Six key steps for a successful cloud data migration

By Ryan Levman, Director Data Engineering, Bell Canada    

Legacy systems, once reliable, are now struggling to keep pace with today's data-driven and AI-powered world. They are often slow, inflexible, and unable to handle the growing volume and velocity of data. Furthermore, they tend to be burdened with technical debt. I once participated in a project where 33% of the processes within a data system were identified as adding no value and therefore decommissioned. The result: streamlined operations, reduced costs, and improved performance. What a great accomplishment!  

Many organizations also face the daunting task of migrating away from these systems due to end-of-life timelines for critical hardware and software.  

The cloud offers a clear path forward. It provides the agility, scalability, and computing power needed to support modern data workloads and unlock the full potential of AI.  

Cloud migration is more than just moving data 

My experience supporting several data migrations has shown me that moving data from on-premises legacy systems to the cloud is only the beginning. Think of it as the foundation for your broader data modernization and AI strategy. When executed effectively, migrating your workloads to the cloud can significantly reduce costs, boost employee productivity, improve reliability, accelerate processing times, and enhance performance. In fact, at Bell, we’ve experienced some impressive results. Examples include:  

• • • Up to 20% reduction in total cost of ownership of the data platforms  
    • 25% reduction in pipeline failures  
    • 95% reduction in processing time for some workloads   
    • 30% increase in AI model performance with larger models and more data 

Simplify your cloud migration journey 

While cloud migration projects are promising, we often make the process unnecessarily complex, leading to unexpected hurdles. Unrealistic deadlines, team burnout, and last-minute surprises due to inadequate planning can disrupt even the best plans.  

To help your teams navigate these challenges, here are six critical considerations I recommend for a seamless migration to the cloud:  

1. Define the "Why"  

Before diving into the details, it's essential to understand the driving forces behind the migration.  

Ask yourself:  

• • • What are your desired outcomes? What problems are you trying to solve?   
    • Are there specific deadlines, security, compliance or performance issues driving the change?  
    • Are external factors, such as the adoption of AI, influencing your decisions?  
    • Is there a corporate cloud strategy in place to align with?  

Failing to define the "why" can lead to misaligned planning and solutions that don't deliver the desired business outcomes. The project may become a reactive exercise rather than a strategic initiative.  

2. Conduct a thorough discovery phase  

Before migration, a thorough discovery phase is crucial for understanding your data. The discovery phase helps pinpoint potential gaps and uncover key use cases, enabling you to address them proactively.   

During this phase:  

• • • Assess the size, scope and complexity of the data you want to migrate. 
    • Identify all source code repositories to help you convert and map your code to the new environment efficiently.   
    • Conduct metadata discovery to understand data usage patterns, lineage, and dependencies.  
    • You can leverage AI in this phase to significantly reduce potential errors during migration. 

3.  Establish a strategic plan  

Once you understand your data, develop a strategic plan for your migration. This includes:  

• • • Choosing your migration approach: Will you lift and shift, or rebuild, or use a hybrid model?  Consider the pros and cons of each approach and select the one that best aligns with your specific needs and goals. 
    • Assembling a skilled team: You'll need a team with expertise in networking, security, privacy, data engineering, AI and more to collaborate on all upstream and downstream dependencies. 
    • Making critical tooling decisions: What data platform, security measures, hyper-scaler, and AI solutions will best support your migration objectives?  
    • Data security and compliance: How will you ensure that data is secure and protected, complies with relevant regulations, and meets your organization's privacy requirements? 

Failing to make these decisions early on can lead to delays, rework, cost overruns, so proactive planning is key.  

4. Build an interim and target ecosystem  

Your target architecture should be designed with future scalability and innovation in mind. Consider: 

• • • How data will move to the cloud: What tools and processes will you use for a secure migration?  
    • Solutions running in parallel: How will you ensure a smooth transition while minimizing disruption to your business when implementing solutions in parallel?  
    • Cutover planning: How will you switch over to the new platform?  
    • Defining the target architecture: What are the functional requirements for your data and AI stack, and how will you ensure alignment with your overall strategic objectives? 

5. Balance templates and best practices with real-world use-cases  

It’s easy to get bogged down in building perfect frameworks, but what truly matters is migrating use- cases that deliver business value. Templates should accelerate, not hinder your focus on high-priority use cases.  

Ask yourself:  

• • • What existing functionality needs to be templated for developer efficiency?  
    • Can templating and use-case migration happen in parallel?  
    • What existing AI and data management practices should be retained and enhanced after the migration?  

Your goal should be to balance frameworks with real-world use cases to ensure the migration drives immediate value.   

6. Build a high-performing execution team 

A successful data platform migration requires a team with the right skills and mindset. Build a team that:  

• • • Delivers with velocity: They should be able to complete tasks efficiently and deliver high-quality results.   
    • Manages projects end-to-end: This means having a team that is proficient in managing the entire project lifecycle – from design to implementation. This will avoid unnecessary handoffs and prioritization battles.  

An under-equipped team or organizational misalignment can hinder the team's ability to meet deadlines and deliver successful results. Ensure the team has the necessary skills, resources, and support.  

Advance your journey to the cloud with Bell 

Ready to take your cloud migration to the next level? Engage with Bell to build a data strategy to maximize the potential of your data in the cloud. Get in touch with one of our experts to learn more.  

About the author 

Ryan Levman has been with Bell for 11 years, starting as a data analyst after graduating from the University of Waterloo’s Management Engineering program. He now leads the consumer data engineering and AI team and is passionate about intersections of people, data, technology and business value.