The Basics of Data Migration: Definition, Types, and Approaches

The benefits of data migration are apparent. Whether you seek comprehensive data integrity, reduced storage costs, or to minimize disruptions to daily business processes, migration can be one way to achieve your goals. Data migration is also crucial in upgrading underlying applications and services while boosting efficiency and can help you scale resources to meet growing business needs.

Unfortunately, the migration process isn’t often a straightforward path. Poor knowledge of source data, data cleaning, and different source systems all present sizeable challenges. Dealing with unique data and different codes are other significant challenges.

Indeed, recent studies show that 55% of all migration efforts are stretched beyond budget, with 66% either proving extremely difficult to complete or failing altogether – stats that will concern even the most experienced IT department.

Fortunately, data migration doesn’t have to be complicated. If you’re thinking about moving your data soon, the following are some of the critical points to keep in mind.

When considering data migration, it’s essential to acknowledge the potential complexities that may arise along the journey. From navigating through poorly documented source data to grappling with diverse source systems, each step demands meticulous attention.

Moreover, the nuances of unique data structures and varying coding standards add layers of intricacy to the process, amplifying the challenges at hand.

However, amidst these hurdles lies an opportunity to not only transfer data but also to enhance its value. Through strategic data enrichment initiatives, organizations can imbue their datasets with additional insights and context that enrich business data.

By integrating external sources, refining existing records, and standardizing formats, businesses can unlock hidden potential within their data, paving the way for informed decision-making and competitive advantage in the ever-evolving landscape of today’s digital economy.

What is Data Migration?

Data migration is precisely what the name suggests – transferring existing data to a new storage, system, or file format.

The migration process often comes as part of a larger project, such as legacy system modernization or replacement, the expansion of a storage or system capacity, or the introduction of an additional system to work alongside an existing application. You may also need to migrate some/all data when moving company infrastructure to the cloud or during a merger.

The process is often confused with two other critical data movement processes, i.e., data integration and data replication. It’s essential to understand the difference between these three processes.

Data integration seeks to merge data from multiple sources, inside or outside the organization, into a single view. It’s a one-way journey that’s complete once the information from the various sources is merged at the desired single location. By contrast, data migration may involve moving data to multiple locations (rather than a single).

Meanwhile, replication refers to creating one or more redundant copies of the same data (or an entire database) for fault tolerance. So, the main distinction between migration and replication is that the old location’s data is eventually abandoned in migration. In replication, the old data is still critical to your overall goals.

Types of Data Migration

There are six main types of data migration, though some migration processes may overlap two or more categories.

• Storage Migration 

Storage migration occurs when an organization transfers data from one physical medium to another or from one physical location to another. For instance, you can transfer your data from paper to digital, hard disk drives to solid-state drives, or even mainframe computers to the cloud. All these qualify as storage migration.

• Database migration 

Database migration refers to moving data from one or more source databases to one or more target databases. When the process is complete, all the data resides in the new/target database. Examples of database migrations include upgrading to the latest DBMS version or switching to a new DBMS provider, such as MySQL to PostgreSQL. The first is called a homogenous migration, while the latter is a heterogeneous migration.

• Datacenter migration

This is the process of moving an entire data center from one environment to another. It’s important to remember that the term “data center” refers to the whole room of servers, networks, switches, and other IT equipment. Therefore, data center migration means that you’re mobbing all this hardware. The destination can be a new physical location or computing environment.

• Application migration 

Application migration is the process of moving a software application from one computing environment to another. For instance, an organization can move one or more applications from the on-premise computing system to a cloud platform. You can also move applications from a public to a private cloud platform.

• Business process migration 

Business process migration is the complex transfer of applications, databases, and sometimes even data centers containing information about customers, products, and operations to a new environment. It’s the most complex of the six migration processes. For instance, Sabre, the second-largest global distribution system, has been moving its data and software from mainframe to virtual computers for over a decade.

• Cloud migration 

Finally, most people don’t list cloud migration as one of the data migration types because all the other five migration processes today involve cloud migration to an extent.

Two Ways to Approach the Migration Process

There are two broad approaches to data migration – the big bang approach and the trickle-down approach.

Approach #1: The Big Bang Approach

Advantages

• Less costly
• Takes less time
• Less complex
• All changes happen at once

Downsides

• High risk of failure
• Significant downtime

If the budget allows and you’re prepared to take the risk, the big bang approach to data migration is a very attractive idea.

The process involves moving all data sets from the source to the target environment in one operation. The migration can be executed on a weekend or legal holiday when customers probably don’t use the application.

However, it comes with substantial risks. For one, even moderate organizations store vast amounts of data these days. Migrating all that data in a single day or over a weekend isn’t an easy task. Additionally, you’ll likely be down and unavailable for the period of the migration.

Approach #2: Trickle Data Migration

Advantages

• Less prone to failures
• Zero downtime required

Downsides

• More expensive in the long run
• It takes more time to complete
• Needs extra time and resources

If you can’t afford downtime or the resources for a big bang migration, the trickle data approach offers another route to your migration goals.

Also known as phased migration or iterative migration, trickle-down data brings agile techniques to the data migration process. The migration process is divided into sub-processes, each with its goals, timelines, scope, and quality checks.

Data is essentially migrated in small increments while the old and new systems run parallel to each other. As a result, there’s zero downtime, and your services remain available 24/7.

Which is the Better Approach?

It depends on the specific organization and your migration goals. Smaller migration projects, such as moving one or two applications at a small-to-medium-sized organization to the cloud, can be completed in one go. But the same project may require the iterative approach at a global organization. To find out more, consult the experts at NIX Solutions.