Leading companies, which were furiously embedding analytics into their decision-making are now tackling the more complex challenges of racing ahead with AI deployments for efficiency and innovation and streamlining operations for traceability and sustainability. Underpinning the success of these initiatives is speedily accessible, clean, well-classified, and secure data. However, even global businesses do not have all their data in this ideal format, raising the necessity for data migration.
Data migration is needed when:
- Businesses replace outdated legacy systems with modern ones
- Moving data from on-premises storage to cloud platforms
- Companies merge or if there is an acquisition, the organization must consolidate and migrate data to a unified platform
- Organizations set up new databases or data warehouses to better organize and analyze
- Compliance with regulations requires data to be stored in specific formats.
What is Data Migration?
It is the moving of data volumes from one location to another for improved optimization of resources, accessibility, accuracy, and security. It is a set of practices for seamless data transfer that ensures the data reaches its new destination in the shortest possible time, completely prepared for old and new use cases.
The daunting criteria that define good data migration can be understood through this metaphor: Imagine being asked to move an entire library of books, each unique and irreplaceable. A simple misstep can lead to lost information, downtime, or costly errors. The complex initiative demands a well-planned roadmap to reach the destination without failure. This is where a data migration checklist comes in.
What is a Data Migration Checklist?A data migration checklist guides the transfer of data from a source to a destination. It lists actions to audit existing data, remove discrepancies, define migration goals, select a strategy, and validate the data post-migration. A checklist ensures the migration process is efficient, error-free, and avoids data loss. |
Our comprehensive checklist covers the critical steps of the data migration process.
Step 1: Audit your Data Landscape
Before starting with data migration, you must have a clear idea of your existing data. There must be a robust understanding of the volume, quality, and formats, in which the data exists.
Identify Data Sources
Before you initiate data migration, it is a must to understand the existing data within your legacy system. Conduct a comprehensive assessment and identify the scope of the data to be migrated.
- Note down all the data sources that are involved in the migration
- Include information such as data types, custom fields, attributes, relevance, size, sensitivity, and formats for each source
- Identify the criticality to prioritize for migration
- Build an accurate inventory.
Data Cleanup
When you combine multiple data sources, there are chances that the data will be mislabeled or duplicated. To avoid such instances, it is imperative that you perform a data cleanup.
Standardize the data to ensure accuracy. A few simple examples:
- Standardization of address formats (e.g., different formats of phone numbers, street, neighbourhood, etc.)
- Date format consistency (US vs. EU formats)
- Character encoding issues (ASCII vs. UTF-8)
- Currency conversion and decimal precision
- Metrics of measurement (Miles vs Kms)
Check for data inconsistencies, duplicate records, and missing values:
- Different prices for the same product
- Multiple records for the same customer
- Important information such as credit score or postcode missing.
Monitor Data Quality
The next step in the data migration checklist is to understand the data’s quality and content. Also termed data archaeology or data profiling, the set of practices measure data quality based on factors such as completeness, consistency, accuracy, timeliness, and accessibility.
- Identify patterns, anomalies, relationships, quality, and structures, by analyzing datasets
- Ensure the integrity of the data by checking for dependencies and interrelationships
- Obsolete data segments that are not relevant to the migration should be excluded
- Create a report that lists the issues present in the data and actions for resolution
Data Mapping
In this step, the data fields from one data source are matched to another. Data mapping is a crucial step in data migration as it ensures the data is properly migrated, and can be used for analysis and decision-making.
- Ensure that there are clear mapping rules for each data element
- Create a mapping document that shows how the field will be mapped from the source to the target system. This will act as a reference during the migration process
- You must validate the accuracy of the data post-migration based on the defined mappings by developing test cases.
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Some technical metrics to monitor:
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Step 2: Data Migration Planning
Define Migration Goals and Scope
The goals and scope of a data migration project are unique to every organization. Factors such as purpose, costs, timelines, technical limitations, and business impact help define data migration goals and scope.
- Clearly define goals for the migration. Are you looking for cost reduction, improving performance, or readying the data for advanced use cases? When your objectives are clear, resources will be utilized in the right way
- Determine the scope of the migration project by defining what will be included so you do not spend time and money unnecessarily. Will you move the entire database or just related applications?
- Establish clear metrics or specific performance benchmarks to measure the success of the migration
- Determine the cost of the migration exercise
- Identify the inherent risks associated with migration and a proactive response strategy.
Define Project Timelines
A visual representation of the tasks and deadlines for a data migration project helps with better planning and communication.
- Create a detailed plan that includes the timelines and milestones for the project
- List each of the project phases, from data evaluation to validation
- Assign responsibilities for each phase
- Identify who will be the main lead of the project, manage data, and communicate with relevant stakeholders
- Plan for contingencies in case of unforeseen situations or emergencies
- Define clear processes for how to track progress, report issues, and handle sensitive cases.
