Modern Data Migration Plan Framework and Best Practices

Why migrate to the cloud?

Migrating to the cloud is now imperative for any organization looking to modernize its data center, ensure high-functioning access to volumes of data, and optimize operational efficiency. By 2023, more than 94% of enterprises had adopted cloud services, and 67% of all enterprise data storage systems operated in cloud environments. 

The continued surge is driven by demands for streamlined data integration, advanced migration tools, and cost optimization. Notably, cloud computing market projections exceed $1trillion by 2030, underscoring the vital function cloud frameworks play in today's digital landscape.

Organizations transitioning to the cloud unlock benefits including:

• Cost savings, often 20–30% less than legacy setups.

• Expanded agility and real-time analytics functions.

• Higher data security via improved encryption, access controls, and metrics. Resilience through hybrid, multi-cloud models—now the standard for competitive businesses.

In summary, moving data and applications to the cloud isn’t about following trends—it’s about equipping your enterprise to scale successfully, secure sensitive data, and future-proof your production environment with the best migration approach. This foundational shift leads directly to the next step: planning your migration with the utmost rigor.

5 common pain points of cloud migration

Cloud migrations are increasingly complex, especially as organizations move beyond basic “lift-and-shift” models to address diverse data types—from structured and transactional databases to machine and semi-structured data. Five pain points commonly encountered include:

1. Complexity of data management: Modern migrations must handle vast volumes of data across various storage systems and formats. As companies collect more non-traditional data (like internal text and machine data), planning requires deeper assessment of assets, functions, and integration needs.

2. Skills gaps and new responsibilities: Migrating to a new system introduces unfamiliar workflows and requires staff equipped to operate migration tools, execute ETL processes, and support the target system. Training and access to cloud management expertise are critical to minimize missteps.

3. Data classification and security risks: Inadequate identification of sensitive data can lead to data loss or exposure, especially if security controls aren’t aligned across the new production environment. Data catalogs and governance frameworks are essential for safeguarding assets throughout migration.

4. Legacy system challenges: Old applications and data storage systems are often incompatible with the cloud. Automation can help re-platform legacy code, but careful inventory and dependency mapping remain vital to prevent costly disruptions or integration failures.

5. Data governance and trust: Establishing governance protocols before migration ensures data quality, access controls, compliance, and business continuity in the target system. Prioritizing trusted, popular data for migration—using catalogs like Alation—drives faster, more reliable adoption and reduces unnecessary data movement.

By kicking off these projects with clear expectations for key challenges, data leaders can craft a thoughtful, useful plan for data migration. 

Why leaders need a thoughtful, thorough plan to migrate

Despite extensive adoption, data migration remains challenging. Research from Gartner highlights that 83% of migration projects exceed budgets and schedules or fail, with cost overruns and downtime stemming from poorly executed migration tools and unclear governance over the target system. Without a robust migration strategy, organizations risk data loss, security breaches, stalled data integration, and wasted resources.

A successful migration process is strategic, not just a technical box to tick. Leaders who treat data migration as a core business transformation, establish clear metrics, and select fit-for-purpose data migration tools will mitigate risks and maximize ROI. 

Addressing these complexities before starting the migration approach sets the stage for success, which leads to the essential pre-implementation preparation.

Building your data migration plan: Key factors to consider

A strong migration plan requires a multidimensional assessment of your organization’s data, compliance needs, and goals:

Industry & compliance demands

Each industry faces unique regulatory requirements—for example, GDPR for global enterprises and HIPAA for healthcare. Evaluating these demands ensures you secure sensitive data across your migration, including structured and unstructured data sets and critical production workloads.

Business priorities & migration goals

Align migration goals—such as cost reduction, improved function in the new system, or compliance—with overall business priorities. Identify top use cases to justify investment and inform which data sets hold the most value.

Risk factors

Proactively assess risks such as integration bottlenecks, system incompatibilities, potential data loss, and the challenge of orchestrating the migration of massive volumes of data into the target environment.

Team building

Build your migration team carefully: bring together experts in IT, compliance, data governance, and business units to oversee each migration phase and metric. Leveraging platforms like Alation enriches this process with centralized management, streamlined documentation, and clarity around every migration step.

With all key factors considered, organizations can enter the practical phase prepared to coordinate their migration tools and workflows effectively.

Practical steps to implement a data migration strategy

1. Assess existing data assets 

Begin with a precise inventory of your legacy and cloud-bound data assets, including their dependencies, storage systems, and use case relevance. Cataloging with Alation or similar data migration tools enables thorough discovery, profiling, and evaluation across the data center—illuminating both data quality and risks to data security.

Mapping out dependencies and reviewing volumes of sensitive data ensures the data migration approach is informed, actionable, and synchronized with ETL processes. With a defined data estate, organizations are equipped to delegate responsibilities efficiently in the next phase.

2. Define roles and responsibilities early

Assign ownership for data sets, migration tools, and compliance oversight at the outset. Document responsibilities and establish communication channels to accelerate issue resolution during migration to the new system.

Early role definition ensures that every phase—from test migration to full production—runs seamlessly. This dovetails into deploying governance protocols that guarantee security and compliance as the migration proceeds.

3. Develop governance protocols

Effective governance is codified before migration begins. Use modern identity management and access controls tailored for cloud storage systems to protect sensitive data and support ongoing compliance.

Establish real-time monitoring of metrics and usage patterns to maintain data quality throughout every phase of migration. When governance protocols are integrated from the start, transferring critical assets and minimizing risk is a matter of process rather than hope.

