In very simple terms data migration is the process of moving data from one system or environment to another. But there ...
In very simple terms data migration is the process of moving data from one system or environment to another. But there is a lot more to it than that for large organizations.
It is an essential part of many business activities, such as system upgrades, data center consolidation, and cloud migration. Sometimes a data migration might occur during a simple system upgrade, but due to the volume of data involved problems can be encountered. Effective data migration requires careful planning, execution, and management to ensure the integrity, security, and accessibility of data. With the rapid growth of data and the increasing complexity of technology environments, data migration has become a critical activity for organizations of all sizes and across every industry.
In this guide to data migration, we'll explore many aspects of data migration, including planning, execution, challenges, best practices, and (importantly) post-migration activities. This guide will equip you with the knowledge and tools to ensure the success of your data migration projects and to achieve your business objectives, now and in the future.
Table of Contents
.webp?width=500&height=333&name=saas-concept-collage%20(1).webp)
Data migration can be defined as the process of selecting, preparing, extracting, and changing data and permanently transferring it from one computer storage system to another. Also, the validation of migrated data for completeness and the decommissioning of legacy data storage are considered part of the entire data migration process. Data migration is an essential consideration for any system implementation, upgrade, or consolidation, and it is usually performed as automated as possible. An ideal step in a data migration should be to examine how to free up human resources from tedious tasks.
Data migration occurs for a variety of reasons, including:
Data migration can involve a wide range of data, including customer data, financial data, communication data (emails), operational data, and other types of information critical to an organization's functions. Data migration is typically carried out to achieve business objectives, such as improving system performance, reducing costs, enhancing data security, or supporting new business processes.
Data migration is a complex process that requires careful planning, execution, and management. It involves various stages. Proper execution of these stages is essential to ensure the success of data migration projects and to minimize the risks of data loss, corruption, or inaccessibility.
Data migrations are necessary for organizations to take advantage of and utilize the benefits of ever-changing technology. As technology and storage systems evolve, so must your data. In some cases, this is an easy task with minimal changes needed. Sometimes a relatively simple data migration task can be time consuming or cause other problems because of the volume of data involved. In addition, some legacy data platforms do not have a clear track forward or require complex changes within your environment.
Some of the key reasons why data migration is important are:
System upgrades and replacements: As systems become outdated, businesses may choose to upgrade or replace them with newer technology. Data migration is crucial in this process, as it ensures that data is effectively moved from the old system to the new one, minimizing any potential disruptions or data loss. This could be a simple 'copy' of data from one system to another, or it could be more complex.
Business growth and expansion: As businesses grow, they may need to consolidate data from different sources into a single platform or migrate data to a cloud-based environment to support their expanding operations. Data migration enables businesses to access and utilize data more effectively to support decision-making, improve processes, and drive growth.
Data security and compliance: Data migration can help improve data security by transferring sensitive data to more secure environments. It can also ensure compliance with regulations by ensuring that data is moved in accordance with legal and regulatory requirements.
Cost savings: Data migration can result in cost savings by reducing the number of systems that need to be maintained or by consolidating data into a single platform. It can also help businesses take advantage of more cost-effective cloud-based environments, or to move data to geographies where data systems can be operated more cheaply.
There are several different types of data migration, depending on the purpose, scope, and complexity of the migration project. Some of the common types are:
This involves moving data from one storage device or medium to another, such as from an on-premise storage array to a cloud storage service, from an expensive storage array to lower cost storage platforms. A business may choose to convert physical media to take advantage of more efficient storage technologies, for example using virtualization technologies. The data format and content itself will not usually be changed and can typically be upgraded with minimal or no impact.
This involves transferring data from one database to another, typically as part of a system upgrade or consolidation. It may be necessary to move from one database vendor to another or to upgrade the version of database software being used. The latter case is less likely to require a physical data migration, but this can happen with major upgrades. In these cases, a physical transformation process may be necessary since the underlying data format can change significantly. This may or may not affect behavior in the application layer, depending mainly on whether the data handling language or protocol has changed. Sometimes organizations will move from paid-for database technologies to open-source alternatives that can be hosted on different (and cheaper) technologies.
