resistance and Adoption Failure

Definition: Users resist the new system, continue using the old system, or find workarounds. Adoption rates are low. Users don’t trust the new data or reports.

Why It Matters: If users don’t adopt the new system, the migration has failed, even if it’s technically perfect. You end up maintaining two parallel systems indefinitely. Business benefits are never realised. The investment is wasted.

Mitigation Strategy:

• Start change management early: before any technical work begins, communicate why the migration is happening and what benefits it will deliver. Get executive sponsorship and visible support.
• Involve users in design: don’t design the new system in isolation. Bring users into design workshops, gather requirements, and show them prototypes. Users are more likely to adopt a system they helped design.
• Create user personas and journey maps: understand who your users are, what they do, and what they need from the new system. Design the system around their needs, not around technical constraints.
• Plan for training: don’t assume users will figure it out. Provide hands-on training before go-live. Use multiple formats: workshops, videos, documentation, one-on-one coaching. Train the trainers so that early adopters can help their peers.
• Identify champions: find power users and advocates who are excited about the new system. Empower them to help other users. Champions are more credible than IT staff.
• Plan for a phased rollout: don’t migrate everyone on day one. Start with a pilot group (e.g., one department), learn from their feedback, and iterate. Then roll out to the next group.
• Measure adoption: track metrics like ”% of users logging in daily,” ”% of reports run from the new system,” and “user satisfaction scores.” If adoption is lagging, escalate and adjust the change management plan.

Success Criteria: Change management plan documented with clear communication and training strategy. User personas and journey maps created. Champions identified and trained. Pilot rollout completed with >80% adoption. Post-go-live adoption metrics tracked and on target.

Risk 15: Knowledge Loss and Tribal Knowledge

Definition: Critical knowledge about the old system, data, or business processes is not transferred to the new team. Key people leave during or after the migration. Tribal knowledge is lost.

Why It Matters: Without knowledge transfer, the new team can’t operate or maintain the system effectively. When problems arise, nobody knows how to fix them. Onboarding new team members takes much longer.

Mitigation Strategy:

• Conduct knowledge transfer sessions: have subject matter experts from the old system document and teach the new team about data, transformations, and business logic. Record these sessions.
• Create a “lessons learned” document: after the migration, capture what went well, what didn’t, and what to do differently next time. Share this with the team and with future projects.
• Implement pair programming or shadowing: have experienced team members work alongside new team members. This is more effective than classroom training.
• Document everything: processes, procedures, troubleshooting guides, and decision rationale. Make documentation a habit, not an afterthought.
• Create a knowledge repository: a central place (wiki, Confluence, GitHub) where all documentation lives. Make it searchable and keep it updated.
• Plan for retention: if key people are likely to leave after the migration, plan for this. Hire backfill staff before they leave. Offer retention bonuses if appropriate.

Success Criteria: Knowledge transfer sessions completed and recorded. Lessons learned document created. Pair programming or shadowing completed. Documentation repository populated and searchable. Retention plan in place (if applicable).

Risk 16: Inadequate User Communication and Expectation Management

Definition: Users are not kept informed about the migration timeline, changes, or impact. Expectations are not set correctly. Users are surprised by changes or downtime.

Why It Matters: Poor communication breeds mistrust and resistance. Users feel like things are being done to them, not with them. They’re more likely to reject the new system if they don’t understand why it’s changing or how it will affect them.

Mitigation Strategy:

• Create a communication plan: document who needs to be informed, what messages they need to hear, and when. Different audiences (executives, managers, users) need different messages.
• Communicate early and often: don’t wait until go-live week to tell users about the migration. Start communicating months in advance. Share progress updates regularly (e.g., monthly).
• Use multiple channels: email, town halls, team meetings, posters, Slack, and newsletters. Different people consume information differently.
• Be transparent about risks and changes: if there will be downtime, say so. If the new system looks different, show screenshots. If there are trade-offs (e.g., “new system is faster but has fewer customisation options”), explain them.
• Set expectations about the transition period: help users understand that there may be a learning curve, that the system may not be perfect on day one, and that you’re committed to fixing issues quickly.
• Establish a feedback channel: users should be able to report problems, ask questions, and provide suggestions. Respond to feedback quickly and visibly.

