Data Migration: Strategy, Tips & Best Practices

Business systems need to be modern to have any relevance in today’s technological landscape. Transitioning from one platform to another is typically a key milestone in a long-term data management strategy. 

Once a new platform has been selected, the main challenge of how to safely and efficiently migrate data to a new system begins. It is crucial that you have a thorough understanding of the risks involved before embarking on a data migration. You should have a carefully planned strategy in place with experienced support to monitor the whole process. 

Understanding the fundamental steps in the migration process is essential for a successful migration. Read on to find out how you can achieve a successful data migration, and how we can help with data management solutions. 

What is data migration?

Data migration is the process of transferring data from one system to another. This may appear a seemingly simple task, however it requires plenty of preparation to ensure it is completed correctly. 

The migration will involve transitioning both storage and database or application and requires planning, creating backups and validating the data. The migration is complete when everything that is needed has been transferred over, the old system has then been decommissioned.

Within the framework of the extract/transform/load (ETL) process, any data migration inherently encompasses at least the transform and load phases. This entails subjecting extracted data to a sequence of operations for refining before it becomes suitable for loading into a designated destination.

Why do businesses perform data migrations?

There are a number of different reasons for an organisation to carry out a data migration across the business. These include:

• overhauling an outdated system
• expanding database storage 
• establishing a new data warehouse
• merging newly acquired data 
• implementing a new system to work alongside pre-existing applications 
• Replacing data silos by transferring to a centralised database to improve data quality and achieve interoperability. 
• moving data to a cloud based platform

Data migration vs data integration

The main difference between these two approaches is that data integration is the process of combining different internal and external data sources into a unified view, whereas a data migration is the process of transferring data from one system or location to another, often involving a change in the data's format, structure, or storage location.

Data integration is a fundamental pillar within the data management strategy, enabling interconnectivity between systems and providing access to content over a diverse spectrum of subjects. The combination of datasets helps to form a reliable data source that can be used for analysis, the extraction of valuable business insights, and comprehensive reporting.

Data migration has an end point and culminates in the transfer of all required information to a designated destination. Data integration on the other hand can be a continuous process with the flow of real-time data and the seamless exchange of information across multiple systems.

Why a data migration strategy is important

Data migrations are important for businesses that are undergoing transitions or changes to their data systems. Getting it wrong can cause significant issues, so it’s vital a robust data migration strategy is in place. 

Here are the main reasons why formalising a data migration strategy is important:

• Data quality: During a data migration, the risk of data corruption, loss, or inconsistencies increases. A well-planned and executed strategy includes data validation and cleansing processes to ensure that data remains accurate and reliable. Without planning, data can become subject to data quality issues that carry over to the new system too.
• Minimised downtime and disruption: Performing a data migration without a strategy can result in significant downtime if things go bad, which can impact business operations and your customer experience. Things like scheduling migrations during off-peak hours, phased or parallel run migrations, can all help limit the disruption to your business and customers.
• Cost efficiency: Planning appropriately for a migration helps reduce the overall cost of migration, as you reduce the risk of something going wrong. Unplanned migrations can lead to unforeseen expenses due to extended downtime, data errors, or system inefficiencies.
• Risk mitigation: A detailed strategy can identify potential risks and be used to outline plans for mitigation in case things do go awry. Anticipating challenges can ensure that the migration process is executed smoothly and minimises any disruption. 
• Data security and compliance: Data security measures and remaining fully compliant with data protection laws are vital when undertaking a data migration. This prevents data breaches, unauthorised access, and makes sure you’re adhering to legal and regulatory standards.
• Scalability: A well-structured strategy considers future scalability needs. This will avoid unnecessary migrations in the future that can be avoided when you migrate to a new system that is able to accommodate future growth and changes when required.
• User training: Planning should include training and onboarding of users to the new system. This helps them understand and navigate the new system effectively and will reduce resistance to change knowing they have support available to answer any questions about it. 

What should a data migration strategy consider?

A thought out data migration strategy should consider the following:

• Understanding the data: The source data should be fully audited before migrating it. This ensures it won’t cause any issues.
• Data cleansing: You should resolve any identified issues within your source data before migration. Depending on the size of data, this may require the support of an external data specialist to help cleanse data to ensure it’s accurate and reliable. 
• Maintenance: Data needs to be maintained to make sure it is accurate. Having processes in place to make sure data quality is maintained and that standards don’t slip, which can lead to errors, inconsistencies and low quality data. 
• Tracking and reporting: Being able to track and report on data quality helps with data integrity. This process should be user-friendly and automated where possible to save time whilst remaining accurate.    

Types of data migration

There are 3 main types of data migrations that businesses will use. They each have their own advantages and disadvantages.  

Big bang database migration

In a big bang database migration, the entire transfer is executed within a fairly limited time frame. During this period, live systems undergo downtime as data undergoes ETL processing and shifts to the new database.