This involves moving data from one application to another, such as when migrating from an on-premises system to a cloud-based system, or that the data is being migrated from two similar applications (for example moving data from Slack to Microsoft Teams). If an application migration is involved in a data migration project, consider the impact on end users and prepare any necessary training that they might need.
This involves moving data as part of a change in business processes, such as when implementing a new enterprise resource planning (ERP) system. Examples of such data migrations are mergers and acquisitions, business optimization, and reorganization to embrace new markets or respond to business competitors. This type of data migration process may require automation tools and services that provide a holistic view of the business model and ensure process fidelity.
This involves moving data as part of a change in the operating system, such as when upgrading Windows Server versions. It typically involves transferring data, applications, and settings from the old system to the new one, ensuring compatibility and functionality of existing hardware and software, and managing any potential downtime or disruption to business operations. There are many reasons why organizations may choose to migrate their operating systems, including the need for improved performance, enhanced security, support for new applications, compliance requirements, or cost savings.
This involves moving data and applications from on-premises systems to cloud-based environments, such as moving from an on-premises data center to a cloud-based platform. This can involve migrating to a public cloud, such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform, or to a private cloud that is hosted internally or by a third-party provider. Cloud migrations often promise increased agility, reduced IT costs, improved security, better disaster recovery capabilities, and greater accessibility and collaboration.
Each type of data migration has its unique challenges. However, the general principles of effective data migration planning and execution remain similar across these types.
How Data Migrations Differ from Cloud Migrations
Data migrations and cloud migrations are both types of migration projects that involve moving data from one environment to another. 
Data migration is a broad term that refers to the process of moving data from one system, application, or environment to another. This can include moving data from one on-premises system to another, consolidating data from multiple sources into a single platform, or upgrading databases or applications.
Cloud migration is a specific type of data migration that involves moving data and applications from on-premises systems to cloud-based environments. Cloud migration can be more complex than other types of data migration because of the unique characteristics of cloud-based environments. Cloud migration often involves reconfiguring applications, updating security protocols, and integrating with other cloud-based services. It might also involve many additional steps in addition to moving the underlying data.
There is a huge list of data migrations that can be undertaken. Let's review two short examples:
A typical example of a data migration would be a transition from a legacy file format. For example, PST files. This is an old form of data storage still used today in some organizations. This format was extremely popular before Cloud infrastructure was available for email data storage. Many years ago, when on-premise Microsoft Exchange mail data storage was at a premium, some companies opted to store e-mails in PST files. PST files may still technically work with today’s modern systems; however, they have risks and disadvantages. Cloud-native technology was recently introduced to help overcome these risks and disadvantages.
Not migrating PST files could potentially put you at risk. To take advantage of the cloud, your PST data must undergo a data migration. In this case, your PST data will be processed and ingested into the cloud.
Another example would be migrating Enterprise Vault data. An Enterprise Vault migration is the process of transferring archived data from one location to another. This could include moving archived data from one Enterprise Vault server to another, or transferring archived data from a legacy system to Enterprise Vault, or from Enterprise Vault to a cloud-based platform. Enterprise Vault migration requires careful planning and coordination to ensure that all archived data is transferred successfully. Time should be given to review and analyze all of the additional steps which might be required along with copying the data.
Migrating Enterprise Vault data is important for ensuring that your legacy archived data remains secure and accessible. With the right migration strategy, you can ensure that your data is kept safe from external threats, has consistent availability and performance, and is easily searchable. Additionally, migrating Enterprise Vault data can help reduce storage costs and make it easier to share and collaborate.
Data migration can bring numerous benefits to a large organization, including:
Improved data quality: Data migration projects provide an opportunity to clean up and improve the quality of data. During the migration process, data can be
leading to a more accurate and reliable dataset. Old data, of low or no business value, can be expired rather than being migrated; helping the organization further.
