Your legacy systems are holding back your growth, but data migration feels like pulling a thread: you never know what else might unravel..
What if something breaks? What if you lose years of customer records? What happens when you can't get your team the information they need to close deals or serve customers?
Here's what successful data leaders know: migration isn't just about moving files from point A to point B. It's your pathway to faster insights, better performance, and the ability to compete with AI-powered analytics that actually work at the speed of your business.
But before you start planning your migration, it helps to understand what it actually involves, and why it’s more than a technical checklist.
What is data migration?
Data migration is the process of moving data from one system, storage location, or format to another. Think of it as relocating your data from an old filing cabinet to a new, more advanced one.
This process goes beyond simply copying files. It involves careful planning, data reformatting, and validation to help your information arrive safely, accurately, and ready to use in its new environment.
According to a 2024 study from Foundry, 63% of IT leaders report that their organizations have accelerated cloud migration efforts, which shows just how important it is to get this process right.
Why data migration matters
Data migration isn’t just a backend task; it’s a business growth move. When you move from outdated platforms to cloud or hybrid environments, you gain scalability, lower maintenance costs, and a foundation for analytics and AI that move as fast as your business.
It also keeps your data consistent and accessible across every application, so you can make faster, more informed decisions. A successful migration strengthens your data governance and security, turning your data into a trusted asset you can build on.
Common challenges you'll face
Even with careful planning, data migration can present a few hurdles. You might face data quality issues like duplicates, inconsistencies, or missing values that can disrupt your new system's performance.
Gaps in planning or poor mapping between old and new systems often lead to data loss or corruption. Timing matters too. If your migration overlaps with daily operations, even a few hours of downtime can ripple across teams and customers.
And the risks aren’t theoretical. In one notable case, the UK Post Office abandoned a cloud migration project after spending £31 million (about $39 million). Without robust governance and testing, you risk operational setbacks and erosion of trust in your data.
Types of data migration
How you migrate depends on what you’re moving and why. The challenges you’ll face and the tools you’ll need depend on the type of migration you’re running. Here are the most common approaches:
Storage migration: Moving data between physical or cloud storage systems to improve performance, capacity, or cost efficiency
Database migration: Transferring data between database systems, like moving from Oracle to Snowflake, while preserving structure and relationships
Application migration: Shifting data to new software environments during upgrades or system replacements
Cloud migration: Moving data, applications, and workloads to public, private, or hybrid cloud platforms
Many organizations combine several of these types, which makes precise mapping, validation, and testing essential for a smooth transition.
Best practices for successful data migration
A successful data migration starts with structure and clarity.
Start with comprehensive planning that clearly defines scope, objectives, and timelines. Include stakeholders from IT, business, and compliance teams from the beginning. A detailed data audit helps identify quality issues early, so you only move clean, relevant information.
Testing and validation at every stage confirm your data remains accurate and your systems stay compatible. Automation platforms reduce manual errors, while solid backup and rollback plans provide safety nets against data loss.
💡Pro tip: Keep detailed documentation of each step and monitor system performance after go-live to maintain consistency and compliance.
How modern analytics supports your data migration
Modern analytics platforms are built to support data migration, not disrupt it. Unlike legacy BI tools that rely on static extracts and slow refresh cycles, cloud-native platforms connect directly to your data warehouse. That means your teams can maintain analytics continuity throughout the migration process, even as systems shift behind the scenes.
Take Odido, for example. Faced with a 120-terabyte on-premises data sprawl that slowed their cloud migration, Odido's business users waited weeks for answers. But once they moved data to AWS and gave everyone access to ThoughtSpot's search-driven analytics, the shift was immediate: questions that once took days are now solved in minutes, freeing up 40 days of labor each month and saving €1 million annually.
With ThoughtSpot, you can use natural language search to validate data accuracy in real time and compare legacy and new data sources side by side. That means you can migrate confidently while keeping business insights intact and accessible.
Spotter, your AI analyst, goes further by proactively surfacing insights and suggesting relevant questions during the migration process. You’ll spot potential issues early and check that your new environment delivers the same trusted insights your teams rely on.
Common use cases for data migration
Data migration underpins many major business initiatives, from modernizing operations to meeting regulatory demands. Here are some of the most common scenarios:
Cloud adoption: Moving from on-premises servers to cloud platforms for scalability and cost savings
System modernization: Replacing legacy ERP or CRM systems while preserving historical data
Mergers and acquisitions: Consolidating data from multiple entities into unified systems
Analytics modernization: Moving from traditional BI platforms to AI-powered platforms for faster insights
Compliance initiatives: Reorganizing data to meet evolving privacy standards like GDPR and CCPA
| Migration Type | Primary Goal | Common Challenges |
|---|---|---|
| Cloud migration | Scalability and cost reduction | Network bandwidth, security concerns |
| Database migration | Performance improvement | Schema compatibility, downtime |
| Application migration | Feature enhancement | Data format changes, user training |
Your data migration checklist
Use this checklist to guide your data migration project from start to finish:
Planning and preparation
Define objectives: Clearly outline why you're migrating and what success looks like
Assemble your team: Include people from your IT, business, and compliance teams
Choose migration type: Determine whether you need storage, database, application, or cloud migration
Set timeline and budget: Establish realistic expectations and contingency plans
Data assessment and cleanup
Audit your data: Identify sources, formats, and dependencies across systems
Clean and standardize: Remove duplicates, fix inconsistencies, and validate formats
Map data relationships: Document how information flows between old and new systems
Create backups: Establish rollback plans in case migration fails
Security and compliance
Encrypt data: Protect information both in transit and at rest
Verify compliance: Check for adherence to regulations like GDPR or CCPA
Control access: Limit who can access the migration environment
Enable monitoring: Set up audit trails for accountability
Testing and validation
Run pilot migrations: Test compatibility and performance with sample data
Validate accuracy: Confirm records are complete, accurate, and consistent
Test workflows: Confirm that business processes work with migrated data
Document results: Record findings and adjust parameters as needed
By following proven practices and using the right platforms, you minimize risks and work toward a smooth transition. This builds confidence not only in your technology but also in your ability to adapt and extract full value from your data assets.
As Pascale Hutz, American Express CDO, said in a recent Data Chief episode:
"Data has to be a living, breathing kind of organism. And when you have that mindset, you don't really think of data as done. Data's never finished."
Put your data migration strategy to work
Data migration helps your organization shift from legacy systems to agile, cloud-based platforms, putting accurate, secure data in the hands of the people who need it to make smarter decisions.
Once your data lives in a modern cloud platform, you can connect it to an analytics tool like ThoughtSpot and let everyone in your organization ask questions and get instant answers.
See how ThoughtSpot's AI-powered analytics can help you get more from your migrated data: Start your trial today!
Data migration frequently asked questions
What are the four main types of data migration?
The four primary types are
Storage migration: Moving between storage systems
Database migration: Transferring between database platforms
Application migration: Moving data to new software
Cloud migration: Relocating to cloud environments
Each serves different modernization goals, from improving performance to enabling advanced analytics.
How is data migration different from data integration?
Data migration is a one-time project to permanently move datasets from source to target systems, after which you typically retire the source. Data integration is an ongoing process that combines data from multiple active sources to create unified views for analytics and reporting.
How can you validate that migrated data matches the original source?
You can validate migrated data by comparing record counts between systems, performing checksums to verify content integrity, and running field-level comparisons. Testing business rules and conducting sample audits helps confirm your data behaves correctly in real-world scenarios before going live.
