Moving data from old systems to new ones sounds straightforward on paper. In reality, it’s one of the trickiest parts of any modernization project.
After helping dozens of companies through this process, we noticed the same questions coming up again and again. So, we put them all in one place. Hopefully, you’ll find the answer you’ve been looking for.
What exactly is data migration from legacy systems?
Data migration from legacy systems is the process of moving data from your old software to a new platform.
The goal is simple: get your data out of the old system and into the new one without losing information, breaking things, or grinding your business to a halt. But it’s more complicated than just copying and pasting data. Legacy systems often store information in formats that modern systems don’t recognize. Data might be duplicated, incomplete, or organized in ways that made sense years ago but don’t work now.
Is legacy system data migration complicated?
Yes, because legacy systems weren’t designed with migration in mind. When these systems were built, the focus was on solving immediate business problems. Nobody was thinking about moving this data to a completely different platform 15 years later.
The biggest issue is outdated technology. Your legacy system might be running on a database or programming language that modern tools don’t support well. Extracting data becomes a technical challenge because you’re working with formats and protocols that aren’t widely used anymore.
Then there’s data quality. After years of use, legacy systems accumulate bad data (duplicate records, incomplete fields, inconsistent formatting). You can’t just move this mess to a new system and hope it works. Legacy systems are also deeply integrated with other applications and databases. Moving data means untangling all of these connections without breaking anything.
The people who built the system might be long gone, with documentation either outdated or nonexistent. You’re left trying to reverse-engineer how everything works. And you can’t shut down operations for weeks while you migrate data. The business needs to keep running, which means you’re essentially renovating a plane while it’s flying.
What’s the difference between data migration and system integration?
Data migration is moving data from one system to another. It’s usually a one-time process with a clear endpoint. You’re transferring everything your business needs from the old system to work in the new one.
System integration is connecting systems so they can talk to each other on an ongoing basis. Instead of moving data once, you’re building bridges that let systems share information in real-time or near-real-time.
For example: If you’re replacing an old CRM with a modern one, the migration part involves moving all your customer records and sales data. The integration part involves connecting your new CRM to your email platform and accounting software so they can share data automatically. Sometimes you need both, especially with legacy system integration with cloud platforms – you move what you can while maintaining connections to on-premise systems that aren’t ready to migrate yet.
How long does legacy data migration take?
It depends on complexity. For a small business with straightforward data, migration might take a few weeks. For a large enterprise with decades of data and multiple interconnected systems, it can take months or over a year.
- Volume of data is a major factor. Moving a few gigabytes is different from moving terabytes.
- Data quality matters too: clean data migrates faster than messy data needs sorting first.
- System complexity plays a huge role. A simple database migration is faster than migrating from a custom-built system with dozens of integrations.
- Testing requirements extend timelines, especially in regulated industries.
- Business constraints matter – if you can only work during off-hours, the project takes longer.
A realistic timeline for a mid-sized company might look like two to three months for planning and assessment, three to six months for migration and testing, and another month or two for final validation. The key is to be realistic about how long it will take, and count in extra time for emergencies. Rushing to hit arbitrary deadlines can backfire.
What are the biggest risks in data migration from legacy systems?
Data loss is the nightmare scenario #1. Data gets lost during transfer without adequate backups. This is why backups and validation are non-negotiable. Sometimes data transfers without errors but gets corrupted in the process(numbers change, dates shift, relationships between records break).
Also, if migration requires taking systems offline, you’re looking at potential revenue loss and operational disruption. Sometimes the migration technically succeeds, but the new system runs slowly because data wasn’t optimized properly. Other systems that depended on the legacy platform might stop working correctly because data formats or APIs changed.
Next: in regulated industries, improper handling of data during migration can lead to compliance issues and huge fines, especially with personal data under regulations like GDPR. Projects often run over budget when teams underestimate complexity or don’t plan for data quality issues. A migration estimated at three months can easily stretch to six if problems aren’t identified early.
Most risks can be managed with proper planning and realistic timelines.
What is SAP legacy data migration, and why is it different?
SAP systems are enterprise resource planning platforms that run core business operations. They handle everything from finance and HR to supply chain and manufacturing. SAP legacy data migration usually happens when companies upgrade from an old SAP version to a newer one, replace SAP with a different ERP, consolidate multiple SAP instances after a merger, or integrate SAP data with cloud platforms.