Increased efficiency: By consolidating data from multiple sources into a single platform, data migration can help organizations streamline their data management processes, reduce manual data entry, and increase efficiency.
Enhanced decision-making: With improved data quality and more efficient data management processes, organizations can make better-informed decisions based on accurate and timely data.
Cost savings: Data migration can help organizations reduce costs associated with maintaining and managing multiple systems, reducing hardware and software maintenance costs, and improving the efficiency of data-related processes.
Improved data security: Data migration can also help organizations improve their data security by consolidating data into a secure and compliant platform, reducing the risk of data breaches and other security threats.
Scalability: Data migration can also enable organizations to scale their data management capabilities to meet growing business needs, such as handling larger volumes of data or supporting new data sources.
Preparing for a data migration project is crucial to its success. Here are some steps that organizations should take to prepare for a data migration:
Define the scope and objectives of the project, including what data needs to be migrated, what data can be expired, the expected timeline, and the budget. Determine which stakeholders and team members will be involved in the project and what their roles and responsibilities will be.
If the data migration is likely to impact end-users a communication plan should be defined. Include in this how, when and the frequency of communication. Ensuring end-users are bought into the data migration project will help ensure its acceptance and ultimately its success.
Conduct an assessment of the data that will be migrated, including its quality, format, and structure. In some situations this might be a time consuming process, so a partial assessment might be conducted. This will help identify any data issues that need to be addressed before the migration and ensure that the data is suitable for the target system. Consider that some of the data, while stored in the same environment, might be in different formats as it was previously created in different software versions.
Develop a comprehensive plan that outlines the steps involved in the migration, including data mapping, extraction, transformation, and data loading (ingestion). The plan should also include contingencies in case of any unforeseen issues wherever possible.
Identify the target system that the data will be migrated to, including its technical requirements and any specific data formats or structures that are required. In addition consider how the data will transit from the original environment to the new environment. Are additional network infrastructure or resources going to be needed?
Before conducting the actual migration, conduct testing to ensure that the data is migrated accurately and in a timely manner. This will help identify any issues that need to be addressed before the migration and ensure that the migration process runs smoothly. If possible perform a proof of concept with the data migration tool.
Once the data migration is complete, ensure that users are trained on the new system and its functionality. This will help ensure that they are able to effectively utilize the new system and maximize its benefits. It will help make them more productive on day-one of using the new system than ever before on the old one.
A successful migration project requires the planning and execution of a strategy that sets migration goals, creates a timeline, identifies resources, anticipates challenges, and defines the criteria that assesses the project’s success. Consider your data migration process as a road map to the goal. It is often necessary to consult an expert to help navigate the highs and lows of data migration. There are many moving parts of data migration, so invest in the right tools and services to minimize the impact of end-users and ensure a successful migration.
The data migration process typically involves the following steps:
Planning: Define the scope and objectives of the migration project, identify the source and target systems, and develop a migration plan that outlines the steps involved in the migration.
Analysis: Analyze the data that needs to be migrated, including its format, structure, and quality. Identify any data issues that need to be addressed before the migration and ensure that the data is suitable for the target system.
Data mapping: Map the data from the source system to the target system, ensuring that the data is mapped correctly and that there are no data mapping errors.
Extraction: Extract the data from the source system, taking into account any data extraction requirements such as data volume, extraction frequency, and data formatting.
Transformation: Transform the data as necessary to ensure that it is suitable for the target system, including any required data formatting, data cleansing, and data validation.
Loading or ingestion: Load the data into the target system, ensuring that the data is loaded accurately and in a timely manner.
Testing: Conduct testing to ensure that the data is migrated accurately and that the target system is functioning correctly. This will help identify any issues that need to be addressed before the migration and ensure that the migration process runs smoothly.
Scale-up: Once testing has validated the overall process full-scale production migration can then commence.
Go-live: Once the testing is complete and any issues have been addressed, go-live with the new system. This includes training users on the new system and ensuring that the new system is fully integrated with the organization's existing systems and processes.