This can be a risky migration strategy as it requires downtime and can be expensive if it goes wrong. The appeal is that it can consolidate the execution of changes in one go, avoiding the hassle of working simultaneously across the old and new systems. When it goes as planned, it’s completed within a short time frame, making it the fastest data migration method. 

There is significant pressure to get this type of migration completed quickly as crucial business resources remain offline until it is finished, therefore it’s best to perform a big bang database migration during off-peak hours to limit the impact on customers.

Who is it suited for?

The big bang migration strategy is suitable for small businesses working with small amounts of data. If the big bang approach aligns with your business needs, it's advisable to simulate the migration process beforehand, ensuring you are well-prepared for the actual event.

Trickle database migration

A trickle migration follows a phased approach and is very different to the big bang method.

During a trickle database migration, the old system and the new system operate simultaneously, eliminating any downtime or disruptions in operations. This allows data to be continuously migrated to the new system in real-time. 

Unlike the big bang approach, trickle implementations tend to have a more intricate design and take much longer to perform. It’s vital to track which data has been already transported, and users need to be able to switch between both systems to access the data they require. This can mean keeping the old system fully operational until the migration is completed, which increases costs and requires data to be synchronised in real time over two different platforms. 

Who is it suited for?

A trickle database migration is recommended for medium and large businesses that want to avoid long downtime and have enough expertise to tackle any technological challenges that may occur. 

Parallel run database migration

A parallel run migration involves duplicating data from the source database to the target database. The new system is installed alongside the old one, and both operate in tandem, enabling users to continue accessing and working with the source database even as the migration takes place.

This method offers several advantages, including reduced business disruption, expedited migration timelines, and lowered costs, particularly when factoring in the potential impact on business operations and the effort involved in coordinating a full-scale migration.

Who is it suited for?

A parallel run migration is best suited for organisations and scenarios where uninterrupted access to the database is critical and downtime would have a significant negative impact on business operations.

Database migration best practices

Initiating the intricate process of a data migration begins with assessing current data and assets and carefully designing a plan, so it’s completed safely and efficiently. The initial phase of planning can be divided into these main steps.

1. Set up the database migration project scope

The primary objective of this stage is to eliminate redundant data and determine the essential information necessary for the system to run efficiently. Performing a comprehensive evaluation of both the source and target systems is required, and this should involve collaboration with data users who will directly be impacted by the impending changes.

2. Analyse your current data

A comprehensive migration plan should include a detailed evaluation of the existing system's operational needs. Understanding this can help you seamlessly integrate data into the new system. 

3. Backup your data before moving it

Backing up your data before starting your migration is an essential best practice that safeguards your data.  There’s a degree of inherent risk involved during a migration, unforeseen errors, technical glitches, or compatibility issues can arise, potentially leading to data loss or corruption. Backing up data preserves it in the original format giving you a recovery option in case something unexpected occurs during the migration. 

4. Communicate the process

Inform all stakeholders about the migration timeline, potential impacts, and the major benefits. There’s a reason you’re doing the migration so if everybody understands why it’s happening, they’re far more likely to get behind the idea. People can be resistant to change, especially when they feel it’s unnecessary so you want to be as transparent as possible with the migration process. Offering training to users on the new system will also help them get accustomed to it faster. 

5. Focus on business goals and customer satisfaction

This is the phase where the actual migration happens and data is extracted, transformed, and loaded into the new system. Using the big bang strategy, this phase will be complete within a couple of days. On the other hand, if the trickle approach is used, data is moved gradually, so the execution process may be extended considerably. 

As highlighted earlier, this gradual approach ensures uninterrupted operations and mitigates the risk of things going terribly wrong.

Opting for a phased approach requires you to remain vigilant to prevent disruption to regular system operations. It’s important that there is clear communication between the migration team and various business units so those involved know when each sub-migration is going to happen and which users will be impacted.

6. Testing during and after data migration is key

Testing is an integral part of the data migration and shouldn’t just be done at the end. If you’re using the trickle strategy, it’s an integrated process that spans the design, execution, and post-migration stages. It’s imperative to systematically test each segment of migrated data, promptly addressing any issues that may arise.

Regular and comprehensive testing is the best way to ensure the secure and accurate transfer of all data into the new infrastructure. If there are issues with how the data is being migrated, you want to know about it early on so you can resolve it rather than finding out later on and potentially having to restart the whole process.

7. Complete a post-migration audit

Results should be validated with key business users prior to fully launching the new system. This critical phase ensures information has been transferred and logged accurately. Only following a comprehensive post-migration audit should the old system be shut down to guarantee there are no errors with the data.

Simplify your database migration strategy with Sagacity

If you’re considering a data migration, we can handle the process for you. Our data experts can help ensure a smooth transition, navigating the complexities of the migration with confidence, reducing downtime, maintaining data quality, and completing your migration in a timely manner. 

Learn more about our data management solutions, or contact us today to see how we can help you.

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