Data migration is never as easy as it may sound. How does data migration work?
According to The Bloor Group, over 80% of data migrations are delayed or over budget. Cost overruns an average of roughly 30%. Time overruns at an average of 41%.
Gartner's research says that 83% of data migration projects fall short or exceed their budgets and timeframes.
Time. Money. Budget: three aspects that are typically interconnected when it comes to any kind of tech development. If one is affected, the rest follow in negation.
But who says it has to be that hard? Having as much knowledge as possible on the subject is the most effective strategy for affecting your business's optimization and development. In this case, we discover four different types of data migration: database, application, storage, and cloud migration.
Overview of Data Migration: What Is It?
Data migration meaning is moving all your stuff from one home to another, but instead of furniture, you’re transferring data between storage systems, different data formats, or applications. It’s the digital equivalent of packing, prepping, and making sure everything fits in the new space. Often, data migration happens when a company adopts new tech or processes that need an upgraded or different system.
Common reasons for data migration involve:
- Replacing, upgrading, or expanding storage systems
- Moving on from outdated software
- Shifting from local storage to cloud solutions for smoother operations
- Merging websites
- Introducing new systems alongside existing ones to enhance functionality
- Performing infrastructure maintenance
- Switching to centralized databases to improve interoperability
- Consolidating various information systems
- Relocating data centers
Data Migration vs. Data Integration
Migration requires moving data from one place to another. Integration, on the other hand, is more about connecting the dots—pulling together information from different sources (both inside and outside your company) to create one clear picture. Integration keeps everything working together, enabling cross-system data access, which is critical for thorough analysis and reporting. Unlike migration, which has a clear start and end, integration is ongoing, keeping the data flow alive and well.
Data Migration vs. Data Replication
When you migrate data, you're moving it to a new place and eventually leaving the old system behind. Replication, however, is like making regular copies of your data and keeping the original around. Replication doesn’t have a finish line and often supports integration by keeping data synced between locations. In some cases, replication can become migration—if you decide to retire the old system entirely.
Now, let’s stick with migration—a one-way trip, like moving to a new house and saying goodbye to the old one.
Why is data migration so risky and difficult?
Data migration is a tricky and risky process due to several factors. First, the complexity of moving large volumes of diverse data across systems, each with its own structure and format, makes it hard to ensure everything transfers smoothly. It is difficult to ensure that no data is lost or distorted during the migration, especially when there are complex data dependencies.
Data quality is another major issue. During migration, organizations often discover problems like outdated, inconsistent, or duplicated data that can wreak havoc if transferred to the new system. Cleaning up this data before migration is essential but time-consuming.
System compatibility further complicates things. The source and target systems often have different data structures or requirements, meaning the data must be transformed or adjusted to fit correctly. This adds an extra layer of complexity, increasing the chance of errors.
A single misstep in any of these areas can result in downtime, data loss, or even system failures, making data migration a high-stakes process that requires careful planning and execution. But when you choose one of the data migration types, you can avoid these headaches.
4 Types of Data Migration
1. Database Migration
Database migration involves transferring data between two database systems. It can affect the data language or protocol at the same time as the initial data shifts. The change is also expressed in the application layer. The migration of databases adjusts data without changing the structure.
Before you start Database Migration:
- Test database size
- Use the database test applications
- Assure the security of data within the database
- Check the migration process consistency
Database migration requires serious planning and data migration testing, as the process includes many small tasks, such as assessing target database storage space, reviewing applications, and maintaining data security.
2. Application Migration
This type of data migration happens when a company transfers from one framework to another system or vendor. Every application has a unique model of the data, and the programs aren't portable. In the development and implementation process, the operating systems, virtual machine settings, and management tools of each application can be different.
It’s imperative to ensure that data is communicable between the two software. Every application may have a specific data model, so attention must be undivided when planning the format of that data. After all, an application is just as strong as the data inside of it.
3. Storage Migration
The storage migration procedure involves moving data from one storage device, such as a hard drive or the cloud, to another. Put simply, data is transferred from one storage medium to another. During this method, it is simple to introduce data protection features such as data validation, cloning, and reduction of invalid or outdated information.
Technological updates are a great time to migrate data. As new technology becomes available, it may be attractive to move data to it. The attraction may be due to the flexibility, price, or experience of accessing and using the data in the new format.
4. Cloud Migration
Shifting into the cloud provides scalability, requires less storage space and is cost-effective. This makes cloud migration one of the newer trends in the data management industry. Properties, software, or services of an entity are migrated to the cloud. The firewall of the cloud protects the data that’s being migrated.
