Jira Cloud Migration: Key Steps for a Smooth Transition

[Ankita Mehta-OpsHub_ Inc]

As Jira Data Center (DC) nears its retirement, many organizations with millions of issues and thousands of projects are transitioning to Jira Cloud. However, migrating to Jira Cloud is not just about moving data; it's about adopting a new tool that affects business processes, team collaboration, and workflows. This transition also presents an opportunity to optimize operations, improve project management, and foster collaboration across teams.

Migrating to Jira Cloud involves careful planning and a structured approach. It’s essential to ensure the migration aligns with organizational goals and that the transition doesn’t disrupt productivity, compliance, or data integrity.

1. Defining Organizational Objectives & Success Metrics

At the outset of any Jira Data Center (DC) to Cloud migration, one of the first steps is defining what success looks like. The goal isn’t just about transferring projects; it’s about realigning how teams collaborate and ensuring Jira Cloud supports broader business strategies.

Key areas to focus on:

• Target workflows: It’s important to document and assess existing workflows that need to be replicated or improved in Jira Cloud.
• Ownership models: Redefining admin roles and responsibilities in Jira Cloud, considering centralized user management and permissions.
• Standardized templates: Creating standardized project templates or global settings helps ensure consistency across the Cloud instance.

Defining success metrics should go beyond uptime or data transfer. Tracking things like cycle times, issue resolution rates, and collaboration velocity provides a clearer picture of the migration’s impact.

2. Configuring Jira Cloud to Match Business Needs

Jira Cloud differs from Data Center in several areas, including user directories, automation, and app architecture. Therefore, configuring Jira Cloud to match business needs is critical to avoid disruptions.

Key configuration actions include:

• System configurations: Mapping Data Center roles, permissions, and notification schemes to Cloud equivalents.
• Reconfiguring integrations: Jira DC integrations with external tools often require reconfiguration due to differences in authentication or API usage.
• Custom fields and issue types: Jira Cloud limits custom fields, so reviewing, consolidating, and removing unnecessary fields is necessary.
• App parity: Many Data Center apps don’t have Cloud equivalents, so finding and testing alternatives before migration is essential.

Proper configuration minimizes operational friction and sets the foundation for scalability and maintainability.

3. Assessing Migration Constraints & Selecting the Optimal Strategy

Every migration presents its own set of challenges. Moving from Data Center, which allows deep customization, to Cloud, which enforces stricter governance, means it’s important to identify migration constraints early and find ways to mitigate them.

Considerations include:

• App dependencies: Review installed apps and custom plugins to assess compatibility with Jira Cloud.
• Custom scripts and automations: Jira Cloud uses its own automation tool, which can be different from what’s used in Data Center, requiring adjustments.
• Data volume and storage limits: Data Cloud enforces storage limits and different indexing methods, so large attachments or histories may need to be pruned.
• User management: Transitioning to Atlassian access for centralized identity management can require adjustments if you’re migrating from LDAP or local directories.

When selecting the migration strategy, various approaches can be applied depending on the size and complexity of the environment:

• Bulk migration is often suitable for smaller, standardized environments.
• Phased migration works well for larger, more complex setups, reducing risk by moving teams in stages.
• Hybrid migration can combine the two, allowing critical projects to remain live while other projects are migrated.

4. Conducting a Pilot Migration

Before a full-scale migration, running a pilot with a small subset of projects and teams helps identify potential issues and refine the migration strategy. It’s crucial for understanding how the Cloud environment handles specific use cases.

During the pilot, it's important to:

• Test data fidelity to ensure that no issues, comments, attachments, or links are lost or corrupted.
• Validate permissions and workflows to ensure that user roles and workflows function correctly in the Cloud environment.
• Iterate based on feedback to refine the strategy before moving forward with the full migration.

This step helps reduce the risk of encountering issues in a larger, more critical environment.

5. Scaling the Migration with Change Management

Once the pilot is successful, the next step is scaling the migration across the organization. For large enterprises with numerous projects and complex configurations, using a wave-based approach is often necessary to minimize downtime and simplify validation.

