Data Migration: Challenges & Risks During Legacy System Modernization

Preparing for data migration

Navigating the process of data migration while ensuring safety is challenging. How can you ensure data consistency, quality, and availability throughout and post-migration? Dive into our comprehensive guide and let us steer you through data migration challenges and best practices. 

Types of data migration & risks connected to them

There are 6 basic types of data migration, based on the source and target location of data:

Database migration

This migration type includes transferring data between two database systems. A database is a structured storage media managed through a database management tool (DMBS). Migration is mostly associated with moving information from one DMBS to another or upgrading the existing DMBS.

The risks connected to database migration include especially:

• data loss or corruption
• downtime and service disruptions
• degraded performance
• differences in data formats or system functionalities
• security vulnerabilities
• regulatory compliance

Application migration

Migration means transferring data from one framework to another vendor or system. The main data migration challenge is that data structure, format, and models vary depending on the application. Before the migration, it’s a must to ensure that data is communicable and transferable between the software, so the format must be well-considered and unified. This migration can be performed with API.

The risks connected to application migration include especially:

• problems with software or hardware compatibility in the new environment
• data loss or corruption
• operational disruptions - downtime and service interruptions
• performance degradation
• increased risk of security breaches
• user adoption challenges
• regulatory compliance issues
• integration difficulties

Storage migration

This type deals with transferring data from one storage device to another. For instance, data can be transferred between two hard disks. The process should be preceded by validation, cloning, and updating the outdated or invalid information. It is typically applied when the organization needs to transfer the data to a more modern technology or storage infrastructure.

The risks connected to storage migration include especially:

• potential for losing or corrupting data
• operational interruptions and limited data accessibility
• slower data access or system performance issues
• security vulnerabilities
• data integrity issues
• scalability concerns
• complexity in data management

Cloud migration

This type involves shifting the on-premise data storage to the cloud environment or between two clouds. The main reason behind the migration is scalability, flexibility, security, and cost-effectiveness. As this type of migration is quite secure and provides companies with numerous benefits, it has become a strong trend. In 2022, 60% of companies stored their data in the cloud, compared with 30% in 2015 and 50% in 2020.

Potential challenges connected to cloud migration include:

• data security and privacy
• performance optimization
• data integrity
• compatibility and interoperability
• downtime minimization
• large data volumes

Business process migration

The main reasons behind this type of data migration are mergers and acquisitions, business reorganizations, or major optimizations. It may involve transferring applications and databases to new environments and infrastructures.

Potential challenges connected to business process migration include:

• interruptions or slowdowns in business operations
• employee resistance
• decreased productivity
• inaccurate process mapping
• risk of data loss or errors during data integration
• quality control problems
• technology integration issues
• compliance risks
• insufficient training or support

Data center migration

As a data center is a physical infrastructure built of servers, networks, and other types of IT equipment, its migration may mean different things. For instance, it can be translated into a relocation of existing devices by the company and going fully digital. It may also involve the migration of data and business applications to new storage and servers.

The risks connected to data center migration include:

• potential loss of service and operational disruptions
• data loss or corruption
• increased exposure to security threats
• challenges in ensuring new hardware, software, and systems work seamlessly with migrated data
• performance degradation
• hardware or software failures during migration
• physical and logistical challenges

‍

Major data migration challenges & how to cope with them

According to Gartner, 83% of data migration projects fail or exceed the timing and budget. This indicates that data migration can be challenging, and failure can severely affect budget and timing.

Now, let’s analyze each challenge in more detail, checking out how to prepare and acknowledging the cautionary points.

Challenge 1: data loss

Data loss occurs when critical data is either not transferred or irreversibly altered during migration.

Cautionary points

• Incomplete data backup.
• Inadequate data validation post-migration.
• Overlooking legacy data formats.

Preparation steps

• Conduct thorough data audits pre-migration.
• Implement robust backup strategies.
• Ensure compatibility of data formats.
• Utilize data migration tools with error-checking capabilities.
• Perform phased migration and validation.

How to do that

A financial app migrating to a new CRM system:

• Step 1: audit all customer data for completeness.
• Step 2: back up data in multiple formats.
• Step 3: test the migration in a controlled environment.
• Step 4: gradually migrate data while monitoring for errors.
• Step 5: validate post-migration data integrity.

<span class="colorbox1" fs-test-element="box1"><p>Need some help with your financial app? Check out this ranking of top fintech software development companies, who are experienced in data migration and legacy system modernization.</p></span>

Challenge 2:emantic risks

Semantic risks involve data being incorrectly mapped or interpreted in the new system.

Cautionary points

• Misalignment of data fields.
• Inconsistent data formats.
• Misinterpretation of data meanings.

Preparation steps

• Map data fields meticulously.
• Standardize data formats before migration.
• Conduct pilot migrations to test data integrity.
• Involve stakeholders familiar with the data's context.

How to do that

An educational platform migrating student records:

• Step 1: collaborate with educational staff for data mapping.
• Step 2: run pilot tests with sample data sets.
• Step 3: adjust mappings based on pilot test feedback.
• Step 4: conduct full migration with continuous monitoring.

Challenge 3: application stability

Stability issues arise when the new platform is inadequately developed or configured.

