When companies change systems or move information to a more secure location, they typically need to perform a data migration. If a company wants to use cloud-based solutions, it must transfer existing information from localized hardware to a cloud environment.
A defined data migration plan is the best way to avoid issues arising during this process. Project teams can use it to guide everything from data migration planning to completion.
A data migration plan should include details on the information being moved, the data migration project’s scope, and the team’s overall goals. It should also include details regarding team members’ roles, the backup methods chosen, and the choice of migration tools. Let’s take a closer look at the steps involved with creating a data migration plan.
1. Assess the Source Data
First, start cataloging all the places where data targeted for migration resides. That includes all systems, databases, applications, and files. Include the metadata for every source. You want information about data types, sizes, relationships, dependencies, and formats.
Laying it all out makes identifying and prioritizing what’s crucial to business operations easier. You also want to ensure you have everything needed for compliance or to help with decision-making.
Data Structure
Review the format of source data to determine whether it’s a fit for the target system. Examples of data models typically used in business environments include:
- Relational models: Information organized in tables with rows and columns, where each row represents a record and each column represents a field. Relational databases use Structured Query Language (SQL) for querying and managing data. This model is ideal for structured data and supports relationships between different data tables through keys and indexes. Common examples of relational databases include MySQL, Oracle Database, and Microsoft SQL Server.
- Hierarchical models: Information stored in a tree-like structure where each record has a single parent, except for the root. Hierarchical databases organize data in parent-child relationships, which are efficient for certain types of queries involving hierarchical data. Examples of hierarchical databases include IBM’s Information Management System (IMS) and the Windows Registry. While data formats like JSON and XML can represent hierarchical data structures, they are used for data serialization and not as database models.
- Unstructured models: Information held in formats like documents, emails, images, videos, and multimedia files that don’t have a predefined data model or schema. Unstructured data lacks a consistent structure, making it more challenging to store and analyze. NoSQL databases and data lakes are often used to store unstructured data because they can handle large volumes and diverse data types. Examples include MongoDB, Apache Hadoop, and Elasticsearch.
When migrating data, you must ensure the information aligns with the schema to avoid data loss or misalignment. Once you identify source system entities, you map them to a corresponding one in the target system, a process called entity mapping.
Document relationships in source systems, such as table references and foreign key constraints. That way, you can replicate or rebuild them in the target system.
Compliance
Sensitive data must be handled carefully during data migrations to avoid data breaches. For that reason, you should document any sensitive information that falls under the project scope, including:
- Personally Identifiable Information: Includes social security numbers, names, and email addresses.
- Financial data: Includes credit card information and transaction records.
- Health information: Includes medical records, prescriptions, and other protected health information.
- Intellectual property: Includes trade secrets and confidential business processes.
Document all regulatory frameworks applicable to data, like HIPAA, CCPA, GDPR, and PCI DSS. Depending on the regulation, you may need to retain information for specific periods or destroy it after use.
2. Determine the Destination System
Next, review your target system to ensure it meets your business and technical needs. What goals are you looking to achieve with the data migration? In addition to transitioning to the cloud, your company may be looking to consolidate disparate data systems into a single source to make processes more efficient.
Understanding the scope and purpose of the migration helps you define what to look for when it comes to your target system’s capabilities. For example, if you’re moving to a new CRM system, ensure it offers the performance necessary to handle heavy data loads. You also need to make sure the source and target data models align.
Other capabilities to look for in your target system include:
- Whether the target system can integrate with other organizational tools
- If the system can handle data volumes needed to accommodate future business growth
- Whether the system can offer real-time data for financial and e-commerce transactions
- If you’re going to take a hybrid approach that uses cloud and on-premises data
3. Define Project Goals and Scope
Outline the main drivers for your migration project, including whether you want to see improved performance for data analysis or set up new business capabilities. Your data migration project plan should align with the company’s broader strategies.
Create SMART (Specific, Measurable, Achievable, Realistic, and Timely) goals to help with decision-making. Some common examples often used for data migration include:
- Ensure data gets moved from the source to the target system without corruption.
- Avoid disruption to business operations during the migration process.
- Ensure the migration complies with regulatory requirements and your information remains protected throughout the transfer.
- Avoid budget overruns while delivering the promised benefits.
4. Outline the Data Migration Strategy
Once you’ve established the scope of your migration, you’ll need to create a migration strategy for execution. Two common approaches to data migration are worth considering. You’ll also need to understand important roles for your team to optimize efficiency.
Big Bang
The Big Bang approach involves handling all data migration in one process, typically during a defined downtime window. Once the transfer is complete and the new system goes live, the legacy system is turned off.
This approach’s main benefits include a quick transition and a simplified project with fewer phases. Big Bang works best for smaller, less complicated migrations that allow for minimal disruption.
Phased Approach
The phased approach handles data migration in incremental stages over an extended period. Instead of doing everything simultaneously, the project team migrates systems or data sets in phases while the legacy system keeps running.
Taking a phased approach helps teams identify issues at the stage, allowing you to fix them at minimal cost. There’s also less system downtime because the source system continues running throughout the process, allowing for continuous business operations.
Define Team Roles
Every team member should have specific roles and responsibilities to ensure accountability. It also helps coordinate tasks during the entire migration. Examples of roles often set up for data migration projects include:
- Project manager: Oversees the entire project.
- Data migration lead: This person manages the technical aspects of data migration, such as mapping, transformation, and testing.
- Data analysts: Conduct tasks including data analysis, mapping, and cleansing.
- Business analysts: Acts as a bridge between team and business stakeholders to ensure adherence to business requirements.
- Quality assurance testers: Responsible for testing the migration process to validate data accuracy and system performance.
5. Prepare Comprehensive Data Backups
Backups safeguard against data loss, corruption, or other failures during a data migration. To start preparing data backups, catalog all data sets, databases, and files that will be part of the effort. Consider whether you plan to back up a database or a subset of information.
There are several backup methods to choose from depending on your infrastructure, the complexity of the system, and the type of data being migrated:
- Full backup: This backup method copies all data in the system and gives you a comprehensive snapshot of the system to ensure all data gets preserved.
- Incremental backup: This only duplicates information that has changed since the last backup of any type — full or incremental.
- Differential backup: This backup method backs up all information that’s changed since the last full backup.
SQL Server Management Studio and Oracle RMAN are examples of backup tools. You can use AWS, Azure Backup, or Google Cloud Backup for cloud infrastructure.
Schedule a full backup during a low-activity period to preserve all data before migrating. If performing a database backup, write scripts to automate the process. Set your automation tools to run the backups across different systems, which is vital in complex environments that use multiple databases.
6. Select Appropriate Migration Tools
There are many tools to choose from that help streamline transferring data across different applications, platforms, and databases. Look for tools that meet your needs regarding data mapping and transformation. Some come with these capabilities out of the box, while others require you to integrate with other third-party tools.
Other things to look for depending on your project needs include:
- Ability to sync data between the source and target system
- Data validation capabilities like record validation, data consistency checks, and error handling
- Rollback mechanisms and recovery options
- Ability to scale based on data size and performance requirements
7. Execute the Migration Process
Before you start, schedule a review of the overall data migration plan with all team members and stakeholders. Review everything from the scope of the migration to everyone’s roles and responsibilities and whether all backup and contingency plans are in place.
Ensure everyone understands communication methods, including team chats, status meetings, and emergency contacts. Schedule regular check-ins to ensure everyone’s on the same page regarding the data migration progress.
Check that the source and target systems are correctly set up and ready for migration. That includes ensuring both systems are operational, both resources are available and capable of handling the migration load, and all team members have the access needed for both systems, including administrative permissions.
Test a small portion of the data for migration before executing the complete migration workflow. That allows you to determine if there are issues that still need resolution. Once you’ve completed the pilot migration, test the results to ensure the entire process worked correctly.
Once you’ve satisfied your test requirements, execute a full backup before proceeding with the full data migration. Monitor the progress closely for issues like performance bottlenecks or system failures. After validating the migration, execute a cutover from the source to the target system.
8. Test Data Quality and Implement a Follow-Up Plan
Execute a final data validation round to ensure the information migrated successfully and no data loss or corruption occurred. Keep monitoring the system performance to see if issues need correcting. Gather feedback and communicate it to all team members and stakeholders.
Will Your Project Require Migrated Data to Integrate with Applications?
After migrating your data, it may be crucial to verify that your applications function correctly with the new environment. Post-migration testing can ensure data integrity is intact and all features work as expected.
Ranorex’s automated testing tools enable you to efficiently perform comprehensive post-migration testing. By automating UI and functional tests, Ranorex helps confirm that your applications interact seamlessly with the migrated data.
Schedule a free demo to see how Ranorex can help.