SAP migration is different because of extreme complexity. SAP systems are massive platforms with thousands of tables and complex relationships. A single customer record might touch dozens of different modules. Most companies heavily customize their installations, affecting how data is structured and what it means. They often contain decades of transactional data: millions or billions of records.
SAP usually runs mission-critical operations, so you can’t afford downtime or errors. And it has its own technical ecosystem. You need people who understand SAP’s data structures, know how to use SAP migration tools, and can navigate the platform’s complexity. Such migrations usually require both SAP experts and data migration specialists to work together.
How do you migrate data from one database to another?
Migration of data from one database to another is one of the most common types of data migration. Start by understanding both databases – the structure of your source and the requirements of your target. These might not match up perfectly, so you need to map how data will translate from one to the other.
Before you move anything, assess data quality. Look for duplicate records, missing values, and inconsistent formats. Address these before migration, not after. Design your transformation logic to figure out how to convert data formats. Choose your migration approach (big bang where you move everything at once, phased where you move data in chunks, or hybrid where you keep both databases running temporarily).
Extract the data from the source database, transform it by applying all the conversions you designed, then load the transformed data into the target database in the right order to maintain relationships. Validate everything by checking record counts, running sample queries, and verifying that relationships are intact. Finally, have actual users test to make sure everything functions correctly.
Important: keep your source database running and backed up until you’re absolutely certain the migration succeeded. It’s your safety net.
What does legacy application modernization have to do with data migration?
Legacy application modernization is the broader project that often includes data migration as one component. When companies modernize legacy applications, they’re usually rehosting, replatforming, refactoring, rebuilding, or replacing the application. In most scenarios, you need to migrate data.
Data structure might change in the process. Modern applications often use different data models than legacy systems. A monolithic application might have stored everything in a single database. A modern microservices architecture might split that data across multiple specialized databases. New capabilities require data transformation. If you’re modernizing to take advantage of features like AI-driven analytics, your data might need to be restructured or enriched.
When moving legacy systems to the cloud, data migration is often the most complex and time-consuming part. You can’t properly test a modernized application without migrating at least some production data. The key is to think about data migration as part of your overall modernization strategy, not as a separate afterthought.
How do you integrate legacy systems with the cloud?
Legacy system integration with cloud platforms is becoming increasingly common as companies move to hybrid environments where some systems run on-premise and others in the cloud. This is different from a full migration, you’re connecting the legacy system to cloud services so they can work together.
API integration creates interfaces that allow your legacy system to send and receive data from cloud applications. Middleware solutions like MuleSoft or Dell Boomi sit between your legacy system and cloud services, handling data transformation and routing. Database replication sets up regular syncing where data from your legacy database is copied to a cloud database. Systems can also communicate by sending messages through a queue.
The challenges include security concerns when opening up legacy systems to communicate with external cloud services. If your legacy system needs real-time data from the cloud, network latency can be a problem. Legacy systems often use communication protocols that cloud services don’t support natively, requiring adapters or middleware. And if the connection goes down, you need fallback mechanisms.
Many companies use integration as a stepping stone toward full migration. They connect legacy systems to the cloud, move some functionality gradually, and eventually retire the legacy system entirely.
What are the steps to complete a data migration successfully?
Here’s a practical process for completing data migration from legacy systems:
Step 1: Planning. Document your current system, the data it contains, and what success looks like. Inventory all data sources, identify what needs to migrate, define success criteria, assess data quality, identify stakeholders, and create a realistic timeline and budget.
Step 2: Choosing migration strategy. Decide between big bang (move everything at once), phased (migrate in stages), parallel running (keep both systems active), or hybrid approaches. Your choice depends on how critical the system is, how much downtime you can tolerate, and complexity.
Step 3: Data mapping. Create detailed documentation showing how data in the source system maps to the target. Map each table or data structure, define transformation rules, document what data will be cleaned, identify dependencies, and plan how to handle data that doesn’t fit the new structure.
Step 4: Organizing your data. Before you migrate, fix what’s broken. Remove or merge duplicates, fill in missing values, standardize formats, correct errors, and archive data that doesn’t need to migrate. This takes longer than most people expect, but migrating dirty data just moves the problem to a new system.
Step 5: Test migration. Never migrate to production without testing first. Run your migration in a test environment, verify data landed correctly, check record counts, test relationships, have business users validate, and measure timing. If you find issues, fix them and test again.
