Cloud Data Migration: A 10-Step Ultimate Guide

Last updated on: June 29th, 2023, Published on: June 29th, 2023
Cloud data migration

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Cloud data migration is crucial for organizations because it allows businesses to scale their operations easily by offering unlimited storage and computing resources. This means they can grow without worrying about hardware limitations.

Cloud data migration future-proofs organizations by enabling them to adopt emerging technologies and stay ahead in the market.

In this blog, we will learn the steps of cloud data migration, key challenges, and the major data governance policies essential for it, along with a case study.

So, let us dive in!

Table of contents

  1. What are the steps of cloud data migration?
  2. Steps for cloud data migration: Explained in detail
  3. Challenges of cloud data migration
  4. Data governance policies essential for cloud data migration
  5. Cloud data migration: A case study perspective
  6. Rounding it all up
  7. Cloud data migration: Related reads

What are the steps of cloud data migration?

Migrating data to the cloud is a major decision for any company, especially those dealing with large volumes of sensitive data such as a financial technology company.

There are 10 steps in cloud migration, which are as follows:

Step 1: Define your strategy

Step 2: Identify and assess your data

Step 3: Choose a cloud provider

Step 4: Define your architecture

Step 5: Choose a migration approach

Step 6: Security and compliance review

Step 7: Perform a migration test

Step 8: Migrate data

Step 9: Validate and optimize

Step 10: Plan for continuous improvement

Now, let us look into each of the above steps in brief:

Step 1: Define your strategy


Understand your key business drivers for migration.

This could be :

  • Cost-efficiency
  • Scalability
  • Improving data security
  • Disaster recovery
  • Driving faster insights

Step 2: Identify and assess your data


Assess the data that you need to migrate. This can be challenging when dealing with legacy systems. Tools for data cataloging can help you discover and classify data.

Step 3: Choose a cloud provider


Evaluate and select a cloud provider that best suits your needs. Consider factors such as cost, services offered, security measures, and compliance standards. The big three are:

Step 4: Define your architecture


Define the architecture of your cloud setup. This could be public, private, or hybrid depending on your needs. Also, define how the components will interact with each other and with on-premises systems.

Step 5: Choose a migration approach


The approach can vary based on the amount and type of data, downtime tolerances, and migration window. The common approaches are:

  • Re-hosting (Lift and Shift): This involves moving applications and data to the cloud with little to no modification. This approach is fast and less resource-intensive but doesn’t take full advantage of cloud-native features.
  • Re-platforming (Lift, Tinker, and Shift): This involves making some cloud-optimized modifications during the migration process to benefit from cloud capabilities without a complete redesign.
  • Repurchasing: This involves moving to a different product, typically a cloud-based service.
  • Refactoring/Re-architecting: This involves redesigning the application to fully exploit cloud-native features. This is the most resource-intensive approach but can yield more long-term benefits.

Step 6: Security and compliance review


Review the security features provided by the cloud service provider. For a fintech company, compliance with regulations like PCI DSS, GDPR, and regional data laws is essential.

Step 7: Perform a migration test


Before fully migrating, perform a migration test. This allows you to assess the effectiveness of the migration process, estimate the downtime, and troubleshoot any potential problems.

Step 8: Migrate data


After successful testing, plan the migration. This might be a single event or staggered over a period of time, based on the business needs and the downtime acceptable.

Step 9: Validate and optimize


After the migration, perform validation tests to ensure everything is working as expected. Also, look for optimization opportunities like cost-saving, performance improvement, etc.

Step 10: Plan for continuous improvement


Data migration is not a one-time process. The business and data landscape will continue to evolve, so there should be a plan in place for continuous improvement and maintenance.

Additional considerations


  • Team structure: Decentralizing your data team could help reduce bottlenecks. You could consider adopting a DataOps approach, where cross-functional teams collaborate to deliver data, insights, and analytics at speed.
  • Data governance: Establish robust data governance procedures to ensure data quality and security. This is especially important for a self-serve analytics environment.
  • Training: Ensure your team is trained in new technologies and methodologies. This is important for the success of the migration and the ongoing operation of the new infrastructure.