4. Execute controlled data transfer phases

Choose the migration approach and sequence that aligns with your organization’s goals, data volumes, and risk profile. Whether you opt for lift-and-shift, phased transfers, or hybrid migration, orchestrate each phase with tested migration tools to avoid data loss and maintain the function of critical business applications in the target system.

Controlled, tool-driven migration minimizes downtime, validates ETL results, and keeps your production environment stable while you transition. With data relocated, performance validation and adoption are the next priorities.

5. Validate performance and finalize adoption

Following the migration, test data quality, completeness, and operational metrics within the new system. Validate application integrations and user access, prioritizing feedback loops to resolve friction points and optimize adoption.

Document lessons learned and optimize ongoing maintenance, ensuring that future migrations benefit from best practices and robust metrics. Successfully addressing migration challenges primes organizations to confront post-migration risks and capitalize on new opportunities.

3 challenges and best practices for effective migration

1. Eliminate inconsistent data formats

Schema mismatches threaten the validity of analytics and the integration of migrated functions. Leverage metadata-driven migration tools and catalogs like Alation to reconcile and standardize data formats ahead of transfer, preventing errors and confusion in the target system.

Early resolution of format issues enables reliable analytics and continuity across migration phases, supporting high-function business reporting and minimizing ETL complexity.

2. Coordinate minimal downtime

Downtime must be minimized to avoid impacting production environments and business users. Use parallel migration tools, real-time replication, and staged migration windows to contain risk and ensure continuous function during transfer.

A well-orchestrated migration allows immediate resumption of critical services and data access in the new system, while also embedding security and resilience—vital for any enterprise-grade data center.

3. Ensure ongoing security compliance

Security is a constant concern, especially when handling sensitive data. Ensure encryption in transit and at rest, frequent audits, and alignment with your sector’s compliance standards. Modern data migration tools can automate several steps here, but manual oversight remains essential for the most sensitive volumes of data.

Integrating security into every aspect of data migration positions the business to scale confidently, knowing data integrity and privacy are guaranteed. Now, the focus shifts to choosing the optimal migration approach for your use case.

Key types of data migration strategies

Selecting the best migration strategy depends on dataset size, criticality, and desired performance metrics.

Carefully weigh the trade-offs of each approach with respect to ETL, integration requirements, and the function of your new or existing systems. Build contingency protocols and measure migration effectiveness with robust metrics to ensure business continuity.

How do you prioritize data to migrate?

Effective migration begins with prioritization. Use data catalogs and analytics to identify which volumes of data are most valuable for immediate transfer, balancing business-criticality, compliance, access frequency, and storage system dependencies.

Prioritizing not only accelerates time-to-value for high-impact use cases, but also sets up your team to leverage migration tools effectively across remaining assets. This momentum becomes a foundation for broader enterprise transformation and supports future innovation.

How a data catalog supports data migration

A data catalog is a pivotal enabler of efficient, accurate, and secure data migrations. Alation’s data catalog offers a unified view of enterprise data across legacy and target environments, simplifying function identification, dependency mapping, and risk mitigation.

Key benefits include:

• Comprehensive inventory: Unmatched visibility into all storage systems and data assets so teams never overlook critical sets.

• Data lineage tracking: Detailed lineage and dependencies preserve integration logic and minimize risk of breaking production or ETL workflows.

• Risk mitigation: Insight into sensitive data and automated governance reduces the risk of compliance breaches or data loss.

• Post-migration optimization: Ongoing monitoring and impact analysis of moved data supports cost control and maximized resource utilization.

Data catalogs also unify data integration and migration metrics across platforms, helping teams optimize both migration tools and business functions post-migration. This supports a seamless transition into the target system and new environment, strengthening the overall migration approach.

How Spark NZ migrated faster, smarter with Alation

Spark New Zealand, the country’s largest telecommunications and digital services provider, faced the challenge of siloed, dispersed data that slowed analytics and reporting. Even highly skilled engineers spent excessive time searching for trustworthy data within fragmented storage systems.

To transform their data center, Spark NZ selected Alation’s Data Intelligence Platform to catalog and prioritize assets before migrating to the Snowflake Data Cloud. By focusing on the biggest value use cases and migrating critical volumes of data first, Spark avoided legacy data loss and obsolete asset transfer. Alation’s data catalog enabled Spark to trace data lineage, ensure sensitive data was properly governed, and provide their team unified access throughout the migration—and beyond.

The results:

• Power BI dashboard delivery accelerated by at least 30%.

• Analyst and developer productivity rose sharply as teams reused reports and datasets.

• Machine learning feature engineering became more efficient, supporting rapid creation of new services.

• Mobile revenue increased by 9% in FY23 as faster reporting led to better strategic decisions and customer engagement.

Alation’s catalog continues to help Spark manage migration-related metrics, prevent feature duplication, and guarantee governance of sensitive machine learning assets. Today, every analytics user at Spark can discover, understand, and trust migrated data—all in their new system on Snowflake.

Building momentum for future initiatives

A successful cloud migration is more than a technical accomplishment—it is the cornerstone for innovation and growth. By leveraging best-in-class data migration tools, deploying effective governance, and integrating robust data catalogs into every step, organizations turn migration into a strategic advantage.

Once your migration to the target system is complete, the foundation is set for smarter decision-making, advanced data integration, and rapid scaling of future initiatives. Platforms like Alation empower teams to continually optimize data function, secure digital assets, and drive value from every use case.