50% of enterprises plan to invest $500,000 or more in data integration in the next year. It’s a figure that reflects just how high the stakes are when moving data, whether it’s to new environments or between systems.

With triggers like shifting from on-prem to cloud warehouses mixed with complications like enterprise scale or legacy systems, data migrations take more than a simple one-off process.

In this guide, you’ll learn more about the data migration process, including how to conduct it successfully, best practices to keep in mind, and industry examples of migration projects done well.

What is data migration?

Data migration refers to the process of moving data from one environment, system, or platform to another. While this can be a relatively simple process with small amounts of data and a transition from one platform to another, enterprise-level migrations are more complex.

They can involve terabytes of data, including a mix of structured and unstructured data. The project could entail a hybrid scenario combining on-premise and cloud environments. Add in strict regulatory requirements, and the downtime and quality issues from poorly managing data migration can directly impact revenue, compliance, and strategic initiatives such as AI projects.

There are many potential triggers for data migration in an enterprise, including:

• The need to support advanced analytics for better, faster business decisions
• Mergers and acquisitions
• Upgrading to new platform versions or switching vendors
• The need for scalability or lower operating costs

On the engineer side, these scenarios can lead to:

• Moving from legacy databases to a cloud warehouse
• Consolidating multiple ERP and CRMs into a single source
• Adopting a hybrid or multi-cloud strategy
• Transitioning from on-prem NAS or SAN systems to cloud object storage

Within these processes, teams must account for everything from schema mapping and change data capture (CDC) to maintaining sync during cutover. If mistakes occur, every step has the potential for costly rework and downtime.

Why data migration is essential for modern enterprises

Data migration is central to enterprise strategies and underpins nearly every modern data-based initiative. For example, digital transformation across the organization relies on consolidated and accessible data rather than remaining in silos in legacy tools. When implementing cloud adoption, teams must move workloads from on-prem environments into cloud infrastructure.

AI-readiness is another good example of the importance of data migration. AI models need unified, high-quality data for accurate training and model fine-tuning. In fact, 42% of enterprises say more than half of their AI projects have seen delays or underperformance as a direct result of poor data readiness, while 29% say data silos are blocking AI success.

Organizations also increasingly rely on business intelligence technology to remain competitive. This technology requires clean, unified data for reliable decision-making. For example, 68% of businesses in one survey said they rely on 50 or more data sources to support decision-making.

Poor data quality in any of these scenarios can drastically increase costs. Engineering overhead may be spent on time-consuming, manual pipeline builds and fixes. When the data does make its way into environments and business tools, poor quality can directly lead to misinformed decisions and impact organizational efficiency or revenue. One Fivetran report found that poor data quality can cost companies up to 6% of their annual revenue.

Types of data migration

Different triggers require different types of data migration projects, and each carries its own risks and technical requirements. The main types are:

• Storage migration
• Database migration
• Application migration

Storage migration means moving data from one physical storage environment to a new one. This can include upgrading from hard drives to SSDs or moving from an on-prem server to cloud storage.

Since the data format usually stays the same during storage migration, the primary considerations are speed and reliability, along with minimal disruption for business users through processes like retry logic and high-throughput transfer.

Database migration typically requires moving data between database systems. It can also happen when upgrading database versions. Let’s say the company is moving from MySQL to PostgreSQL, for example, or from a legacy DBMS to a modern, cloud-based system.

In these scenarios, data integrity is key. So, teams will focus on schema compatibility and data integrity. Processes may include transformation logic and reconciling data types, or CDC for systems involving a high volume of transactions.

Finally, application migration means moving data between applications and platforms, such as transitioning CRMs from Salesforce to HubSpot or moving from an on-prem ERP to a SaaS application. Since these projects usually require changing data formats and structure, mapping data fields and preserving relationships are critical to ensuring full functionality on the new application.

Key challenges of data migration

Even the best-laid data migration plans can encounter obstacles. Complex data and large sets across many systems leave plenty of opportunity for risk.

Data silos

Data fragmentation across enterprise systems is a common scenario. Different business units adopt their own SaaS applications over time, or data silos between on-prem systems occur. When marketing is relying on one platform and finance on another, for example, schema and governance models can differ significantly.

These silos become a challenge during migration, as different schemas and governance models mean that data unification requires careful planning. Risks like pipeline duplication can lead to time-consuming reworks.

Legacy schemas and formats

Data migration from legacy systems often involves schemas and formats that do not align with modern cloud platforms. Things like fixed-width files and non-UTF encoding require extensive recoding to ensure data is clean before loading into new environments.

When these systems have been in place for a long time, engineers may have to tackle technical debt from patchwork modifications and hidden dependencies or risk loading poor-quality data into new systems.