Post-migration validation: Conduct post-migration validation to ensure that the data is accurate and that the new system is functioning correctly. It's worth assessing this at a data level as well as with end-users (if the data migration involves end-user data).
In this video we share with you how easy it is to create migration templates to perform more than just the data migration requirements of a digital transformation project. You can provide communication updates to users, migrate additional workloads and much more, with over 100 workflow steps - and more are being added all the time.
Data migration can be a complex and challenging process. Here are some common challenges that organizations may face during a data migration:
Poor data quality can lead to errors and inconsistencies during the migration process. This can result in data loss, corruption, or duplication. It is important to identify and address data quality issues before beginning the migration process. Consider that some of the source data may have been written to a target storage system many years ago.
Mapping data from the source system to the target system can be complex and time-consuming. It is important to ensure that the mapping is accurate and that there are no data mapping errors. If the data migration involves a cloud migration, some of this effort will have already been performed many times before and be available from the cloud vendor.
Large data volumes can make the migration process slow and difficult. It is important to optimize the migration process to minimize the impact on the organization's operations. Consider also the count of items that are to be migrated, as well as the overall volume.
The source and target systems may have different data formats, structures, and requirements. It is important to ensure that the systems are compatible and that the data can be migrated successfully.
During the migration process, there is a risk that sensitive or confidential data may be exposed or lost. It is important to implement appropriate security measures to ensure the confidentiality and integrity of the data.
The migration process may require system downtime, which can impact the organization's operations. It is important to minimize the downtime and ensure that critical business processes are not disrupted.
Testing the migration process can be time-consuming and may require significant resources. It is important to conduct thorough testing to identify and address any issues before the migration.
After the migration, users may need training to become familiar with the new system and its functionalities. It is important to ensure that users are properly trained to avoid any negative impact on the organization's operations.
Here are some best practices for data migration that can help organizations ensure a successful migration:
Plan thoroughly: Develop a detailed migration plan that outlines the steps involved in the migration process, including a timeline, budget, and resource requirements. Identify potential risks and challenges and develop strategies to mitigate them. Also begin to outline what will happen when unforeseen issues arise; they should not be allowed to grind the project to a halt.
Validate source data: Ensure that the source data is accurate, complete, and consistent. Conduct data profiling to identify any data quality issues that need to be addressed before the migration, even if that can only be conducted on a subset of the data (due to things like time constraints).
Map the data carefully: Develop a data mapping strategy that maps the data from the source system to the target system. Validate the data mapping to ensure that the data is mapped accurately.
Transform the data as necessary: Develop a data transformation strategy that addresses any data formatting, data cleansing, and data validation requirements. Test the transformation process to ensure that it is accurate and efficient. For regulatory and compliance purposes ensure that any necessary transformation is fully understood, documented and audited.
Test the migration process: Conduct thorough testing of the migration process to identify any issues and ensure that the data is migrated accurately. Test the migrated data to ensure that it is complete and accurate. We always recommend that a proof of concept is performed to ensure that the chosen solution matches all of the business and project requirements.
Implement appropriate security measures: Implement appropriate security measures to ensure the confidentiality and integrity of the data during the migration process. This can include data encryption at-rest as well as in-transit.
Minimize downtime: Minimize the impact of the migration process on the organization's operations. Plan for system downtime and ensure that critical business processes are not disrupted. Many global organizations no longer have an 'acceptable' maintenance window in order to perform any data migration.
Communicate with stakeholders: Communicate with stakeholders throughout the migration process to keep them informed of the progress and address any concerns or issues that arise. If the data migration involves end-user data then it is best practice to involve them in the data migration planning at the earliest possible stage. This helps ensure that they understand and buy-into the overall process, and also sets their expectations of the future systems.
Conduct post-migration validation: Conduct post-migration validation to ensure that the data is accurate and that the new system is functioning correctly. Address any issues that are identified whenever possible, especially if they cause significant issues to your organization.
