Cloud Data Migration Strategy and Best Practices


Table Of Contents

Enterprise organizations have long since migrated to the cloud to handle increasing amounts of data. But this doesn’t mean that these organizations only use one cloud-based system; rather, they use multiple cloud systems because different providers offer specific features that benefit different use cases. Essentially every organization is multi-cloud, using various cloud-based systems to handle data movement and use-case scenarios.

In most cases, using multiple cloud-based systems with different approaches to security and access makes it difficult for IT teams to manage these systems with a consistent approach to data governance and security. This applies to user data access and extracting data from multi-cloud systems.

Further, IT teams may also have to deal with legacy on-premises systems that must be migrated to the cloud. In this case, ensuring that cloud data migration efforts follow a strict procedure focusing on data integrity is essential.

This article outlines the importance of cloud data migration and why organizations need to build an effective strategy to migrate data to the cloud. Then, we offer a solution for extracting, preparing, packaging, and distributing data effectively to and from multi-cloud environments.

Let’s start with a brief overview of cloud data migration.

What Is Cloud Data Migration?

Migrating data to the cloud is the process of migrating data from on-premises storage to a cloud-based environment.

Cloud data migration involves moving data from various on-premise and legacy sources such as databases, applications, file systems, and websites. The migration process involves transferring the data from the source to the destination using various cloud data migration tools, processes and strategies, such as Infrastructure as a Service (IaaS) systems, Platform as a Service (PaaS), and more.

The main objective of migrating data to the cloud is to ensure that data is transferred accurately, efficiently, and securely to the destination. This process involves:

  • Analyzing the data to be migrated
  • Selecting the appropriate migration method
  • Cleaning and preparing the data for transfer
  • Performing the migration
  • Validating that the data has been successfully migrated

Why is Data Migration to the Cloud Important?

Companies have to deal with more and more internal and external data, and it’s important that this data is managed properly. Sensitive data (e.g., employee information, customer data like credit card numbers, addresses, etc.) must be stored securely, and aspects of governance, such as access to this data, must be established per local and national laws.

Migrating data to the cloud assists organizations with managing large data sets effectively, ensuring security yet ease of access, and much more.

1. Allows More Types of Data and Larger Amounts of Data to be Ingested in Less Time

Because cloud-based data storage is infinitely scalable, more types and larger amounts of data can be stored in the system. Cloud data warehouses, for instance, have the capability to ingest structured data from databases and raw data from archives and data lakes to the cloud data warehouse parallelly without worrying about their processing. Also, cloud data warehouses provide data processing tools for all types of available data in a single place. You can process all formats of data, such as JSON, XML, CSV, text, etc., and convert them into a single format using tools available at cloud data warehouses. This is often not possible with on-premises data infrastructure due to the lack of processing power.

2. Easily Scalable

For some types of organizations, the inflow of data is always increasing. For instance, large healthcare entities like provincial healthcare organizations in Canada use interconnected cloud-based systems to securely share patient information between hospitals, doctor’s offices, and other healthcare facilities. Thanks to the cloud, as more patients are added, the system can scale infinitely while still ensuring that governance and security measures are maintained.

3. Reduced IT Costs

Traditionally, IT was always involved in data requests, whether those requests were internal or external. The resources and time required to find, prepare, and distribute the data sets was extremely taxing on IT teams, not to mention the time and energy required to verify the credentials of the requester in an effort to maintain security.

With solutions like a data marketplace, data consumers can access data on demand without the constant involvement of an IT team. Revelate, for example, offers a completely automated marketplace solution that incorporates centralized security, self-service data access, and a customizable storefront so companies can display their data products according to their needs. When a data set is requested, established security and access checks are performed, ensuring that the right data is distributed to the right person every time. Of course, monitoring and management are still performed by data professionals, but the workload is decreased as they can perform management tasks using centralized software rather than having to jump between different cloud environments and legacy systems to ensure that governance and security standards are met and enforced.

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4. Builds a Flexible and Sustainable Data Foundation

Asian Paints, a paint company in India, was termed India’s biggest data science company that sells paints by a Ph.D. scholar at Harvard Business School. Why? Asian Paints has invested heavily in analyzing patterns in the sales of its products. By interconnecting the systems at different retail locations using the cloud, the machine learning models at Asian Paints can accurately predict the shade, color, and quantity of paint required at each store. This helps them manage inventory and maximize profits.

While other FMCG companies spend 30-40% of the retail price on logistics and distribution, Asian Paints has restricted this cost to 3%. All thanks to the accurate prediction of requirements using ML models supported by the power of cloud computing.

5. Empowers Business Analysts, Data Scientists, and Data Engineers

When you opt for a cloud data infrastructure, you free your data scientists and data engineers of tasks such as configuring software systems. Cloud systems often provide easy-to-use tools to handle and process data using just a few clicks. They also provide faster processing which means that tasks can be completed faster. This helps data scientists and data engineers shift their focus to solving more complex business problems.

