Data Migration Strategies Moving and Transforming Data in the Cloud

Data Migration

Data Migration Strategies Moving and Transforming Data in the Cloud


Understanding Data Migration

Data migration refers to the process of moving data from one location or format to another. In cloud computing, it often involves migrating data from on-premises environments to the cloud, or between cloud providers. Data migration may also require transforming data to match the structure and format of the target system.

The Growing Importance of Data Migration

Recent research highlights the exponential growth in data migration needs as cloud adoption accelerates globally:
• Per Flexera, 93% of enterprises now have a multi-cloud strategy, underlining the need for seamless data migration across environments.
• MarketsandMarkets predicts the data migration market size to grow from $10.4 billion in 2021 to $20.7 billion by 2026 at a 14.5% CAGR.
• Mimecast states 47% of companies intend to shift more applications from on-premises infrastructure to the cloud over the next 1-2 years.
• However, Statista indicates 83% of enterprise workloads were still on-premises as of 2020, pointing to massive migration efforts required.
• A Datometry survey reveals nearly 80% of businesses suffered disruptions due to problematic data migrations.
• Furthermore, poor data quality costs large enterprises an average of $128.7 million annually, per Informatica.
Data migration is crucial for realizing cloud benefits but fraught with challenges. Proper strategies and planning are vital to avoid disruptions

Key Data Migration Strategies

Lift and Shift

This fast and simple method lifts current systems and shifts them to the cloud without changes. It provides quick migration and maintains configurations. However, it misses optimization opportunities that cloud-native architectures offer.


Use for short-term migrations when uptime and speed are critical.

Rehosting (IaaS)

Also called “lift and shift,” rehosting moves applications and data as-is to cloud infrastructure (IaaS). It enables fast migration to the cloud and reduces on-premises hardware management.


Suitable for legacy applications bound to a particular infrastructure. Allows maintaining current setups while realizing cloud benefits.

Replatforming (PaaS)

This approach involves some rearchitecting to optimize applications for cloud platforms (PaaS). It allows leveraging cloud capabilities like auto-scaling and managed services.


Provides increased agility, scalability, and efficiency compared to lift and shift.

Refactoring (SaaS)

With refactoring, applications undergo more significant changes to adopt cloud-native features and microservices architecture. This enables full optimization but requires extensive modifications.


Best suited for modernizing complex, mission-critical applications via incremental changes.

Repurchasing (SaaS)

Repurchasing replaces current software with ready-to-use SaaS alternatives. It eliminates infrastructure management but may involve data migration complexity.


Quickly attain IT modernization when existing software lacks cloud-ready architecture.


Best Practices for Successful Data Migration

Thorough assessment and planning, data cleansing, testing, backups, security controls, and monitoring are essential for seamless migrations. Engage experts early for complex initiatives spanning long durations or multiple platforms. With careful execution, you can harness the full benefits of the cloud while minimizing disruptions.

1. Develop a Comprehensive Migration Plan

Outline detailed requirements, constraints, timelines, resources needed, and success criteria. Identify data types, volumes, and sources. Determine optimal migration strategies based on business goals. Define contingencies for potential issues.

2. Perform Extensive Data Profiling and Cleansing

Examine source data to identify anomalies, redundancies, and quality problems. Cleanse data to remove duplicates, inaccuracies, and outdated information. Standardize formats. Data cleansing minimizes later issues.

3. Validate Data Integrity

Confirm data completeness, accuracy, and validity through comparisons across source and target systems. Verify referential integrity for relational data. Check for missing information, incorrectly mapped fields, formatting errors, or data corruption.

4. Test Migrations in Staging Environments

Execute test migrations in a staging environment that mirrors real-world conditions. Observe performance, troubleshoot issues, and refine processes before conducting actual migrations. Testing minimizes disruptions.

5. Maintain Appropriate Security Controls

Implement access controls, encryption, data masking, and network security to safeguard sensitive data during migration. Adhere to regulatory compliance requirements and enterprise security policies.

6. Establish Backup and Rollback Mechanisms

Maintain complete backups of data before migration. Confirm the ability to rollback in case of any failures, errors, or data loss. Backups enable recovery from worst-case scenarios.

7. Monitor Migration Progress

Continuously track migration status, data throughput, errors or lags in processing. Monitor system performance post-migration. Fine-tune configurations, re-allocate resources, or optimize databases as needed.

With meticulous planning, validation, testing, security controls, backups, and monitoring per these best practices, companies can migrate data smoothly and minimize disruptions. Data migrations may seem daunting but following proven procedures will lead to successful outcomes.