Remember that every migration is unique and these steps should be adapted based on your business context and technical environment.

Steps for cloud data migration: Explained in detail

In this section, let’s break down each step further, looking at potential challenges, the resources required, and success metrics.

Step 1: Define your strategy


  • Challenges: Articulating clear goals for migration, aligning various stakeholders, and determining budget and timeline constraints.
  • Resources: Strategic alignment workshops with executives, business, IT, and data teams. SWOT analysis to identify strengths, weaknesses, opportunities, and threats of the migration. Cost-benefit analysis.
  • Success metrics: Finalized migration strategy with agreed-upon objectives, budget, and timeline.

Step 2: Identify and assess your data


  • Challenges: Difficulties in understanding data scattered across various legacy systems, identifying sensitive or regulated data, assessing data quality, and defining data migration priorities.
  • Resources: Data cataloging tools, data quality assessment tools, cross-functional team to analyze the data, existing system documentation, and data dictionary.
  • Success metrics: Thorough understanding of data landscape with data catalog, identified sensitive data, and prioritized data migration list.

Step 3: Choose a cloud provider


  • Challenges: Difficulties in evaluating cloud providers based on pricing, performance, security, services offered, and compliance with relevant regulations.
  • Resources: RFP (Request for Proposal) documentation, responses from potential cloud vendors, vendor evaluation matrix.
  • Success metrics: Selection of cloud provider that matches business, security, and compliance needs within budget.

Step 4: Define your architecture


  • Challenges: Balancing requirements for efficiency, scalability, resilience, security, and cost-effectiveness in the architecture. Integrating with existing systems.
  • Resources: Cloud architects, architectural design tools, best practice guides, and references from the chosen cloud provider.
  • Success metrics: Detailed cloud architecture plan that satisfies requirements for efficiency, scalability, resilience, security, and cost-effectiveness.

Step 5: Choose a migration approach


  • Challenges: Choosing between speed and resource efficiency (rehosting), moderate change for more benefits (replatforming), or full redesign for maximum benefit but high cost (refactoring).
  • Resources: Cost-benefit analysis of different approaches, insights from similar migration projects, pilot migration projects.
  • Success metrics: Chosen migration approach that aligns with strategic objectives, resource availability, and risk tolerance.

Step 6: Security and compliance review


  • Challenges: Ensuring the chosen cloud provider, architecture, and processes comply with all relevant regulations and provide adequate data protection.
  • Resources: Information on relevant regulations and standards, cybersecurity experts, and third-party auditors.
  • Success Metrics: Compliance audit report, risk assessment report.

Step 7: Perform a migration Test


  • Challenges: Ensuring the test accurately replicates the final migration and interpreting results to identify potential issues.
  • Resources: Test data sets, data migration tools, test environments, data validation tools.
  • Success metrics: Successful test migration, documented insights about potential issues and their solutions, accurate estimate of migration time.

Step 8: Migrate data


  • Challenges: Managing downtime, ensuring data integrity during transfer, and dealing with unexpected issues during migration.
  • Resources: Data migration tools, migration team including project managers and engineers, coordinated communication plan.
  • Success Metrics: Successful migration within agreed downtime, no loss of data integrity, a post-migration report documenting any issues and their resolutions.

Step 9: Validate and optimize


  • Challenges: Verifying data integrity post-migration, and tuning new systems for optimal performance.
  • Resources: Data validation tools, performance monitoring, and management tools, technical team for system tuning.
  • Success metrics: Achieving or exceeding initial performance benchmarks, favorable feedback from users, stable system operation with minimal downtime or errors.