Step 6: The actual migration. Follow the exact process you tested. Monitor progress continuously, document issues, keep stakeholders updated, and be ready to execute your rollback plan if things go wrong. Schedule during low-traffic periods and back up everything.
Step 7: Validating results. Immediately after migration, run automated validation checks, compare record counts and metrics, have business users test critical workflows, check that integrations still work, and monitor system performance under real load.
Step 8: Support. Provide support for users adjusting to the new system, monitor for issues that only show up with real-world use, address data discrepancies, optimize performance, and document lessons learned.
Don’t skip any steps, especially testing. You cannot over-test a data migration.
How do you maintain data quality during migration?
Data quality is one of the biggest challenges in legacy data migration. Your new system is only as good as the data you put into it.
Profile your data early by analyzing your source data to understand what you’re working with. Look for patterns, identify problems, and quantify data quality issues. Establish data quality rules that define what “good data” looks like. For example, email addresses must contain an @ symbol, customer records must have valid postal codes, order dates can’t be in the future.
Clean data at the source when possible. It’s often easier to fix problems in the legacy system before migration. Use transformation to improve quality during the migration process itself—standardize formats, deduplicate records, enrich incomplete data. Validate continuously throughout every stage. Don’t wait until the end to check quality.
Involve business users because they know what the data should mean and can spot problems that automated checks might miss. Plan for exceptions—decide in advance how you’ll handle data that doesn’t fit your rules. Document everything and accept that perfect isn’t realistic. Set realistic quality targets and focus on the data that matters most to business operations.
Should you migrate all your data or just some of it?
The answer is almost never “migrate everything.” Legacy systems accumulate huge amounts of data over years. Much of it is historical information nobody accesses anymore. Migrating every single record from the past twenty years is expensive, time-consuming, and often unnecessary.
What needs to migrate:
- Active transactional data (current customers, open orders, active projects)
- Recent historical data (typically 2-5 years for reporting and analysis)
- Master data (customer lists, product catalogs, employee records)
- Legally required data (based on regulatory retention requirements – some user data should be stored for 10 years, in banking for instance)
- Reference data (codes, categories, lookup tables that give context)
What you might not need:
- Ancient historical data that nobody looks at (archive instead)
- Temporary or working data (draft records, cache data)
- Duplicate data (migrate the authoritative version only)
- Obsolete data (old product codes, closed accounts with no future relevance)
The strategy that works: migrate what you need for operations and recent reporting, archive everything else in a read-only format that can be accessed if needed, and discard what has no value. This speeds up migration, reduces complexity, and starts your new system with cleaner data.
Make sure you understand your retention requirements before deciding what not to migrate. The last thing you want is to discover years later that you needed data you didn’t migrate and can no longer access.
What happens if the data migration fails?
Migrations fail more often than anyone likes to admit. Sometimes it means delays or budget overruns. Sometimes data is wrong or incomplete. In the worst cases, it means data loss or systems that don’t work.
If you detect failure during testing, that’s the best scenario. You discover problems before touching production. The impact is limited to delays while you fix issues. If issues appear immediately after go-live, you might be able to revert to the legacy system if you have a good rollback plan. Problems discovered days or weeks later are worse—by then you might have already decommissioned the legacy system or overwritten data.
When migration fails, execute your rollback plan if you have one. Assess the damage to figure out exactly what went wrong. Recover what you can using backups. Communicate transparently with stakeholders about what happened and what you’re doing to fix it. Fix the root cause before attempting another migration, and test more thoroughly.
The best way to handle migration failure is to prevent it. Maintain comprehensive backups of everything. Test exhaustively before go-live. Have a detailed rollback plan. Keep the legacy system running until you’re absolutely certain the migration succeeded.
Many “failed” migrations could have succeeded if teams had taken more time for planning and testing. The pressure to meet deadlines causes companies to cut corners, and that’s when failures happen.
Final Thoughts
Data migration from legacy systems looks deceptively simple on the surface. How hard can it be to move data from point A to point B? Turns out, pretty hard.
But it’s also doable. Companies successfully migrate data from legacy systems every day. The difference between success and failure usually comes down to planning, realistic timelines, thorough testing, and being honest about complexity.
If you ever need help with safely transferring your data, we’re here to help.