How to Create a Cloud Data Migration Strategy?


A cloud data migration strategy is a plan for moving data from an on-premise data center or legacy system to a cloud-based environment. The strategy involves a combination of technology, processes, and tools to ensure that data migration to the cloud is seamless and secure.

Gartner has outlined recommendations that should be involved in a cloud data migration strategy:

1. Rehost

The first recommendation suggests that an organization should move their applications to a new hardware environment, then once that’s complete, change the infrastructure configuration of the applications. This process is called rehosting and makes migrating applications to the cloud faster since the infrastructure is prepared in advance.

Several options are available at this point. Infrastructure as a service companies provide one solution, but even though this type of service was just established in 2010, it’s already being overshadowed by newer technologies. These new technologies consist of things like containers and serverless computing, which provide even more flexibility and options for managing applications in the cloud. These features are usually provided by modern PaaS companies.

2. Refactor

Once the hosting is set up, applications need to be rewritten or reconfigured to take advantage of a cloud network (the core architecture of the application remains intact). This is where PaaS comes into play, as it provides a holistic repository of resources like infrastructure, frameworks, languages, containers, and sometimes even serverless computing that developers can draw from. So instead of reinventing the wheel, developers can use the resources available to them and leverage the strategic code established by the organization.

3. Revise

The revise step builds upon the previously mentioned strategies and suggests that, at this point, more significant changes are made to the architecture and code of the systems involved in data migration to the cloud. Of course, this is done so that the applications can take full advantage of the cloud-based services being used and requires advanced expertise and planning to execute properly.

4. Rebuild

With the rebuild step, you’re taking the revise approach and going further by completely eliminating the old code base and replacing it with the new and improved one built for the cloud. This should be done over a long period of time, and companies should only decide to completely axe the legacy system completely if it’s no longer meeting their needs.

5. Replace

Replacing the code base instead of rebuilding it is another option that is probably more likely that organizations will be using. The replacing step occurs when a company migrates to a pre-built solution provided by a vendor and therefore doesn’t have to build its own environment from scratch. With the replace solution, companies migrate their applications data only, fully utilizing the vendor’s environment as well as the applications they provide. An example of this could be migrating from one CRM to another, or on a larger scale, migrating to Microsoft 365 from a legacy Microsoft productivity suite.

Data Marketplace for Data Migration From On-Premise to Cloud

Data migration from on-premise to the cloud requires strategic planning and forethought. At the end of the day, a cloud data migration is essentially rebuilding an organization’s data ecosystem. A data marketplace solution is one avenue that can assist with not only moving data from legacy systems to the cloud but clean and organized data.

Data marketplaces assist with this being an essential component of your data ecosystem and assisting with the democratization of data in your organization.

Creating a Data Ecosystem Using Data Marketplaces, Data Catalogs, and ETL Tools

To create a data ecosystem, different components need to be brought together that can help manage data efficiently and effectively. Data catalogs, ETL tools, and data marketplaces are essential components in your organization’s data ecosystem.

  • Data discovery involves finding the different data sources in your organization and what data lives within them. It’s essential for organizations to do this to ensure that all data is accounted for, and ensure that the proper security and access measures are in place for sensitive data. Once data is discovered, it can be organized and categorized.
  • Moving your organization’s data from legacy systems to the cloud is similar to moving to a new house. When most people move, they often sell certain items, give them to others, or simply throw them away. It’s like having a fresh start in your new environment without the relics of your old one weighing you down. Data catalog tools help us discover, understand, and manage data assets. They assist with searching for internal data assets based on various criteria, such as data type, format, location, and metadata. Information about the quality and lineage of the data can also be gleaned using data catalogs. This helps stakeholders make informed decisions about the data they access.
  • ETL stands for Extract, Transform, and Load. It is a process used to extract data from different sources, transform it into a format that can be used by the target system, and load it into the target system. ETL is a technology that has been around for a long time, but it’s evolved to support the demands of the cloud. ETL tools can help us consolidate data from different sources and transform it into a format we can use for analysis and reporting.
  • Data marketplaces enable the distribution of data sets from a source to a target via automated processes and self-service options.

By combining data marketplaces, ETL tools, and data catalogs, a data ecosystem can be created to manage data effectively.

How Data Marketplaces Utilize Data Migration in Cloud To Democratize Data?

Data democratization refers to making data accessible and available to everyone within an organization rather than restricting access to a small group of people, such as data scientists or IT specialists. Anyone with the necessary permissions can access, analyze, and use data to make informed decisions without requiring specialized technical knowledge or skills.

Here are some of the ways by which data marketplaces utilize cloud data migration to democratize data.