8 Basic Steps of Data Migration Process
No matter which path you take, every data migration project follows the same core data migration stages:
- Planning
- Data auditing and profiling
- Data backup
- Migration design
- Execution
- Data migration testing
- Post-migration audit
- Ongoing maintenance
Let's walk through each step to make sure your data gets to its new home without any headaches, delays, or a budget meltdown.
1. Planning
Data migration is no small task. The first step is carefully examining your data and crafting a solid migration plan. The planning phase itself has four important steps.
Step 1—Define the Scope
Focus on identifying the essential data needed to keep your systems running smoothly. This involves a high-level look at both your source and target systems, and you’ll want input from data users who will feel the impact of these changes.
Step 2 — Evaluate the Source and Target Systems
Your plan needs a clear understanding of how your current system operates and how it will work in the new environment. A solid assessment here ensures nothing is missed.
Step 3 — Set Data Standards
Establishing clear standards allows your team to pinpoint potential issues at each phase of the migration process and avoid nasty surprises once the migration is complete.
Step 4 — Set Budgets and Timelines
Once you’ve refined your scope and assessed systems, you’ll have a better idea of what approach to take—whether an all-at-once "big bang" migration or a phased "trickle" migration. From there, you can estimate resources, create a realistic budget, and set timelines.
2. Data Auditing and Profiling
Before moving your data, it’s crucial to give it a good once-over—this stage is all about checking and cleaning up the data set. The goal here is to catch any potential conflicts, fix data quality issues, and get rid of duplicates and other oddities before they sneak into the new system.
Now, let’s be real—data auditing and profiling can be a slog. It’s time-consuming, tedious, and can feel like a mountain of work. That’s why automation tools are your best friend for larger projects. Some of the go-to solutions include MigrateMyCRM, Open Studio, Data Ladder, SAS, Informatica, and IBM InfoSphere QualityStage, just to name a few.
3. Data Backup
While this step might not be technically mandatory, it’s one of those "better safe than sorry" situations. Data migration best practices strongly recommend creating a full backup of all the content you're about to move before you start the process.
Migrations, especially large or complex ones, are prone to unexpected hiccups, like data corruption, transfer errors, or system crashes. If something goes sideways and important data gets lost or scrambled, having a backup ready means you can quickly recover and avoid a disaster.
Without a backup, you could be left scrambling to fix things, leading to prolonged downtime or even permanent data loss. So, while backing up might add a little extra time upfront, it’s worth the peace of mind knowing you’ve got a failsafe if things take an unexpected turn.
4. Migration Design
The migration design phase is where you set the rules of the game. It’s about defining how migration will happen, what testing needs to be done, and who’s responsible for what on the migration team.
While there are many tech options for data migration, ETL (Extract, Transform, Load) is often the go-to choice. And if your project involves handling large volumes of data or complex data flows, it’s a good idea to hire an ETL specialist—someone who knows the ins and outs of the process. Whether it’s an ETL developer or a software engineer with deep ETL expertise, they’ll make sure everything runs smoothly.
During this phase, ETL developers or data engineers will write scripts or set up third-party ETL tools to manage the data transition. A key part of this is data mapping, which ideally involves not just the ETL expert, but also a system analyst (who understands both the source and target systems) and a business analyst (who knows the true value of the data being moved).
How long will this take? It depends. If you’re simply customizing pre-existing tools, it could take just a few weeks. But if you need to build scripts from scratch or acquire new software, you’re looking at a few months.
5. Execution
Now it’s showtime! This is where the migration happens—extracting, transforming, and loading data. If you’re doing a big bang migration, it’ll be over in a couple of days. If you’re opting for the phased, trickle approach, expect a longer process, but with the bonus of no downtime and minimal risk of big issues.
For phased migrations, ensure your regular system operations aren’t interrupted. And don’t forget communication! Your migration team should coordinate with business units to plan when each chunk of data will be moved and which users will be affected. Throughout the process, keep the focus on business goals and customer satisfaction.
6. Data Migration Testing
Testing isn’t just a one-and-done step—it’s woven throughout the entire migration journey, from design to execution and beyond. If you're using the trickle method, be sure to test each batch of data as it’s transferred so any problems can be immediately identified and resolved.
Consistent testing guarantees that your data arrives safely at its new home, maintains quality and meets all requirements when it enters the new system.