Success Criteria: Communication plan documented and executed. Regular progress updates shared. Town halls or team meetings held. Feedback channel established and monitored. User satisfaction surveys show positive sentiment.

Risk 17: Business Process Changes and Workflow Disruption

Definition: The new system requires changes to business processes or workflows. Users’ jobs change in ways they didn’t expect. Productivity drops during the transition. Critical business processes are disrupted.

Why It Matters: Even if the new system is technically superior, if it disrupts critical workflows, users will resist. A process that took 2 hours in the old system should not take 4 hours in the new system. If it does, users will find workarounds or revert to the old system.

Mitigation Strategy:

• Map current workflows: before designing the new system, understand how users currently work. What steps do they take? What data do they need? What outputs do they produce?
• Design new workflows with users: don’t impose new workflows from above. Work with users to design workflows that are as close as possible to their current processes, but optimised for the new system.
• Identify process improvements: the new system may enable new ways of working. Highlight these and help users adopt them. But don’t force change unless there’s a clear benefit.
• Plan for a transition period: acknowledge that productivity may dip initially as users learn the new system. Plan for this in your capacity planning (e.g., hire temps, defer non-critical work).
• Measure workflow efficiency: track metrics like “time to complete a report,” “number of manual steps,” and “error rate.” If the new system is slower or more error-prone, investigate and fix.
• Have a rollback plan: if a critical workflow is broken and can’t be fixed quickly, you may need to rollback or run the old system in parallel temporarily. Plan for this.

Success Criteria: Current workflows mapped and documented. New workflows designed with user input. Process improvements identified and communicated. Workflow efficiency metrics defined and tracked. Transition period planned. Rollback plan documented.

Risk 18: Inadequate Post-Go-Live Support and Stabilisation

Definition: After go-live, support is insufficient. Issues are not resolved quickly. Users are frustrated. The system is unstable or unreliable during the critical first weeks.

Why It Matters: The first few weeks after go-live are critical. If issues are not resolved quickly, users lose confidence in the system and revert to the old system. The migration is deemed a failure, even if the underlying system is sound.

Mitigation Strategy:

• Plan for a “war room” period: for the first 2–4 weeks post-go-live, have the full team on standby to resolve issues immediately. Prioritise user-facing issues.
• Set up a dedicated support channel: users should be able to report issues easily (e.g., a Slack channel, a ticketing system, a hotline). Issues should be acknowledged within 1 hour.
• Define escalation paths: critical issues (data loss, security breaches, system outages) should be escalated immediately to senior leadership.
• Monitor the system continuously: have someone monitoring system health, performance, and error rates 24/7 during the first week. Alert the team immediately if anything looks wrong.
• Plan for quick fixes: some issues can be fixed without a full deployment (e.g., data corrections, configuration changes). Have a process for deploying quick fixes rapidly.
• Communicate status: keep users informed about known issues, workarounds, and ETAs for fixes. Transparency builds trust.
• Run a post-go-live retrospective: 2–4 weeks after go-live, gather the team and users to discuss what went well, what didn’t, and what to improve. Document and share findings.

Success Criteria: War room staffed and ready. Support channel established. Escalation paths defined. Monitoring configured. Quick-fix process defined. Status communication plan in place. Post-go-live retrospective scheduled.

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Implementing Your Own Risk Register

Now that you understand the 18 risks, how do you implement a risk register for your own migration?

Step 1: Customise the Register for Your Context

The 18 risks we’ve outlined are based on our experience across 50+ migrations, but your migration is unique. Take this framework and adapt it:

• Remove risks that don’t apply to your situation (e.g., if you’re not subject to HIPAA, you may not need the same level of audit trail rigor).
• Add risks specific to your context (e.g., if you’re migrating from a legacy mainframe, you might have risks around mainframe expertise or legacy system quirks).
• Adjust severity levels based on your business impact (e.g., if your BI system is mission-critical, all risks are higher severity).