Key factors during scaling include:

• Wave execution: Grouping projects by department, geography, or business function helps organize the migration into manageable phases.
• Communication plan: Keeping stakeholders informed about timelines and new access methods ensures everyone is on the same page.
• Rollback strategies: Preparing for unexpected performance issues or missing data by defining contingency plans.
• Change management: Training admins and users on Cloud-specific functionality and features ensures everyone is prepared for the transition.

6. Post-Migration Reporting & Analytics

After the migration is complete, attention turns to optimization. With Jira Cloud, there are powerful tools for providing visibility into performance and user adoption. Ensuring transparency and measuring progress is crucial.

Key post-migration actions include:

• Verifying data fidelity through Jira Query Language (JQL) and Cloud reports to ensure everything has been accurately migrated.
• Using Jira Cloud dashboards and Atlassian Analytics to track key metrics like team velocity, project progress, and adoption rates.
• Setting up executive dashboards to give leadership insights into overall system health and user engagement.

Establishing robust analytics early helps measure the ROI of the migration and identify areas for further optimization.

7. Continuous Improvement & Evaluation

Migration is not the end of the journey. Jira Cloud evolves quickly, and it’s important to focus on continuous improvement. Regularly evaluating and refining workflows, permissions, and apps is a must to ensure Jira Cloud remains aligned with business needs.

Best practices for ongoing success:

• Retrospectives to evaluate what went well and identify areas for improvement.
• Regularly review and refine workflows, fields, and permissions as business needs evolve.
• Monitoring Marketplace apps for updates and new Cloud-compatible versions.

By continuously improving and adapting, Jira Cloud becomes a strategic enabler of collaboration, agility, and productivity across the organization.

Conclusion

Migrating from Jira Data Center to Jira Cloud is more than just a technical upgrade, it’s an opportunity to transform how teams work, collaborate, and deliver value. The move to Cloud offers scalability and resilience, but it requires structured planning and strong change management.

By following a phased, strategic approach and leveraging enterprise-grade platforms like OpsHub Migration Manager (OMM), organizations can ensure a zero downtime, non-disruptive, high-fidelity migration to Jira Cloud.

Let’s Discuss!

What migration strategies have worked for your organization? How have you managed the transition to Jira Cloud? Looking forward to reading your experiences and tips in the comments.:)

[Darin - Opus Guard]

Great post @Ankita Mehta-OpsHub_ IncStrong YES AND!

Migrating from DC to Cloud is more than a simple switch, I can't emphasize enough the points on not bringing over all the baggage. Unlimited storage is simply not a thing in cloud and also incredibly risky if you think of the added threat vectors of Cloud.

Really important in the journey to migrating from DC to Cloud is to establish an information governance system at the start. Making sure you have content classification and lifecycles established to keep your Cloud instances clean.

I'm here to help everyone making the migration from Data Center to Cloud when it comes to clean content management as a complement to OpsHub's Migration Manager with Content Retention Manager for Confluence and Jira.

I'm always up for a coffee chat to talk cleaning up these systems and risk management if anyone needs help.

[Darin - Opus Guard]

This is such a clear and thorough breakdown on the importance of house cleaning during the Atlassian Data Center EOL. I really like how it frames migration as both a technical and cultural shift. 

One area I have seen make a big difference after migration is establishing clear content governance and retention policies early in the Cloud environment. Once everything’s in Jira Cloud, it’s surprisingly easy for legacy data and inactive projects to start piling up again, which can slow down search, clutter space management, and even affect the quality of AI tools like Rovo or Glean.

I've been helping a few Atlassian customers approach this by setting up lightweight content retention policies before migration, keeping only what’s still relevant so the new Cloud instance stays cleaner and faster long-term.

If that’s a topic you're interested in exploring as a follow on to this, I'm always up for a coffee chat, in the mean time, this short overview might be useful: Move to Atlassian Cloud with AI ready, clean, compliant content