7. Post-Migration Audit
Before you roll out your freshly migrated data, validating the results with stakeholders is crucial. This step serves as your last reminder to make sure everything has been moved correctly and is operating as it should in the new setting. During the post-migration audit, you'll want to confirm that the data aligns with the business needs, is properly structured and that no information was lost or altered during the migration. Business users, particularly those who rely on the data daily, will play a vital role in reviewing the migrated data to catch any inconsistencies or issues.
Once you’ve received the green light from your audit and the business users are satisfied with the results, it’s finally time to retire the old system. Saying goodbye to the legacy system might feel like closing a chapter, but it’s also an exciting milestone—marking the official launch of your new system, ready to support the future of your organization with clean, accessible, and properly migrated data.
8. Ongoing Maintenance
Ongoing maintenance is key to ensuring your data stays in tip-top shape and that the new system runs like a well-oiled machine. This phase is all about monitoring the system, optimizing performance, and addressing any hiccups that pop up along the way.
You’ll need to keep an eye on data quality, run regular audits, and fix any emerging issues quickly. Maintenance also includes updating your migration tools or scripts as needed, and making sure your system scales smoothly with growing data demands. Think of it like routine car maintenance—you need to check the engine, rotate the tires, and get the oil changed to avoid any major breakdowns.
And don’t forget about your users! Regularly gathering feedback from them can help identify any problems or places where the system might work better. Staying proactive with maintenance means fewer disruptions, better performance, and happy business users all around!
Risks of Data Migration
Data migration is a complex process, and with complexity comes risk. Several potential issues can arise during the transfer from one system to another, each of which can impact the migration project's success and the target system's stability. Here are a few of the biggest risks:
Data Loss Risk
One of the most critical risks during migration is the potential for data loss. When transferring large volumes of information, it's easy for some data to be missed or improperly mapped to the new system. This can result in missing records, incomplete transactions, or even entire datasets disappearing. When sensitive or important data is lost, it can be hazardous because it may disrupt corporate operations and take a long time and a lot of resources to retrieve.
Semantics Risk
Data might not just get lost—it can also lose its meaning. The semantics risk occurs when data from the source system is migrated to the wrong field or column in the target system. Even a minor misalignment in where data is placed can create major headaches. For example, customer names might be moved to a column meant for addresses, or financial data could end up in fields that are read incorrectly. This misplacement can cause confusion, errors in reporting, and potentially lead to compliance issues if not caught early.
Extended Downtime Risk
Downtime is a real concern when migrating data. During the transfer process, the source system often becomes inactive, leading to interruptions in normal operations. For businesses that rely on real-time data or run around-the-clock services, even a brief outage can result in serious disruptions. The longer the downtime, the higher the risk of productivity loss, customer dissatisfaction, and financial setbacks. Extended downtimes also put pressure on IT teams to rush the process, which can increase the likelihood of mistakes.
Data Corruption Risk
When migrating data, there’s always the risk of introducing errors or “dirty data” into the new system. Redundant, obsolete, or incorrect data can slip through the cracks and enter the target platform, leading to data corruption. Corrupted data can cause crashes, glitches, or inaccurate analytics, making the new system unreliable and even unusable in some cases. Fixing data corruption post-migration can be costly and time-consuming, often requiring a complete rollback or major repair efforts.
Application Stability Risk
Migrating data to a new system isn’t just about transferring files—it’s about ensuring that the new platform can handle the data without breaking down. The stability of the target application is a critical concern. If the target system has been poorly developed, inadequately tested, or improperly aligned with business needs, it may crash under the strain of the new data load. Unstable applications can result in frequent errors, degraded performance, and constant troubleshooting, all of which undermine business operations.
Target Application Parameterization Risk
Target system configuration is another factor that can lead to post-migration instability. If the target system has weak controls or few limitations, data migration tools may inadvertently overload it, leading to bugs, inefficiencies, or errors. Poor parameterization can also cause issues with data compatibility or create conflicts within the system itself. This risk is especially high if the data migration involves large-scale changes, such as moving from a legacy system to a cloud-based environment. Properly setting parameters, thoroughly testing configurations, and conducting stress tests are essential steps to prevent instability.
Data Migration: The Point of Knowing Them
So, we discovered what is data migration process, and that every business or company structure is different. Even if it’s in the same industry, everyone likes to do things their own way. Depending on your technological medium, database, application, storage, or cloud migrations affect the outcome of your data’s integrity.
We like to mention the risks involved when it comes to data migration for one reason: it’s not easy. Most people try to do it themselves, conduct it in-house, or hire a freelancer. You can see why doing any of these options maximizes major, potential risk.
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