Step 10: Plan for continuous improvement


  • Challenges: Establishing processes for ongoing optimization and adaptation to evolving business and data needs. Staying up-to-date with cloud technology advancements.
  • Resources: Regular system performance review, user feedback mechanisms, continued learning and training for the team, innovation budget.
  • Success metrics: Implementation of the improvement plan, evidence of iterative improvements in system performance, team adaptability, and user satisfaction over time.

The process of cloud data migration is complex and requires careful planning, execution, and follow-up. Ensuring you have the right resources in place, anticipate challenges, and set clear success metrics for each step will contribute to a smoother transition.

Remember that every organization’s migration journey will be unique, so use these guidelines as a starting point, but adapt as necessary for your specific circumstances.

Challenges of cloud data migration

In the process of cloud data migration, there are several areas that organizations often underestimate:

  1. Data cleanup
  2. Training and change management
  3. The complexity of legacy systems
  4. Security and compliance
  5. Time and cost overruns
  6. Post-migration optimization
  7. Integration with existing systems
  8. Managing downtime

Let us understand these challenges in detail:

1. Data cleanup


It’s important to perform data quality checks and clean up data before migration. Companies often underestimate the amount of time and effort required for this process. Data that is incorrect, outdated, duplicated, or irrelevant can lead to inefficiencies and inaccuracies post-migration.

2. Training and change management


The shift to a cloud-based platform often changes how people work. This involves not only technical training on the new tools but also managing the change at an organizational level.

Without proper training and change management, user adoption might be slower than expected, reducing the effectiveness of the migration.

3. The complexity of legacy systems


Legacy systems often have complexities that are not fully understood until the migration process begins.

These systems may use outdated technology, have poorly documented features, or be intertwined with other systems in ways that make migration challenging.

4. Security and compliance


While cloud providers have robust security measures in place, it’s crucial for organizations to understand that security in the cloud follows a shared responsibility model.

The cloud provider is responsible for the security of the cloud, while the customer is responsible for the security of what they put in the cloud.

Also, compliance with industry-specific regulations can be complex in a cloud environment.

5. Time and cost overruns


Migrations often take longer and cost more than initially planned. This could be due to unexpected technical challenges, the need for additional training, the extra time required for testing, or other unforeseen factors.

6. Post-migration optimization


After the migration, additional work is often required to optimize the new environment. This could include fine-tuning performance, configuring autoscaling, implementing cost optimization measures, and more.

7. Integration with existing systems


While moving to the cloud, companies often still need to maintain some on-premises systems for a variety of reasons. Ensuring seamless integration between cloud and on-premises systems can be a challenging aspect of cloud migration.

8. Managing downtime


Minimizing downtime during the migration process is critical, especially for businesses where a few minutes of downtime can result in a significant loss. However, achieving minimal downtime often requires careful planning and potentially additional resources.

Thoroughly considering these often underestimated aspects during the planning stage can make your migration process more predictable and effective.

Data governance policies essential for cloud data migration

Data governance is an important aspect of any data management strategy, especially when considering a cloud data migration. It provides an overarching structure to ensure the security, availability, usability, and integrity of data in an organization.

Data governance includes various policies, procedures, and practices. In the context of a self-serve analytics environment and cloud data migration, these include:

  1. Data quality
  2. Metadata management
  3. Data access and security
  4. Data lifecycle management
  5. Data integration
  6. Compliance

Let us understand these policies in detail:

1. Data quality


This includes standardizing, cleaning, and ensuring the accuracy and completeness of data.

Bad data can lead to incorrect analysis and poor decision-making, so it’s crucial to establish procedures to continuously check and improve data quality.

Tools for data profiling and data cleaning can be used to automate some of these tasks.

2. Metadata management


Metadata provides context about data, making it easier for users to find and understand data. This is particularly important in a self-serve analytics environment.

Implementing metadata management involves creating a metadata repository and establishing procedures for maintaining it.

3. Data access and security


Defining who can access what data and under what conditions is crucial for data security. This includes setting up role-based access control, implementing data masking for sensitive data, and establishing audit logs to track data usage.