When Padiso engages with clients on platform engineering and custom software development, we always start by customising the risk register to their specific context. There’s no one-size-fits-all approach.

Step 2: Assign Owners and Define Escalation

For each risk, assign a named owner. This person is responsible for:

• Monitoring the risk throughout the project.
• Implementing mitigations.
• Escalating if the risk status changes.
• Updating the risk register weekly.

Define escalation paths: if a risk becomes “critical” or if a mitigation is not on track, who needs to be notified? Typically, this is the project manager, the project sponsor, and relevant stakeholders.

Step 3: Track Mitigations with Success Criteria

For each risk, define the mitigation strategy and success criteria. Success criteria should be measurable and verifiable:

• ✅ Good: “100% row-count match between source and target for all critical tables.”
• ❌ Bad: “Data quality is good.”

Track the status of each mitigation: not started, in progress, complete, or at risk. Update status weekly.

Step 4: Review and Escalate Weekly

Hold a weekly risk review meeting with the project team and stakeholders. Go through each risk:

• Has the status changed since last week?
• Are mitigations on track?
• Are there any new risks?
• Do any risks need escalation?

Document the meeting and share the updated risk register with stakeholders.

Step 5: Validate Mitigations Before Go-Live

Two weeks before go-live, validate that all critical mitigations are complete:

• Has the data quality audit been run? Are results acceptable?
• Has performance testing been completed? Do results meet thresholds?
• Has UAT been completed? What’s the pass rate?
• Have access controls been audited? Are there any over-provisioned or under-provisioned users?

If critical mitigations are not complete, delay go-live. Going live with incomplete mitigations is a recipe for disaster.

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Monitoring and Escalation Protocols

A risk register is only useful if it’s actively monitored and escalation is taken seriously.

Real-Time Monitoring

During the migration project, risks should be monitored continuously, not just weekly:

• Daily standups: the team should discuss risks and blockers daily. If a risk status changes, escalate immediately.
• Automated alerts: set up monitoring and alerting for technical risks (e.g., ETL failures, performance degradation). Alert the team immediately if thresholds are breached.
• Metrics tracking: track key metrics (test pass rates, data quality scores, adoption rates) daily. If metrics are trending in the wrong direction, investigate.

Escalation Triggers

Define clear escalation triggers. For example:

• Critical risk: If a critical risk is not on track for mitigation, escalate to the project sponsor immediately.
• New critical risk: If a new critical risk is identified, escalate immediately.
• Mitigation failure: If a mitigation attempt fails (e.g., UAT fails), escalate and reassess the risk.
• Go-live readiness: If critical mitigations are not complete 2 weeks before go-live, escalate and consider delaying go-live.

Escalation Paths

Define who escalates to whom:

• Project manager escalates to project sponsor (typically a C-level executive).
• Project sponsor escalates to steering committee (typically executives from IT, business, and finance).
• Steering committee makes go/no-go decisions.

Make sure escalation paths are clear and that escalation is fast. A risk that should have been escalated but wasn’t is worse than a risk that was escalated unnecessarily.

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Post-Migration Validation

Go-live is not the finish line. The critical period is the 4–8 weeks after go-live when the system is stabilising and users are ramping up.

Validation Checklist

Before declaring the migration successful, validate:

1. Data integrity: Run a full data quality audit. Compare source and target row counts, data types, and sample records. Verify that all data has been migrated correctly.

2. Performance: Monitor query performance, data refresh times, and system resource utilisation. Verify that performance meets SLAs.

3. User adoption: Track login rates, report usage, and user satisfaction. If adoption is lagging, investigate and adjust.

4. System stability: Monitor error rates, system availability, and incident frequency. The system should be stable by week 4 post-go-live.

5. Compliance: Run compliance audits to verify that SOC 2, ISO 27001, or other requirements are being met. If you’re using Vanta, run an audit.