Cautionary points

• Overlooking system integration requirements.
• Inadequate testing of new applications.
• Ignoring performance benchmarks.

Preparation steps

• Ensure comprehensive system integration planning.
• Conduct extensive application testing.
• Set and monitor performance benchmarks.
• Implement fallback mechanisms.

How to do that

A healthcare app integrating a new patient management system:

• Step 1: map out integration requirements with existing systems.
• Step 2: run extensive beta tests in a clinical setting.
• Step 3: monitor system performance against set benchmarks.
• Step 4: keep the old system on standby for fallback.

Challenge 4: data security

The risk of data breaches increases during migration due to potential vulnerabilities.

Cautionary points

• Insecure data transfer channels.
• Lack of data encryption.
• Inadequate access controls in the new system.

Preparation steps

• Utilize secure, encrypted data transfer methods.
• Implement robust access control mechanisms.
• Regularly audit data security during migration.
• Train staff on data security protocols.

How to do that

A financial institution migrating to a cloud-based system:

• Step 1: encrypt all data before migration.
• Step 2: use secure, vetted data transfer channels.
• Step 3: implement new access controls in the cloud environment.
• Step 4: conduct ongoing security audits during migration.

Challenge 5: extended downtime

Downtime occurs when the source system is unavailable during migration, affecting operations.

Cautionary points

• Inadequate planning for migration phases.
• Underestimating the migration timeline.
• Failure to communicate with stakeholders.

Preparation steps

• Develop a phased migration plan.
• Communicate timelines with all stakeholders.
• Establish temporary operational measures.
• Monitor migration progress closely.

How to do that

An educational institution updating its learning management system (LMS):

• Step 1: plan migration during low-usage periods.
• Step 2: inform faculty and students about expected downtimes.
• Step 3: provide alternative resources during migration.
• Step 4: monitor and adjust the migration process in real-time.

Challenge 6: exceeded budget

Cost overruns occur when the migration process is prolonged or encounters unforeseen challenges.

Cautionary points

• Underestimating resource requirements.
• Failing to account for unexpected challenges.
• Inadequate project management.

Preparation steps

• Develop a detailed budget with contingencies.
• Regularly review and adjust the budget.
• Employ efficient project management practices.
• Prioritize critical migration elements.

How to do that

A healthcare provider upgrading its electronic health record (EHR) system:

• Step 1: set a realistic budget with a buffer for unforeseen expenses.
• Step 2: conduct regular budget reviews and adjustments.
• Step 3: prioritize migration of critical patient data.
• Step 4: streamline project management to avoid inefficiencies.

Challenge 7: data volume and complexity

Large volumes of data or highly complex data structures can make migration a daunting task.

Cautionary points

• Underestimating the time and resources needed for migration.
• Potential data corruption or loss during transfer.
• Performance issues when dealing with big data sets.

Preparation steps

• Assess and categorize data by volume and complexity.
• Use scalable data migration tools and techniques.
• Implement robust data validation and error-checking processes.
• Consider incremental migration strategies.

How to do that

A large retail company migrating customer data to a new CRM system:

• Step 1: categorize data by priority and complexity.
• Step 2: migrate data in phases, starting with less complex sets.
• Step 3: continuously monitor and validate data integrity.
• Step 4: adjust migration strategy based on early-phase learnings.

<span class="colorbox1" fs-test-element="box1"><p>Read also: Big Bang Migration vs Trickle Migration Approach in Legacy Modernization [Key Differences & Considerations]</p></span>

Challenge 8: data quality issues

Poor data quality, such as incomplete, outdated, or inaccurate data, can lead to significant issues during migration.

Cautionary points

• Propagating existing data quality issues into the new system.
• Additional time and effort required for data cleansing.
• Dependence on legacy data standards.

Preparation steps

• Conduct a thorough data quality assessment.
• Cleanse and standardize data prior to migration.
• Update data governance policies.
• Implement continuous data quality monitoring.

How to do that

An insurance company upgrading its claim processing system:

• Step 1: audit existing claim data for quality issues.
• Step 2: standardize and cleanse data as needed.
• Step 3: migrate cleaned data to the new system.
• Step 4: implement new data quality checks in the upgraded system.

Challenge 9: integration with existing systems

Ensuring seamless integration between the migrated data/applications and existing systems can be challenging.

Cautionary points

• Compatibility issues between old and new systems.
• Disruptions to existing workflows.
• Data synchronization problems.

Preparation steps

• Map out all integration points and dependencies.
• Test integrations in a controlled environment.
• Develop a fallback plan in case of integration failures.
• Provide training and support for new system integrations.

How to do that

A university integrating a new student information system:

• Step 1: identify and document integration points with existing systems.
• Step 2: conduct pilot tests to validate integrations.
• Step 3: roll out the new system incrementally.
• Step 4: monitor system performance and user feedback.

Challenge 10: regulatory compliance and data privacy

Adhering to regulatory requirements and ensuring data privacy during migration is critical, especially for sensitive data.

Cautionary points

• Non-compliance with data protection regulations.
• Risks of data breaches during migration.
• Inadequate data privacy controls in the new system.

Preparation steps

• Understand and comply with relevant regulations (e.g., GDPR, HIPAA).
• Encrypt sensitive data during transfer.
• Implement robust privacy controls in the new system.
• