Services
SERVICES
SOLUTIONS
TECHNOLOGIES
Industries
Insights
TRENDING TOPICS
INDUSTRY-RELATED TOPICS
OUR EXPERTS
Data migration strategy:
key steps, best practices & benefits
July 22, 2025
A data migration strategy outlines the approach and steps for moving data from one computing environment or storage system to another safely and accurately, as well as defines the project’s requirements, roles, timelines, and deliverables to ensure smooth data transfer.
Itransition provides data migration services to help companies move their data from one infrastructure, storage solution, data center, or application to another while maintaining data integrity, consistency, and compatibility with target systems.
Six steps to realize a data migration strategy
While to some extent tailored according to companies’ specifics and needs, a data migration process usually consists of the following key stages.
1
Planning
At this stage, you should outline your project expectations, choose a migration approach, such as immediate,
incremental, or parallel, set a realistic project budget and timeline, and lay out the metrics for success.
For this, you first need to assess the project’s scope, performing an exhaustive comparison of source and recipient
systems to locate whether additional configurations of the new system are required to ensure data structure compatibility.
Also, decide whether you want to use the old system after moving the data and plan its decommissioning if that
suits your needs.
Also, unless you have small amounts of data to transfer, you will need data migration tools for automating data
mapping, transformation, validation, and loading, so make sure to choose one based on your technical environment
and project scope.
Finally, build a data migration team from either your in-house IT specialists or outsourced experts.
2
Preparing data
The preparation of data for transfer encompasses three fundamental activities: auditing, backup, and
governance.
The data selected for migration should be examined at a granular level for any inconsistencies, duplicates, incompleteness,
and data quality issues, with any of them resolved them prior to migration. To mitigate data loss or corruption
risks, back up all your data to be able to restore it if anything goes wrong.
Additionally, specify users who have rights to access, edit, and remove data and map these roles and access levels
to the new system to avoid any misunderstanding and security gaps.
3
Pipeline design
Define what fields of the source system correspond to those of the target one and prepare detailed rules for
matching them, including setting up data transformation rules to meet the data format requirements of the
new system.
Outline the stages the data will go through, such as extraction, transformation, loading, and validation, and
develop a solid data security strategy for the migration project.
4
Process setup & testing
At this stage, proceed to setting up the destination environment and migration tools and then testing the migration process on a smaller scale using dummy data. Make sure the target environment has enough capacity to accommodate the source data and is configured in line with the established data governance and security requirements and permissions.
5
Execution & validation
To migrate data, you need to extract data from the source system, transform it into the right format, and
finally load it into the target system using an ETL solution or data migration tool.
After the migration is complete, check whether all necessary data was moved correctly and whether any was lost.
Verify the migrated data by running unit, system, full-volume, and batch-application tests and use the backup
to recover data if you find any problems or gaps in it.
Additionally, examine the target system’s performance, conduct integration and user acceptance tests, and disable
the legacy system if no longer needed.
6
Post-migration maintenance
To mitigate arising issues in a new environment, continue monitoring the new data environment. By keeping an eye out for any data discrepancies, performance problems, or possible errors in the new system, you can make timely adjustments and make sure the migrated data continues to meet your business requirements.
Benefits of creating a data migration strategy
Having a well-defined data migration plan can bring multiple benefits to businesses, allowing them to fully achieve their project goals.
Data loss & corruption prevention
A thoroughly planned data migration strategy encompasses data cleansing and validation, as well as continuous testing, helping avoid quality and availability issues with the migrated data.
System downtime minimization
A strategy clearly outlines data migration steps and the responsibilities within a team, minimizing confusion, disruptions, and delays during the migration.
Migration costs optimization
By outlining the project scope and clear steps to migrate data, a solid migration strategy helps businesses optimize resource allocation and avoid wasting time and money on unnecessary migration efforts.
Project predictability
With the project goals, scope, stages, mitigation steps, and rollback plans defined, a data migration strategy allows companies to estimate project timelines and budgets more accurately, reduce unforeseen problems, and deliver more predictable outcomes.
Get a tailored data migration strategy for your use case
10x
faster data processing than similar tools
Email archive migration solution
Itransition built a custom tool to support smooth migration of legacy email archives from/to 25+ archive systems, both on-premises and in the cloud, without compromising data integrity. The solution offers fast data processing, automated checks and reporting for high data fidelity, flexible migration options, capabilities to minimize impact on users during migration, and custom connectors to facilitate migration between and within different format versions and environments.
Fintech ITSM improvement & cloud migration
Itransition’s Atlassian experts optimized the client’s ITSM strategy and migrated the client’s Atlassian Jira and Confluence systems to the cloud, performing the migration in parallel to save time. During the migration, we backed up the client’s data and migrated it to a test cloud environment, validating data integrity before full-scale migration to the production instance.
Atlassian migration for IATA
Over the course of six months, Itransition’s Atlassian team performed comprehensive business analysis of the client’s processes, implemented Jira Service Desk and migrated data from the legacy tool to it, and conducted admin training sessions to streamline the transition to the new solution. We also automated a number of administrative workflows, allowing the client to save license costs and reduce the number of post-project issues.
Salesforce CRM for a global software engineering company
We migrated from a legacy on-premises CRM solution to a cloud one to manage customer information more efficiently and get access to advanced analytics and robust out-of-the-box functionality. Instead of dividing the project into multiple stages, we opted for one-time migration, finishing the transfer of all data, including around 30,000 leads, 10,000 opportunities, and 7,000 accounts and contacts, in less than 12 hours.
Industrial risk management software support
Itransition’s team introduced a new customizable preventative maintenance management module that improves operational visibility and creates an ordered workflow for maintenance operations. We also developed a custom data migration tool that enables events mapping between old and new product versions, helping seamlessly migrate existing data.
Odoo ERP system implementation
We assisted the customer in automating their business processes by customizing and implementing the Odoo ERP system. Our team migrated existing data to Odoo and performed bidirectional data synchronization between the source and target systems. To streamline data migration, we also developed an integration module to obtain customer data with minimal involvement of the client’s team and bypass additional manual reviews.
Key types of data migration strategies
There are three primary strategies for data migration, each with its advantages and disadvantages and used in different scenarios.
Immediate migration | Phased migration | Parallel migration | |
|---|---|---|---|
Overview | Also known as big-bang data migration, this strategy involves moving all data in a single operation, usually over a convenient period of time like a weekend or holiday. | Alternatively called incremental, agile, or trickle data migration, this strategy involves moving data in batches over time, with each data portion migration having its own scope, deadlines, and goals. | This approach involves keeping two systems running for a period of time until the company moves the data and departs from the legacy system. |
Advantages |
|
|
|
Disadvantages |
|
|
|
Best practices for successful data migration
To make the data migration process more efficient and cost-effective, consider adopting the following best practices.
Migrate low-critical data first
When migrating data from systems that support critical business functions, even a brief period of their unavailability can be costly, disrupt business processes, and negatively impact your business reputation. To maintain regular operations during the migration, start with less critical and smaller datasets, thoroughly testing the migration before proceeding to transferring live data in production systems.
Avoid upgrades during migration
Complete any planned system upgrades or new tool implementations before you begin the data migration process or postpone them until the migration project is fully completed. Doing several projects simultaneously increases the chances of compatibility issues or other errors, jeopardizing migration success and making troubleshooting more difficult.
Involve non-IT stakeholders early
Involve business users at the planning stage of your data migration project to better ensure its success. Stakeholders who perform data-driven decision-making and understand the structure and value of the source data should share their expertise about data types to prioritize for migration, what data supports daily operations, what data can be archived, and what formatting can or can’t be changed.
Data migration strategy challenges & how to overcome them
There are several challenges businesses should be aware of when developing a data migration strategy.
Challenge | Solution | |
|---|---|---|
Underestimating project timeline & scope | In some cases, data migration projects stretch beyond the predicted timelines and budgets due to
businesses being overly optimistic about their current data quality and the availability of internal
resources or underestimating the complexity of data transformations involved. | Possible solutions include conducting a business readiness assessment to understand existing project-related conditions, such as team expertise, existing data governance policies, the availability of data documentation, or legacy system limitations. Drawing on this assessment, you can build an action plan to close identified gaps to prevent them from impacting the project’s success in the future. Also, when developing a data migration strategy, include a safety period for possible contingencies and dealing with migration challenges. |
Choosing the wrong migration approach | Big-bang, trickle, and parallel migration cater to different business needs, so when improperly chosen,
they add to the project complexity, duration, costs, and team workloads. | To prevent opting for the wrong migration approach, invest in thorough pre-migration analysis and planning, turning to experienced data migration consultants if feasible. Consider your data volume, budget constraints, timeframe expectations, and overall goals to decide whether you can compromise on data migration speed and opt for a phased approach or if you need immediate data migration. |
Ignoring legacy system dependencies | Data is usually stored in systems that are interdependent and interconnected with the company’s IT
infrastructure. If not identified and managed, these dependencies can cause system failures, loss of data
integrity, business process errors, and increased system downtime after data migration. | Include dependency mapping into your data migration checklist to identify, understand, and visualize the relationships between different fields, tables, or entities. To optimize the process, use a dependency mapping tool for automation. Determine foreign relations, hierarchies, linked records, and the effects of changes in one field on related fields after the migration. Use staging environments or sandboxes to safely test the migration and associated dependencies to avoid them affecting your production environment. |
Itransition’s data migration services
Migration
Our experts craft a tailored data migration strategy for your specific business needs and provide end-to-end data migration services, relocating your assets between any data storage solutions, apps, or environments.
- Assessment of the current IT infrastructure, source data, source systems, and interdependencies between apps and databases
- Assistance with target system and migration tech stack selection
- Preparing a comprehensive data migration strategy
- Setting up ETL/ELT processes, designing the migration, and establishing data governance and data management rules
- Migration execution, testing, and validation
- Post-migration testing and troubleshooting
- Implementation of monitoring tools on demand
- User training
- Advising on system upgrades and updates
Consulting
Itransition’s team closely collaborates with your IT specialists and project stakeholders to assist with creating a data migration strategy that ensures minimal disruptions and data security risks. We also advise your team throughout the whole data migration cycle, recommending the best risk mitigation strategies and courses of action.
- Eliciting business needs and requirements for the data migration project
- Evaluating the current IT infrastructure in the company, the data to be migrated, and existing data management practices
- Assessing the company’s data migration readiness
- Calculating the project’s budget and duration and establishing project deliverables and success criteria
- Helping choose a suitable migration approach and tools, develop the migration logic, and define data governance policies and quality frameworks
- Assisting with post-launch maintenance and ensuring system scalability and security
Ensure business stability with a robust data migration strategy
Considering the current importance of data for business success, companies of various sizes will continue to accumulate it, treat it as an asset, and take care of it when moving to different technological environments. However, developing a well-thought-out data migration strategy can be a challenging process as it should take into account diverse data formats, system dependencies, and business requirements to prevent system downtime or disruption. If you’re seeking an experienced team to help ensure the success of your data migration project, you can reach out to us.
Searching for a partner to carry out data migration?
FAQs
Data migration refers to the process of moving data from one or multiple storage systems or applications to another and involves steps such as data preparation, extraction, transformation, and loading.
There are numerous reasons to start a data migration project, including switching to new business software, moving from on-premises to cloud-based data warehouses or other storage solutions, or integrating numerous systems during corporate transformations like mergers and acquisitions.
Depending on the location and destination of the data migrated, types of data migrations include:
- Cloud migration, which involves moving data to, from, or between cloud environments
- On-premises migration, which deals with moving on-premises data stored on the resources located on the company’s own infrastructure
- Hybrid migration, when some data is moved to the cloud, and the rest stays on-premises, and vice versa
Zero-downtime entails performing data migration with minimal service interruption, such as by migrating the full copy of the source data to the target system, to enable users to work in the source system while the migration is underway.
Migration types comprise schema migration, application migration, business process migration, and storage migration, including database migration, data warehouse migration, or data lake migration. These types go beyond data migration, can involve moving the whole platforms and applications from one environment to another, and require the creation of more robust migration strategies, such as a cloud data migration strategy or a data center migration strategy.
Related cases
Insights
Data Management Services and Solutions We Deliver
We help companies build a robust and efficient data infrastructure to turn data into a strategic asset. Book a consultation with our data management experts.
Insights
Big Data Governance: Roles, Frameworks, and a Case Study
Review the anatomy of big data governance and learn why it’s an essential component of any big data strategy.
Insights
Data Analytics Strategy - Itransition
Itransition’s BI team presents the reactive data analytics strategy for uncertain times. Learn more.
Case study
Email Archive Migration Solution
Read how Itransition delivered data migration services for the client’s Windows-based email archive content migration tool.
Insights
The Future of Big Data: Forecasts & Statistics for 2025
Explore projections for the future of big data, including insights into big data adoption, market trends, and industry-specific applications.
Insights
Developing a Business Intelligence Project Plan: A Guide
Learn how to bolster your investments by utilizing our four-step business intelligence planning process.