6. Business benefits: Measure the benefits the migration was supposed to deliver (e.g., faster reporting, cost savings, improved data quality). If benefits are not being realised, investigate.

Decommissioning the Old System

Once you’re confident the new system is stable and users have adopted it, decommission the old system:

1. Data archival: Archive historical data from the old system for compliance and reference purposes.

2. System shutdown: Turn off the old system once all users have migrated and data has been archived.

3. Knowledge capture: Document any unique features or data from the old system that won’t exist in the new system. This is important for compliance and audits.

4. Team transition: transition the team from migration mode to operations mode. Define ongoing support and maintenance responsibilities.

Lessons Learned

After the migration is complete, conduct a formal lessons learned session:

• What went well? What should we do again?
• What didn’t go well? What should we do differently?
• What surprised us? What did we learn?
• What should we document for future migrations?

Capture these insights and share them with the organisation. They’re invaluable for future projects.

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Next Steps: Building Your Migration Strategy

If you’re planning a BI migration or data platform modernisation, use this risk register as your starting point. Here’s what to do next:

1. Customise the Register

Take the 18 risks and adapt them to your situation. Add risks specific to your context. Adjust severity levels based on your business impact.

2. Assemble Your Team

You need the right people to execute a successful migration. This includes data engineers, BI analysts, project managers, security specialists, and change management experts. If you don’t have these skills in-house, bring in partners. At Padiso, we provide fractional CTO leadership and co-build support for startups and enterprises undergoing platform modernisation. We can also help with AI & Agents Automation and AI Strategy & Readiness if your migration involves AI-driven analytics or automation.

3. Build a Detailed Project Plan

Use the risk register to inform your project plan. Allocate time and resources to mitigate each risk. Build in buffer time for unexpected issues.

4. Establish Governance

Set up a steering committee, define escalation paths, and establish weekly risk reviews. Make sure governance is lightweight but effective.

5. Invest in Change Management

Don’t skimp on change management. Communicate early, involve users, provide training, and measure adoption. Change management is often the difference between success and failure.

6. Plan for Ongoing Support

After go-live, you’ll need ongoing support to stabilise the system, resolve issues, and optimise performance. Plan for this upfront. Don’t assume the team will just “figure it out.”

7. Consider External Expertise

If you’re migrating a complex system or don’t have in-house expertise, consider bringing in external partners. A good partner will bring experience from other migrations, help you avoid common pitfalls, and accelerate time-to-value. When evaluating partners, ask about their experience with your specific technology stack, their approach to risk management, and their track record on similar migrations. At Padiso, we’ve successfully shipped data migrations for seed-stage startups through to enterprise organisations. We bring a proven methodology, deep technical expertise, and a focus on outcomes.

8. Measure Success

Define success metrics upfront: time-to-go-live, data quality, performance, adoption, and business benefits realised. Track these metrics throughout the project and post-go-live. Use them to evaluate the success of the migration and to improve future projects.

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Conclusion

Data migrations are complex, high-stakes projects. The difference between success and failure often comes down to discipline: having a clear risk register, assigning owners, tracking mitigations, and validating relentlessly.

The 18-item risk register we’ve outlined in this guide has been battle-tested across 50+ migrations. It covers technical risks, operational risks, governance risks, security and compliance risks, and change management risks. Each risk has a defined mitigation strategy and success criteria.

Use this framework as your starting point. Customise it for your context. Assign owners. Track mitigations. Review weekly. Validate before go-live. And most importantly, don’t go live until critical mitigations are complete.

If you’re planning a BI migration or data platform modernisation and want expert guidance, Padiso can help. We bring experience from 50+ migrations, a proven risk management methodology, and a focus on outcomes. Whether you need fractional CTO leadership, help with platform engineering, or support with security audit and compliance, we’re here to help you ship successfully.

Reach out to discuss your migration strategy. We’re based in Sydney and work with ambitious teams across Australia and beyond.