Data migration might seem straightforward, but it's actually a highly complex task. First off, it involves securely transferring data from its original sources to new systems. Then, the data must fit perfectly with the architecture and protocols of the new system. This step is often trickier than most organizations anticipate.

The challenge lies in keeping the data intact and functional once it transfers. Any disruption during this process can  lead to significant operational setbacks. It’s also why businesses must plan the data migration process carefully 

In this article, we'll break down what happens during data migration and why it matters. Getting a good grasp of how data migration works will enable you to effectively transfer and reuse your data in new environments.

What is data migration?

Data migration involves moving your data from one computer or storage setup to another. Businesses often need to transfer data between different systems to improve visualization, update technology, or move to cloud-based operations.  The data may be structured, semi-structured, or completely unstructured and could require significant transformation to ensure compatibility in its new environment. 

The primary objective of data migration is to execute this transfer securely and efficiently, minimizing disruptions to daily business operations. It's important to use a data migration tool specifically designed to support these goals by automating and optimizing the transfer process. These tools maintain data consistency by keeping data accurate and uniform across different systems throughout the migration.

Planning a data migration journey

Before starting a data migration project, businesses must get a good handle on their data environment. They need to evaluate their existing data to see how much there is, what kind it is, and how good the quality is. Understanding this data will shape their migration strategy, as it includes all important data and anticipates potential challenges. Additionally, it allows them to design a migration plan that minimizes disruptions and improves data transfer efficiency.

Next, businesses need to consider how data migration might impact their operations. They’ll need to identify who’s involved, from key stakeholders to experts, assign responsibilities, set budgets, define timelines, and figure out how everyone will stay in touch throughout the project. Understanding these elements helps a business effectively manage the migration from start to finish and tackle any challenges head-on before they become bigger issues.

Oldcastle Infrastructure's successful data migration to the cloud underscores the importance of thorough planning in such initiatives. The company made the effort to identify key stakeholders and meticulously map out every aspect of the migration—including timelines, responsibilities, and communication strategies—enabling it to seamlessly transition their scattered on-prem and cloud-based data systems into a unified platform. Their focus on preparation enabled them to optimize operations without major disruptions.

Implementing the data migration plan

Once businesses understand their project’s scope, they can proceed to the implementation phase. This phase involves selecting the most suitable data migration software and hardware, outlining detailed migration specifications, and deciding the pace of the data migration. That last point is critical — whether to transfer data all at once, in stages, or progressively. Make the wrong choice and you risk significant disruption. Consequently, many companies seek expert advice to scale their migration efforts.

A large part of this phase is dedicated to continuously monitoring and testing the data. The objective is to verify that it transfers correctly and remains free from conflicts, quality issues, duplicates, and anomalies. This evaluation is not intrusive —- it takes place in a staging environment that closely mirrors the production environment. As a result, data teams are able to quickly identify and resolve any issues that emerge.

Implementing a data migration plan may involve integrating Change Data Capture (CDC) technology. CDC continuously updates changes from your old system to the new one as they happen. This approach keeps everything running smoothly without having to shut down operations. It's especially handy for businesses moving to the cloud who can't afford downtime. Using CDC technology keeps data synchronization seamless and uninterrupted.

GuruShots serves as an excellent example. The photo-sharing platform adopted a CDC approach to seamlessly transition their MySQL data into a new system. The team leveraged CDC to effectively manage updates without disrupting their operations. 

Maintaining data integrity after data migration

Once all the data has been transferred and everything is set up, the data team conducts a thorough review of the new system. This involves using data validation tools to compare data sets between the old and new systems to ensure completeness and accuracy. They verify that every piece of data has moved over correctly and that it functions as expected within the new environment. This meticulous approach helps safeguard the integrity of the migrated data.

The data team may even take additional monitoring steps. Automated scripts may be run to detect any discrepancies or data integrity issues, such as missing or corrupt data. A diligent team will not decommission the old system until key stakeholders have reviewed and approved the migration results, confirming that everything meets the required standards.

Types of data migration

There are six distinct types of data migration, each designed to address specific aspects of updating or integrating IT systems within an organization.

1. Application migration 

The process of taking a software application, such as an ERP or CRM system, and transferring it to a different computing environment. A common example includes moving from an application from a local server to a cloud platform. During this process, the data often needs to be transformed to align with the new data model. A company will often make this shift after deciding to adopt a new application vendor, 

2. Cloud migration 

Moving data and applications from one computing environment to another, such as from on-premises servers to a public cloud or between different cloud platforms. A cloud migration strategy is commonly adopted for transferring large volumes of data, scaling operations, or shifting entire business processes to more flexible cloud environments. Doing so reduces IT cost, improves scalability, and increases security.

3. Storage migration 

Storage migration primarily involves relocating data from one storage medium to another, such as transitioning from analog tapes to modern solid-state drives. The objective is to leverage advanced storage technology for its improved performance and reliability. During this process, companies frequently seize the opportunity to streamline their data, verify its current and relevant, and discard any outdated information.

4. Database migration 

Database migration involves transferring data from one database system to another, often to improve performance and increase scalability. This process enables companies to optimize their operational efficiencies, manage large data volumes, or upgrade to more advanced technology. These newer database systems often feature superior indexing and query optimization that streamline data handling. 

5. Data center migration

Data center migration involves moving data processing operations from one data center to another. It often includes transferring physical and virtual servers, applications, and large volumes of data. Companies take this type of migration when they seek to upgrade their infrastructure, boost data security, and enhance operational efficiency. 

6. Business process migration

Business process migration involves transitioning business operations from one environment to another, often as part of a reorganization or broader digital transformation initiative. Companies pursue this type of migration to enhance process efficiency and cut costs. However, caution is warranted because sometimes it's challenging to replicate these processes in a new environment.

Data migration vs data conversion

Data conversion and data migration might sound similar, but they play quite different roles within data systems. Data conversion aims to change data from one format or structure to another. This is often necessary when a company needs to update from an outdated database format to a more efficient, modern one. It's a commonly accepted step in updating software or platforms, as it maintains the compatibility of existing data with the new system.  

Data migration is a more thorough process that entails moving data from one system to another, such as from an on-premises data center to the cloud or between various cloud platforms.. The goal is to relocate existing data to a new environment without any loss or degradation in quality. However, it’s much more comprehensive than just changing the format or structure of data. Instead, data migration focuses on strategies designed to smoothly integrate data into new applications and services.  

How Fivetran helps with your data migration requirements

Undertaking a large data migration process can be a daunting task for any organization. This is where Fivetran steps in, providing a seamless and efficient solution tailored to your unique needs. They offer a suite of robust, pre-built connectors that simplify the data migration process. Their platform is designed to support and streamline every step of your journey, whether you're consolidating disparate data sources or transitioning to a cloud-based architecture.  

To learn more about how Fivetran can help you modernize your application, visit this page. 

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