Data Backup
Before you migrate, create a full backup of the systems you plan to migrate. Even if you plan to leave the original system without any change, this part is important. If there are other systems that might be affected by the migration, back those up as well.
- Make sure that data backups are available to avert data loss during the migration
- Protect sensitive data during migration by using encryption and multi-factor authentication.
Select a Migration Strategy
Identify the data migration method that is suitable for your organization
Choose the appropriate data migration among the following: Big bang (migrating all data at once), Phased (migrating in phases), or Trickle (continuous, incremental) migration
The right migration strategy depends on data and use case complexity, resource and organizational constraints, and desired long-term impact.
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The four types of data migration
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What are the 6Rs of cloud data migration?
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Pre-Migration Assessment
This step evaluates the components of a database migration and identifies issues that prevent migration from running properly. The goal is to identify and fix problems before migration to avoid delays.
- Use the ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) process to prepare the data for migration
- See if existing frameworks or tools can be used, or if new tools are required
- Leverage third-party tools that are useful for complex migrations.
Allocate the Necessary Resources
The right people, tools, budget, and time must be optimally allocated for each phase of the migration process to ensure a smooth exercise.
- Determine how much help you need for each task, that is, data cleanup, profiling, mapping, and testing
- For a seamless integration process, assign the right resources, budget, and time
- Keep the necessary tools and software handy
- Clearly define job responsibilities for each stakeholder
- Develop a communication plan to keep all the stakeholders informed
- Use automation tools for repetitive tasks to find more time to address critical issues
Step 3: Migration Execution
Real-Time Monitoring
It ensures that data is transferred correctly and risks are mitigated.
- Use monitoring tools to track progress and ensure timely completion of the migration
- Implement triggers or alerts for critical errors or delays which need immediate redressal
- Check for data integrity issues or errors that can come up during the migration process
- Ensure there is constant communication with all the stakeholders and apprise them of the progress
Step 4: Testing and Validation
Post-Migration Validation
- By taking a sample set from the source system, the migrated data must be validated for functionality, performance, and documentation
- Validate the data integrity after migration by running queries on fields and comparing the results between the source and target systems
- Check for data accuracy and completeness by comparing the data in the source and destination databases
- Conduct User Acceptance Tests (UAT) to ensure that the migrated data is viable for the business’s requirements
Post-Migration Monitoring
- There should be ongoing monitoring processes in place to track the performance of the migrated data. Performance testing should include load testing, stress testing, and performance tuning
- Track the data migration effectively by establishing logging systems
- Continuously monitor metrics such as system downtime, data transfer rates, and error logs to identify potential issues.
Clean Up and Optimize
- Identify residual errors like duplicates or incomplete records that may have occurred during the migration process
- Verify data mapping by ensuring that data fields are properly aligned with the new system’s structure
- Delete redundant data that isn’t relevant to your future operations
- Create a troubleshooting guide for handling common post-migration issues.
Knowledge Transfer
- Set up support channels for addressing queries regarding the migration
- Define who is responsible for maintaining and supporting the new system
- Present an overview of the migration to all stakeholders, highlighting the outcomes
Conclusion
Data migration is a risky and complex endeavour. Using this data migration checklist will ensure it goes smoothly, no matter your objective, source, or destination. Given its complexity, it is important to meticulously plan your migration exercise by following the above pointers.
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An American retailer was struggling to move to the cloud with their current environment, consistently failing to meet SLAs and unable to scale ML workloads across key use cases. Tredence helped the company modernize its data infrastructure and take full advantage of its data through our migration accelerators, which led to:
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FAQs:
What is data migration?
Data migration is the exercise of moving data from an old source to a modern destination for improved resource utilization, accessibility for decision-making, and tangible business impact.
What are the best practices for data migration?
- Access or prepare a thorough data migration checklist that covers the following steps:
- A thorough migration assessment: Create a vision for your data migration
- Isolating production systems: Once the data migration is complete, your teams should test data with less important systems
- Completing system upgrades prior: If original systems or new applications need an upgrade, do so before the migration process
- Establishing a timeframe: Partners can help you accurately scope the migration initiative, including setting appropriate timelines
- Validating and testing data post-migration: Ensure that data performs as expected
- Audit processes: Keep projects on track and proactively mitigate risk by auditing processes and systems indefinitely.
What should I test after data migration?
After the migration, you should test the following to see if it was successful:
- Compare the data in the new system to the source system to make sure that all the data was transferred accurately
- Check if all the features and functions work properly
- Test if the system can handle the expected volume of data and transactions
- Make sure that the data is secure and the system is in line with security standards
- Verify that it is delivering the desired business impact.