Downtime risks

Minimizing downtime is a key objective during the migration process, especially for mission-critical systems. Taking an ERP platform offline at the wrong time can delay payroll, for example, or an outage on customer-facing systems may impact revenue and trust.

For this reason, many enterprise migration projects need to perform cutovers in phases with careful planning. Parallel runs or CDC processes help to keep systems in sync and maintain continuity.

Engineering overhead and manual pipelines

Relying on hand-coded scripts or an individual ETL process means engineers spend large amounts of time and resources maintaining fragile workflows. Manual scheduling and unreliable connectors then lead to a higher risk of failure when a source schema or API changes.

At scale, this level of overhead compounds into cycles of rebuilds unless teams build automation into the data migration process from the start. Data engineers in this situation spend 44% of their time manually building and maintaining pipelines, which costs over half a million dollars annually.

Governance and compliance

Moving the data itself is just one consideration during migration. If the migration involves moving sensitive data between systems or business units, regulatory controls can come into play. Data migrations must ensure compliance with the GDPR, HIPAA, or other regulations.

This can mean maintaining encryption, both in transit and at rest, and ensuring audit trails are in place. Failure to adhere to compliance here can expose the company to audits and fines, so it’s no surprise that 64% of CIOs have delayed innovation efforts due to compliance concerns.

How to perform a successful data migration

Conduct a detailed audit and discovery stages

A pre-migration audit is more than a list of databases and systems. Teams should spend time compiling a complete inventory of every source, including ERPs, CRMs, SaaS platforms, data lakes, flat-file repositories, and any other relevant source.

Beyond this inventory, teams need to document:

• Data models
• Field-level schemas
• Volumes
• Refresh frequencies
• Interdepencies
• Metadata
• Sensitive fields

This step enables you to plan the migration as carefully as possible. Identifying hardcoded business logic in legacy scripts or hidden dependencies between systems at this stage can save costly reworks and downtime during cutover.

It also provides a baseline for compliance understanding, so you can start building a blueprint for the entire migration process.

Select the best migration strategy and tool

Now that you have a complete map of the existing environment, the next step involves deciding how to move data and through which mechanisms. This largely depends on the type of data migration project the team is handling.

If it’s a relatively straightforward storage migration, a lift-and-shift process can work. More complex projects like database moves might require more phased approaches or a hybrid strategy. Let’s say you’re moving from an on-prem Oracle instance to a cloud warehouse. You may need to plan for parallel runs to avoid downtime and leverage CDC to keep data synced during migration.

Ultimately, this means choosing the right migration tool depending on your needs:

• On-premise solutions work well for highly regulated workloads that cannot leave the existing environment
• Open-source frameworks provide a high level of customization and control
• Cloud-based solutions provide a high level of automation and scale for modern warehouses

As long as your migration approach closely relates to your goals and technical requirements, you can reduce risks and ensure the new environment supports data usage as quickly as possible.

Automate pipeline building and transformations

Engineering teams use manual pipelines frequently, and although they may work for small projects, they create errors and rework down the line when it comes to enterprise-scale environments. So much so that 80% of engineers report having to rebuild pipelines, and 39% say they have to so often or always. Not only does automation reduce engineering overhead, it also accelerates cutover during the migration process while drastically lowering risk.

Automation plays several critical roles here by moving pipeline creation from manual coding to straightforward configuration. Rather than writing and fixing hand-coded scripts, automation allows teams to define pipelines through solutions like managed ELT tools with pre-built connectors to handle schema drift. Meanwhile, workflow orchestration tools can generate repeatable pipelines from templates to handle scheduling and error handling across all sources consistently.

On the transformation side, CDC enables engineering teams to replicate inserts and deletes almost in real time to maintain sync between source and target during the migration process.

Finally, in-destination transformations with dbt automate the enforcement of standardized models and business logic at scale with embedded validation and testing.

Test and validate to maintain data quality

Rather than leaving this step to the end as a final check, validation should be built into the migration process from the beginning. This means everything from validating row counts to comparing sample records between source and target.

Even before the migration itself, pilot runs with small datasets can surface any potential issues. A staging environment also gives the engineering team an opportunity to test specific transformations and schema changes in isolation to validate before the full migration.

Monitoring logs and continuously running reconciliation reports also help to reduce discrepancies. Otherwise, they may only become apparent when business users notice and report errors after migration.