Engage with experts: A data migration process can be a complex undertaking. Engaging with industry experts can greatly help smooth out the process and their expertise can be used by your organization to help meet project timelines and goals.
Train users: Ensure that users are properly trained on the new system and its functionalities. Provide support and resources to help users adapt to the new system. This can be given during the data migration in most cases, and delivered or re-emphasized after the migration.
Choosing the right data migration software is an important decision that can have a significant impact on the success of your data migration project. Here are some factors to consider when selecting data migration software:
Compatibility: The software should be compatible with both your source and target systems. Check the specifications and make sure it supports the types of data you need to migrate. Consider that the source system might include data of various formats depending how long the system has been in place.
Ease of use: The software should be user-friendly and easy to set up and use. Consider software that provides intuitive interfaces and helpful documentation. In addition the data migration vendor might provided managed services where the operational activities in the migration are performed by external staff. In this situation ensure that adequate progress reporting is available to internal stakeholders and that appropriate teams are aware of their responsibilities.
Features: Look for software that includes all the features you need to complete your migration, such as automated scheduling, error handling, and support for multiple data formats. If your overall data migration involves much more than moving data from a source system to a target, consider whether (and how) all the additional steps can be automated and included in the overall process.
Scalability: Consider software that can handle your data migration requirements now and in the future as your organization grows and your data needs increase. If your organization is likely to go through mergers and acquisitions can the chosen migration software be used in any potential future projects?
Support: The software vendor should provide reliable customer support, including online resources, email or phone support, and assistance with any issues that arise during the migration process. Swift resolution of unexpected issues will ensure that any project timelines can be met.
Security: The software should have robust security features that protect your data during the migration process, including encryption, access controls, and auditing.
Cost: Consider the cost of the software, including any ongoing fees or licensing requirements, and compare it to the benefits it provides. In many situations the cost of the software can still lead to a positive ROI in a short time due to the lower costs of the target system (compared with the source)
By considering these factors, you can choose data migration software that meets your organization's needs and helps ensure a successful, on-time migration.
Post-migration activities are important to ensure that the data migration has been successful and that the new system is functioning correctly. Here are some key activities that organizations should consider after completing a data migration:
Validate data: Validate the data in the new system to ensure that it is accurate and complete. Conduct data quality checks and compare the data to the source system to identify any discrepancies.
Conduct testing: Test the new system to ensure that it is functioning correctly. Conduct functional testing to ensure that all functionalities are working as expected, and performance testing to ensure that the system can handle the expected workload. If possible try to test future load on the new system - this is the load that you expect in 1-5 years time, as your business grows.
Conduct user acceptance testing: Involve users in testing the new system to ensure that it meets their needs and expectations. Identify any issues and address them before the system is fully utilized.
Train users: Train users on the new system and its functionality. Provide resources and support to help users adapt to the new system.
Monitor performance: Monitor the performance of the new system to identify any issues and ensure that it is functioning correctly. Address any issues that arise promptly. If the data migration also involved a cloud migration consider that additional network infrastructure and resources may be required in order to have good, reliable access to the internet based resources.
Address issues: Address any issues that arise promptly to minimize the impact on the organization's operations. Develop a process for reporting and addressing issues and ensure that it is well-communicated to all stakeholders.
Measure success: Measure the success of the data migration by comparing the performance of the new system to the performance of the old system. Identify any areas of improvement and address them to ensure that the organization is fully realizing the benefits of the migration. Involve users and ask them how well they think the new system performs.
Develop a maintenance plan: Develop a maintenance plan for the new system to ensure that it remains secure and up-to-date. Remember that in many cases having regular backups even of cloud based data, is essential. Identify the human resources required to maintain the system and ensure that they are in place.
Are you planning to migrate data from a legacy system to a new solution? Cloudficient examines the seven steps that are needed for any data migration...
Are you using data migration validation best practices? Follow these steps to ensure the integrity and reliability of your data during migration.
Creating a comprehensive data migration strategy reduces the risk of surprises. Working with an experienced migration team ensures even better...
