Understanding Data Migration Strategies

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If you are like most businesses, you rely on big data to drive day-to-day tasks that impact your bottom line. Data enables you to make informed decisions. It helps you to deduce insights, notice patterns, and predict outcomes. In short, databases are the lifeblood of your modern business.

Therefore, data migration needs to be a well-established seamless process. To achieve flawless data migration, you have to understand data migration strategies.  In this post, we will look at what data migration is and define the different types of data migration strategies.

Let’s jump in.

What Is Data Migration?

Data migration is the process of transferring historical data to a new file format, storage system, or database. However, data migration involves more than just moving data from one database or system to another. It encompasses several intricate tasks such as reformatting and mapping, as well as pre and post-migration steps such as planning, testing, creating backups, quality testing, and validating outcomes, just to mention a few.

For this reason, you should not rush over the data migration process. Understanding data migration strategies and knowing the best practices to keep in mind is vital for data migration success.

Depending on the capability of your legacy system, the amount of data you have, and the level of compatibility with the new system, it might take you between a few months to a year to complete a data migration process.  The process is only deemed complete after you have shut down the old system or database.

Most of the time, you will find that data migration is part of a larger project such as:

·   Expanding your storage or system capacity

·   Moving your IT infrastructure to the cloud

·   Replacing or Modernizing your legacy software

·   Consolidating your IT systems after a merger or acquisition

·   Introducing an additional system that will work alongside your existing applications

Sometimes, people confuse data migration with other processes like data integration and data replication since they also involve large data movements. To avoid any confusion, let's define the two terms before we move any further.

Data Migration Vs Data Integration

Data integration is the process of consolidating data from many sources both outside and inside the company into a single view.  Data integration is a vital element of a company’s data management strategy. It enhances connectivity between systems and allows access to content across a variety of subjects.

A consolidated database is vital for making accurate analyses, reporting, and extracting insights.

Data integration differs from data migration in that data migration is a one-way movement of data from the source system to the target system. The process ends when all the data has been successfully moved to the new system. Data integration on the other hand is a continuous process, often involving streaming live data and sharing the information across systems.

Data Migration Vs Data Replication

Data replication involves the transfer of data from the source system to the target system. However, unlike in data migration, the source system is not destroyed after the data transfer.  Data replication can sometimes form part of data integration. It can also become data migration if the source storage or system is destroyed after the data transfer.

Now that you understand what data migration is and how it is different from other data management strategies that involve data movement, let us look at the different types of data migration.

Main Types of Data Migration

There are six main types of data migration: storage migration, database migration, application migration, data center migration, business process migration, and cloud migration.

However, the division between these six types of data migrations is not strict. For instance, one type of data transfer may involve both database migration and application migration.

Storage Migration

Storage migration usually happens when an organization wants to change from out-of-date equipment to modern ones.  Storage migration involves moving data from one physical medium to another, or from a physical to a virtual environment.

The reason for storage data migration is usually an urgent need for a technology upgrade as opposed to a need for more space. If your business has a large-scale system. Storage migration is a process that can take years to complete.

Here are a few examples of storage data migration:

·   Moving data from paper to digital documents

·   Moving data from mainframe computers to cloud storage

·   Moving data from hard disk drives to solid state drives

Database Migration

Databases are used to store data and provide a structure for organizing information in a specific way. A database is typically controlled through a database management system.

So what is database migration? Well, database migration can be either homogenous or heterogeneous.  A homogenous database migration involves upgrading to the latest version of the database management system you are already using. A heterogeneous database migration, on the other hand, involves switching to a new database management system such as moving from Oracle to MSSQL.

A heterogeneous database migration is more challenging than a homogenous one, especially if the database management system you are currently using supports different file formats than the one you are switching to. The task might become even more challenging if you have to conduct a database transfer from a legacy database like IMS, Adaba, or IDMS.

Application Migration

Application migration involves moving data from one computing environment to another. This usually happens when your organization needs to switch from one enterprise software to another. This type of data movement is usually challenging, especially when the new and old infrastructures have different data models and support different data formats.

Data Center Migration

Organizations rely on data centers to keep critical applications and data. A data center migration can mean different things depending on the organization. For instance, it can refer to the relocation of business data and applications to a new server or storage or the relocation of digital assets and computers to other premises.

Business Process Migration

This type of data migration happens after a merger or acquisition, reorganization, or business optimization. It is used to address challenges or to enter new markets. Business process migration usually involves database migrations and application databases to new environments.

Cloud Migration

Cloud migration is any data migration that involves moving data from on0premises data solutions to cloud-based ones or between two different cloud environments. Depending on how much data you are moving, cloud migration can take anywhere from a few minutes to years.

Data Migration Strategies

There are different ways of building a data migration strategy. Which way you settle on will depend largely on your organization's specific needs and requirements. However, data migration strategies will mostly fall under two categories Big Bang, or trickle.

Big Bang Migration

A big bang migration is a data migration strategy that is carried out within a short period. Live systems will usually experience downtimes as data is going through ETL processing and transitioning to the new database.

The main advantage of the big bangs data migration strategy is that it requires a short amount of time to complete. However, the process is very demanding and stressful and there is always the risk of a compromised implementation as the business has to operate within one of its offline resources.

If you consider using the big bang data migration strategy in your business, you must run through the migration process before the actual event.

Trickle Migration

In contrast to the big bang strategy, the trickle data migration strategy involves completing the migration process in phases.

When you use the trickle data migration strategy, you have to run both the old and new systems in parallel. As such, you eliminate downtime and prevent operational interruptions. Having your processes running continually ensures that data is continually migrating.

Although they reduce downtime, trickle data migrations are more complex than big bang migrations, however, they drastically reduce risks, so the added complexity is worth it.

So far, we have discussed what data migration is, the different data migration strategies, and the types of data migration. Now, let's look at how you can create a data migration strategy that will work for your organization

Key Steps in Creating a Data Migration Strategy

1.   Assess the source

To migrate data effectively, you must understand what you're migrating and how it will fit into the new system. How much data are you pulling over? What does that data look like? Are there any missing data fields within the source that you need to fill out? Is the data even worth moving to the new system?

All of these are questions you should answer at the very beginning of your data migration process.  Skipping this step could lead to wasted time and money or you might run into a critical flaw in the data mapping process that could stall the data migration process.

2.   Define and design the migration

This is the stage where you decide on the type of migration you should take on. This is the time to determine the technical architecture of the solution and detail the migration process. 

This is also the stage where you begin to define timelines and any project concerns. Keep in mind that some of your data might need a security plan when migrating. Include the measure to be taken during migration in the documentation for the migration plan.

3.   Build the migration solution

In this step, it is advisable to break down the data into subsets and build out each category one by one. After each category, do some testing before moving to the next. If you intend on doing a massive migration, consider building and testing in parallel to save time.

4.   Conduct a live test

Even after testing the code in the building stage, you must repeat the test with real data to ensure that the implantation and completeness of the application is accurate.

5.   Flip the switch

Once you have done the final testing, it's time to implement according to your plan.

6.   Audit

After the implementation goes live, create a system to audit the data and ensure that the migration was accurate.

Final Word

Data migrations are big and important projects. They should be planned for in advance and conducted with care and preciseness. The tips we have discussed here will help you choose the right data migration strategy for your organization, and implement it seamlessly. Remember, proper planning is the key to ensuring that your data migration goes according to plan, stays within budget, and is completed on time.

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