Incorporate governance and security measures

With sensitive information, migration can involve complex compliance requirements. So, it’s crucial to embed governance and security into the migration process itself to avoid:

• Regulatory violations and fines
• Data loss
• Unauthorized access

Some tactics the engineering team can employ or look for in data migration solutions include:

• Encryption in transit and at rest
• Role-based access controls
• Audit trails

You may also need to collaborate with other business units on updating policies for data retention and lineage to accurately reflect the new environment. Post-migration backups and monitoring should also confirm that any security controls are working correctly when data is live in the target database.

Best practices for data migration projects

Involve both business and technical stakeholders

Ownership is an important aspect of both governance and quality maintenance, and migrations often expose ownership gaps. Involving critical stakeholders from relevant business units from the start means you can cover off critical definitions and requirements, so the new environment supports cross-functional decisions with accurate data post-migration. Shadow IT systems that bypass security protocols consume 30-40% of enterprise IT budgets, so this level of ownership helps identify and eliminate any hidden systems during migration.

Design with hybrid and multi-cloud environments in mind

Single destination migrations are rare for large enterprises. A mixture of on-prem databases, cloud providers, and SaaS applications often serves as home to data across the organization. This means choosing architectures and solutions that support hybrid or multi-cloud deployments to provide flexibility and scale alongside the business.

Treat it as an ongoing integration project

Cutover is not the final step of a data migration, especially when APIs and schemas continue to evolve. Viewing migrations as a continuous pipeline or data integration exercise ensures you can handle updates and extensions and, with the right solutions, automate much of the process so the new environment doesn’t become another data silo.

Standardize data models early

A lack of standardization means engineering teams continue to spend their time reconciling inconsistent reports and contradictory logic. Poor data quality costs organizations an average of $13 million annually.

Taking the opportunity to standardize data with standard schemas and definitions creates consistent transformation rules and, ultimately, more reliable business analytics post-migration.

Plan for API changes and schema drift

Pipeline breakages due to API changes and schema drift are inevitable unless you build a plan for handling them into your migration process. Automated schema detection and drift handling ensure data stays consistent across systems and reduces engineering overhead previously spent on pipeline fixes.

Include observability and lineage in the process.

Data observability during the migration process is about end-to-end visibility into pipeline health and latency so that you can catch and fix anomalies at any stage. Lineage then tracks the origin of data and its transformation, so you can pinpoint what needs to be fixed and where. Embedding this into the process means early detection of issues and a higher level of trust in the data that’s reaching business users downstream.

Examples of data migration initiatives

Coke One North America

Coke One North America’s SAP landscape previously ran on on-premise DB2, supporting 35,000 employees across multiple bottling partners. The setup created reporting delays due to siloed data across ERP instances.

To resolve this, the team decided on a migration strategy to move to SAP S/4HANA on Google Cloud with the aim of maintaining as much business continuity during the process as possible. They implemented CDC to replicate ongoing transactions from DB2 into Google BigQuery and used a medallion architecture to structure data into bronze, silver, and gold. This layering preserved data integrity and delivered it to the new environment ready for end-user analytics.

After a successful cutover, the entire enterprise now had access to real-time SAP data, and engineering teams no longer had the burden of manual ETL maintenance.

Oldcastle Infrastructure

With a mixture of on-prem SQL Server databases and NetSuite, Oldcastle Infrastructure’s setup left critical data trapped in silos. Reporting was highly manual, and engineers spent much of their time building and repairing one-off pipelines.

The Oldcastle Infrastructure team decided to migrate to Snowflake as a central cloud data warehouse. They used automated connectors to replace custom ETL scripts to enable repeatable data ingestion. Meanwhile, in-destination schema mapping and transformations enabled the team to accelerate cutover with a much lower risk of breakages.

Post-migration, the organization saves $360,000 annually in engineering costs, with consolidated insights now available to the business via Tableau.

Tinuiti

Tinuiti was managing millions of marketing data points across multiple platforms, resulting in silos and a reliance on manual reporting from fragile pipelines. As a result, the company struggled to make decisions at a competitive speed, and engineers spent 150 hours per month on API maintenance.

They migrated from this setup to a centralized data lake on AWS S3, with automated ingestion and Iceberg in place for table management. The team also implemented a programmatic setup to reduce the onboarding time of new data sources to under an hour.

Now, the business has near real-time reporting across over 100 marketing channels and has reduced manual pipeline maintenance by 80%.

Conduct faster, safer data migrations

From patchwork legacy systems to a continuous influx of new SaaS platforms, even minor oversights in an enterprise data migration can cause downtime or significant data quality issues. They require a methodical, detailed approach from initial audit and discovery to model standardization and embedded governance.

Simplifying the process by automating pipeline creation and adaptation to schema changes removes manual overhead and reduces risk levels.

With Fivetran, you can do all this while syncing data in near real-time with CDC, accelerating cutover and delivering analytics-ready data to your users from day one.

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