  1. Scalability and flexibility: Cloud data migration is great for organizations as they can scale their infrastructure as load increases or decreases. This makes storing and processing small as well as large amounts of data easier. From there, data marketplaces can be used to distribute this data effectively.
  2. Data security: Data marketplaces can leverage cloud data migration by using encryption and access controls to enhance data security while data transfers. This ensures that data is protected from unauthorized access and that users can securely access the data they need.
  3. Collaboration: Cloud data migration makes it easier for organizations to collaborate and share data. By centralizing data in the cloud infrastructure, data marketplaces can facilitate collaboration and foster a data-driven culture where companies can work together to achieve shared goals.
  4. Accessibility: Data marketplaces make accessing data from anywhere, anytime, and using any device easier. Revelate, for instance, has a variety of automated processes that occur when a data set is requested, meaning that IT can be hands-off and individuals can get the data they need through a self-service platform.
  5. Data Monetization: Data marketplaces help organizations monetize data by enabling them to sell, share, or exchange their data with other organizations easily, and without the involvement of IT teams.

Why is a Data Marketplace Crucial for Your Cloud Data Migration Strategy?

Data marketplaces are important in each cloud data migration strategy aspect, from data consolidation to cost optimization.

Aspect Importance
Data consolidation A data marketplace can act as a central repository for data. This will allow your company to consolidate data sources from multiple cloud environments and on-premises systems. This results in a simplified data migration process and reduces the risk of data loss or corruption.
Data governance Companies can use data marketplaces to maintain data integrity during the cloud data migration process. A data marketplace supports tools and strategies for data quality and governance. Using these tools, we can ensure that data is accurate, consistent, and meets regulatory compliance requirements.
Data sharing Using data marketplaces, we can enable data sharing and collaboration between different teams and departments within an organization and with external partners or customers. We can also use data marketplaces to integrate and migrate across multiple cloud environments to promote greater data visibility and transparency.
Speed of migration A data marketplace lets organizations to leverage cloud-based services and gain competitive advantages through faster data migration. It provides pre-built connectors, APIs, and other tools that we can use to accelerate the data migration process and reduce the time to value for cloud-based solutions.
Cost optimization A data marketplace can offer a cost-effective way to access a wide range of cloud-based data services and tools. This eliminates the need to invest in expensive infrastructure or resources, reducing the total cost of ownership for cloud-based solutions.

Cloud Data Migration Best Practices

While using cloud data migration, we must follow specific heuristics and practices while using cloud data migration. Some of the best practices for implementing a successful cloud data migration strategy are as follows:

  • Define clear goals: First, specify if the data migration process facilitates moving to a new system, upgrading an existing system, consolidating data, or all of them.
  • Test your migration: Create a testing plan to verify the accuracy and completeness of the data migration process. Testing the migration process in a staging environment with sample data before using it for the actual data transfer is a good best practice.
  • Ensure security: Ensure data is secure during migration by using encryption and security protocols to protect sensitive data.
  • Communicate with stakeholders: Inform stakeholders about the data migration cloud process, including the goals, timeline, and any issues that can arise. Ensure everyone knows the impact of the events occurring during the migration process.
  • Backup your data: Before the migration process starts, it’s essential to back up your data. This ensures that you have a copy of your data if anything goes wrong.
  • Plan for post-migration activities: Plan for post-migration activities, such as data validation and reconciliation. This ensures the data is accurate and complete after the migration.
  • Have a rollback plan: In case of migration failure, a recovery plan needs to be in place. A rollback plan to revert to the original system should also be created.
  • Document everything: All aspects of the migration process should be documented, including the plan, testing procedures, issues, and resolutions. This can be used for future reference and to improve future migration processes.



Cloud data migration is an essential process for modern businesses to take advantage of the benefits of cloud computing. As businesses deal with the influx of data from within and outside of their organization, it’s important that they take advantage of this data for direct monetization or indirect monetization by using the data to improve their products and services.

But migrating to the cloud from on-premise systems is a process that should be approached more like a marathon rather than a sprint. It’s important to practice careful planning, create thoughtful strategies, and involve data experts and consultants where needed to ensure the migration process goes smoothly.

Once data is migrated to the cloud and the newest technologies like machine learning, artificial intelligence, and automations are implemented, every organization should be able to reap the benefits of the time and resource savings resulting from using cloud-based technologies.

By embracing cloud data migration and utilizing the data marketplace, your company can stay competitive in today’s rapidly changing market.

Book a free trial to try Revelate’s data marketplace solution today and discover how it can be an essential component in your cloud-based data ecosystem.

Unlock Your Data's Potential with Revelate

Revelate provides a suite of capabilities for data sharing and data commercialization for our customers to fully realize the value of their data. Harness the power of your data today!

Get Started