The Data Migration Process Explained in 5 Easy Steps

If you’re planning a data migration but don’t know the first thing about it – you’ve come to the right place. Today, we’re breaking down what data migration is, the data migration process, and the key challenges you can encounter. Without further ado, let’s get into it.

What Is Data Migration?

Before we can break down the data migration process, it’s important to have a good grasp of the basics. So what is data migration?

Data migration is the process of moving data from one location to another or one system to another. It involves a change in storage and database application, which is why it often seems like a challenging process.

Data migration involves lots of preparation, as well as post-migration activities, which can include:

  • Planning
  • Creating backups
  • Doing quality testing
  • Validating results

The migration only ends when the old system, database, or environment has been shut down and all the data they had been storing is now moved. Often, data migration is part of much larger projects, which can cover:

  • Modernization or replacement of legacy software
  • Expanding systems or storage capacities
  • Introducing new systems to work alongside the existing application
  • Shifting to a centralized database to eliminate data silos
  • Moving IT structures to the cloud
  • Merger and acquisition activities, requiring consolidation to a new system

Data Migration vs. Data Conversion vs. Data Integration

Even though the terms data migration and data conversion are sometimes used interchangeably, they represent two very different things. As we mentioned, data migration refers to the process of moving data between locations, formats, or systems. It includes data profiling, cleansing, validation, as well as ongoing data quality assurance.

On the other hand, data conversation refers to the process of transforming data from one format to another. It’s often the first step in the complex data migration process. It’s a crucial step when moving data from a legacy application to an upgraded version or a new system altogether.

Types of Data Migration

There are countless business benefits to upgrading systems or extending a data center into the cloud. But for many organizations, it’s just the next natural step.

Companies use data migration to fuel top-line growth, reduce capital expenses, increase agility, and more. That said, the type of migration they have to undertake will depend on time and budgetary constraints. 

So let’s take a closer look at the types of migration.

Storage Migration

The process of moving data off existing arrays into more modern ones that enable other systems to access it is called storage migration. It offers much faster performance, as well as more cost-effective scaling. What’s more, storage migration enables expected data management features like cloning, backup, disaster recovery, and snapshots.

Cloud Migration

Cloud migration is the process of moving data, applications, or other business elements from either one cloud to another or from an on-premises data center to a cloud.

Application Migration

The process of moving an application from one environment to another is called an application migration. It can include moving the entire application from an on-premise IT center to a cloud. What’s more, an application migration can also include moving between clouds or moving an application’s underlying data to a new form of the application.

Why Having a Data Migration Strategy Is Important

Regardless of the exact purpose of the data migration, its goal is typically to improve competitiveness and performance.

A complete data migration strategy will prevent subpar experiences that could create more problems than it would solve. Not only that but it will minimize cost, data loss, and downtime. It will also maximize utility for the users of data systems that depend on the migration.

On the other hand, a poorly designed data migration can lead to business disruptions and or poor performance of the newly migrated system.

Data Migration Strategies

There is more than one way to build a data migration strategy and a business’s specific needs will help establish what the best one is. That said, most strategies fall into two categories: Big Bang and Trickle.

Big Bang Migration

In the Big Bang migration, the entire transfer is completed within a limited timeframe. Live systems will have to experience some downtime while data goes through ETL processing and transitions to the new base.

The downside of using Big Bang is that it needs to happen in a short period, as a one-time boxed event. It’s done in that way to ensure minimal downtime to end users. That’s why it can be pretty intense as the business will have to operate with one of its resources online. 

Trickle Migration

In contracts, the trickle migration takes part in phases rather than as a one-time event. During the implementation, both the old and the new systems run simultaneously, eliminating downtime and operational interruptions. What’s more, processes that are running in real-time can continue doing so while the data is migrating.

The drawback of the trickle migration is that it can be pretty complex and requires lots of stages. However, when done right, that added complexity will actually help reduce risks.

Best Practices for a Data Migration Process

It’s important to note that not every data migration process will follow the exact same pattern as it will depend on a business’s size, needs, and goals. But most follow a common, recognizable pattern.

  1. Exploring and Assessing the Source

Before you migrate any data, you have to have a firm grasp on exactly what you’re migrating and how it’s going to fit within the target system. What’s more, you should understand how much data you’re pulling over and what that data looks like.

At this step, you should ask yourself what you need to migrate over, what you can leave behind, and what might be missing.

You should also run an audit on the actual data that are contained within your data fields. If you’re seeing any poorly populated fields, inaccuracies, or incomplete data pieces, consider whether you really need to go through the migration.

Never underestimate the source review step as it can result in wasted time, money, and other resources. What’s more, your business could run into a critical flaw in the data mapping process that will stop any project in its tracks.

  1. Defining and Designing the Migration

The define and design phase is where the business will decide which strategy to take on, big bang or trickle. They’ll do so with the help of the data migration experts to ensure they have the best technical architecture and a detailed data migration process.

What’s more, at this stage, you will have to consider the design, the data you’re migrating, as well as the target system. Then, you can begin to define timelines and project concerns.

During this stage, it’s also crucial to go over any and all security plans for the data and consider what needs to be protected and how. By the end of this stage, the entire project should be well documented.

  1. Building the Migration Solution

Since you’re undergoing the implementation one at a time, it’s key that you have a good development approach. A common tactic includes breaking data into subsets and building out one category at a time, all followed by a test. If your organization is running a large migration, it might make sense to build and test simultaneously.

  1. Doing Live Tests

It’s important to test the data migration design with real data to ensure the accuracy of the implementation.

After the final testing, you can proceed with implementation, using the practices and strategies outlined in the plan. 

  1. Doing Audits

Once the tests have been done, and the implementation has gone live, it’s time to begin with the audits. It’s important to know that the data migration process isn’t over once the data has been transferred. Instead, it’s an ongoing process that ensures the accuracy and security of the migration.

The Challenges of Data Migration

Even though data migration is a common practice nowadays, often you will hear stories of big blunders, causing massive downtimes. So here are the most common challenges of data migration to be on the lookout for.

Not Contacting Key Stakeholders

It doesn’t matter how big or small the migration is; if you’re moving something, chances are, someone cares about that data. You need to know how those stakeholders are and explain the need for the project, as well as its impact. 

Not Communicating With the Business

Once the stakeholders are aware of the project, it’s important to keep the business in the loop when it comes to all progress. Regular communication will go a long way in building trust with business and stakeholders.

Cross-Object Dependencies

Oftentimes, cross-object dependencies are not discovered until very late into the data migration process. That’s why it’s important to have contingencies for them so the entire delivery date isn’t easily thrown off.

To Sum Up

There you have it – the data migration process explained. Data migration, no matter its size, is a complex process that requires lots of tools, strategies, and expertise. That’s why it’s always best left to the professionals.

If you need your data migrated – contact Coherence Inc. Using custom products and tools and years of expertise, we can ensure your migration goes off without a hitch.