Creating A Data Migration Strategy: How To Plan To Migrate Your Data

Even though I work in the Data and Tech Industry, I don’t think I’m biased when say databases are the lifeline in every organization, big or small. We know that data stored in databases can really help business users make more educated decisions and because of this, data of the highest quality is required. This brings in a new challenge as technology constantly changes and sometimes a newer system is better than the one you currently have. So, when it comes time to upgrade your data storage from an older version to a newer version, data migration planning should be your first step.

In the data migration process, there are a lot of challenges. Unfortunately, it’s far more involved (and sometimes costly) than simply “copying and pasting data”. For the entire process, you’ll need to be well-versed in data migration techniques and best practices.

In order to get your data migrated and ready for the business user, you must design a data migration plan that specifies all of the process’s steps.

In this post, we’ll look at the many data migration options that can help you manage data better while you’re migrating from legacy systems or updating.

What is Data Migration?

The process of moving data from one location to another, from one format to another, or from one application to another is known as data migration. This is usually the outcome of introducing a new data storage system or location.

Usually the cause of data migration is due to legacy systems not providing organizations with what they need whether it be cost, storage space, performance time or ease of use.

Data migrations are becoming more common in today’s world as companies migrate from on-premises infrastructure and applications to cloud-based storage and apps in order to optimize or change their businesses.

This procedure is not as straightforward as it appears. Planning, making backups, quality testing, and validation of results are all part of the pre-migration and post-migration operations. Only when the old system, database, or environment is shut down does the migration finally come to a conclusion.

There are quite a few important variables to consider while migrating data, regardless of the type of migration you’re doing:

  • Impact of the Business (Pre and Post Data Migration)
  • Cost of the Data Migration Process
  • Quality of the Data
  • User Experience
  • Data Assessment
  • Potential Downtime (and risks associated to it)

What’s the difference between Data Migration and Data Integration?

Data migration and data integration are two different projects but definitely get confused.

When you perform data integration, you combine two separate data repositories into a single huge one. This is common in big data initiatives that need massive amounts of data available for various analytical tasks.

When you migrate data, you’re moving data from one source to another, and all of the data must be in the same format.

Types of data migration

Upgrading systems or extending a data centre into the cloud has a lot of commercial benefits. This is also a natural progression for many businesses.

Companies that use the cloud want to focus their employees on business priorities, boost top-line development, increase agility, cut capital expenditures, and pay only for what they need on demand. The type of migration, on the other hand, will decide how much time IT professionals can devote to other tasks.

Let’s focus on the different types of Data Migration.

  1. Database Migration

Databases are data storage media that are ordered and structured. Database management systems are used to keep track of databases.

As a result, database migration entails switching from one database management system to another or upgrading from one version of a database management system to the latest version of the same database management system. The first is more difficult, particularly if the source and target systems employ distinct data structures.

Database migrations are more complicated than storage migrations, owing to the fact that you’re working with larger volumes of data that may be formatted differently. Backup the databases, disconnect them from the engine, and move the files to a new engine. After that, you can restore the files to the new database and location.

  1. Storage Migration

When a company migrates data from one storage site to another, it is known as storage migration. It refers to the transfer of information from one digital layer to another. Upgrading data storage to a more advanced storage platform is a common reason for migration. Examples of this can include, it includes the transition from paper to cloud, from HDD to solid-state drives, and from hardware-based storage to cloud storage.

Certain tasks, such as data validation, cloning, data cleansing, and redundancy, can be performed during storage migration.

  1. Application Migration

When an organization changes its application software or its application provider, application migration occurs. This migration necessitates the transfer of information from one computing environment to another. Due to novel application interactions, a new application platform may necessitate drastic transformation.

The biggest problem stems from the fact that the old and target infrastructures use different data models and formats.

Vendors can provide application programming interfaces (APIs) to guarantee data integrity. Data migration can be aided by scripting vendor web interfaces.

  1. Cloud Migration

Moving data, applications, or other business pieces from an on-premises data centre to the cloud or from one cloud to another. It frequently includes a storage migration as well.

  1. Business Process Migration

Mergers and acquisitions, company rationalization, or reorganization drive this sort of migration to solve competitive difficulties or join new markets. All of these changes may necessitate the migration to the new environment of business applications and databases containing data on customers, goods, and operations.

  1. Data Centre migration

A data centre is a piece of physical infrastructure that allows businesses to keep their mission-critical programs and data safe.

Data centre migration can take several forms, ranging from the relocation of existing computers and cables to the movement of all digital assets, including data and business applications, to new servers and storage systems.

The Process of Data Strategy

To avoid going over budget or causing a lengthy process, the data migration strategy should be well-planned, frictionless, and efficient. In the planning, migration, and post-migration phases.