4. Data lifecycle management


This involves establishing policies for data archiving, retention, and deletion. This not only helps manage storage costs in the cloud but also ensures compliance with various data protection regulations.

5. Data integration


Ensuring that data from various sources can be seamlessly integrated is key for a comprehensive analysis. This might involve:

  • Establishing data naming standards
  • Defining a common data model
  • Implementing a data integration tool

6. Compliance


Depending on your industry, you might need to comply with various data protection regulations, such as GDPR or CCPA. This involves establishing procedures for data privacy, data residency, and data handling practices.

For organizations thinking of cloud data migration, here is some specific guidance around data governance:

a. Involve stakeholders: Start by getting all stakeholders onboard – from executives to data users. Their input can help define clear data governance policies and make implementation smoother.

b. Assess current state: Understand your current data governance maturity level and identify areas that need improvement. This could involve auditing your current data quality, security, and compliance practices.

c. Define governance policies: Based on the assessment, define clear data governance policies. This includes policies around data quality, metadata management, data access, data lifecycle, data integration, and compliance.

d. Implement Tools: Consider implementing data governance tools that can help automate some of the tasks. This could include tools for data quality management, metadata management, data cataloging, data lineage, etc.

e. Continual Monitoring and Improvement: Data governance is not a one-time task but an ongoing process. Continually monitor your data governance practices and look for areas of improvement.

By establishing robust data governance, organizations can ensure that their data is secure, reliable, and ready for use in a self-serve analytics environment. Moreover, it can make the cloud data migration process smoother and more efficient.

Cloud data migration: A case study perspective

There are many publicly available case studies that might provide insights into the process of cloud data migration and the implementation of data governance:

1. Capital One


Capital One is one of the most notable examples of successful cloud migration. They migrated most of their workloads to AWS, and they went all-in on the cloud.

They emphasized security as a major consideration in their migration process and created a “Cloud Governance Framework” to ensure that security was embedded in all aspects of their cloud deployment. The framework involved a continuous auditing process and an automated system to ensure compliance.

Here is a link to the AWS case study for more information: Capital One - AWS Case Study

2. Dow Jones


Dow Jones migrated its data centers to AWS. A notable aspect of their migration was their use of AWS’s IAM (Identity and Access Management) for data governance.

IAM allowed Dow Jones to control user and programmatic access to AWS services and resources, ensuring that only authorized services could access their data.

Here is the AWS case study for Dow Jones: Dow Jones - AWS Case Study

3. DBS Bank


DBS Bank from Singapore migrated to Google Cloud and developed their data governance strategy.

Their approach included a comprehensive review of data privacy and protection laws in their countries of operation and using Google Cloud’s tools for data management, access controls, and auditing to ensure compliance.

Here is a related Google Cloud case study: DBS Bank - Google Cloud Case Study

4. Telefónica


Telefónica is a global telecommunications company that migrated to Google Cloud. They employed a strong governance model, ensuring secure data access, implementing Identity and Access Management (IAM), and using tools for data loss prevention.

Here is a related Google Cloud case study: Telefónica - Google Cloud Case Study

Please note that while these case studies provide an overview of their process, the exact details of their data governance policies and procedures might not be disclosed due to the sensitive nature of the subject.

However, they should give you a good understanding of how large companies have approached cloud migration and data governance.

Rounding it all up

Cloud data migration is crucial for organizations because it allows businesses to scale their operations easily by offering unlimited storage and computing resources.

The need for robust data governance to ensure data quality, security, access control, lifecycle management, data integration, and compliance in a self-serve analytics environment.

Involving stakeholders, assessing the current state, defining governance policies, implementing data governance tools, and continuously monitoring and improving.

This information should serve as a comprehensive guide to understanding cloud data migration, the importance of data governance, and practical insights from industry case studies.

Each organization’s migration journey will be unique, and these guidelines should be adapted as necessary to meet specific circumstances.

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