Data Migration Process

Data migration involves 3 basic steps:

  1. Extract data
  2. Transform data
  3. Load data

Stakeholders may become agitated when essential or sensitive data is moved or legacy systems are decommissioned. It’s critical to have a good plan in place, but you don’t have to reinvent the wheel.

Most Data Migration strategies fall into one of two categories: “big bang” or “trickle.”

The Big Bang Data Migration

In a big bang data migration, the entire transfer takes place in a short period of time. While data goes through ETL processing and moves to the new database, live systems incur downtime.

While data travels and undergoes changes to match the criteria of a target infrastructure, systems are offline and unavailable to users. Migration should be performed during a weekend or country holiday as users are unlikely to access the data then.

The big bang strategy allows you to finish migration in the shortest amount of time feasible, eliminating the burden of working on both old and new systems at the same time.

Advantages of the Big Bang Data Migration Strategy:

  • Less complicated
  • Costs less
  • Doesn’t take too much time as all changes happen quickly

Disadvantages of the Big Bang Data Migration Strategy:

  • High risk as everything is happening at once
  • Expensive if fails
  • Requires downtime

Small to medium enterprises that operate with small volumes of data will benefit from the big bang migration strategy. However for businesses or services which must be available 24 hours a day, seven days a week, this strategy may be ineffective.

Trickle data migration

In contrast to the big bang migrations, trickle migrations finish the migrating procedure in stages. The old and new systems are run in parallel throughout implementation, avoiding any downtime or operational disruptions. Processes that run in real-time can keep data traveling indefinitely.

This approach, often known as a phased or iterative migration, applies Agile principles to data transfer. It divides the process into sub-migrations, each with its own set of goals, dates, scope, and quality assurance checks. As a result, you benefit from zero downtime, and your customers benefit from the application’s availability 24 hours a day, seven days a week.

On the downside, the iterative technique takes much longer and adds to the project’s complexity.

Your migration team must keep track of which data has already been transferred and guarantee that users can get the information they need by switching between two systems.

Another option for trickle migration is to keep the old application up and running until the migration is complete. As a result, your clients will continue to utilize the old system until all data has been successfully loaded into the target environment, at which point they will transition to the new application.

When data is created or altered, engineers must ensure that it is synchronized in real-time between two platforms. This makes things quite hard for the engineers to keep up with.

Advantages of the Trickle Data Migration Strategy:

  • Less likely for unplanned failures
  • No shutdown or downtime needed

Disadvantages of the Big Bang Data Migration Strategy:

  • More costly to perform
  • Takes time
  • Additional technical resources are needed to keep two systems running

The trickle migration method is ideal for medium and big businesses that can’t afford long periods of downtime but have the resources to address technical difficulties.

A Seven Phase Checklist for Data Migration

Stakeholders may become agitated when essential or sensitive data is moved or legacy systems are decommissioned. It’s critical to have a good plan in place. I’ve provided you with some simples step to ensure you have a smooth migration of data.

Data Migration Checklist

  • Pre-migration planning: Technical leads should begin evaluating the data which needs to be moved for stability. During this phase, the business should elect a Project Manager or Migration lead.
  • Project initiation: The project manager should now identify and brief key stakeholders. This is also the chance where all stakeholders can discuss and establish a rough timeline, potential risks and desired goals.
  • Landscape analysis: The Data Migration team may now develop a robust data quality rules management procedure and inform the business about the project’s objectives, which include the shutdown of outdated systems.
  • Solution design: Determine which data to transfer and the quality of that data before and after the transfer.
  • Build and test: Create the migration logic and test it in a replica of the production environment.
  • Execute and Validate: Show that the migration met the requirements and that the data transferred is usable for business purposes.
  • Decommission & monitor: Old systems should be shut down and disposed of.

Conclusion:

Creating a successful data migration strategy is vital to prevent any data loss.

It will help you migrate your data safely and get you the desired results. Choosing a deployment model that aligns with business requirements is essential to make sure that any data migration is both smooth and successful and delivers business value in terms of performance, security, and ROI.

Want us to help you migrate your data? We do consulting work too!

Interested in more? Check out our product, Vantage Point. Vantage Point (VP) is a no-code, click & go business acceleration tool which enables data driven decisions across your business. It drives interactivity across all parts of your organization by communicating value (KPIs), autogenerating tasks with cutting-edge ML/AI technology and enabling users to combine VP’s ML/AI recommendations with their own analysis. You can finally track the exact ROI impact throughout your entire business with Vantage Point.

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Written by

Devasha Naidoo

Senior Technology Architect

Written by
Devasha Naidoo
Senior Technology Architect

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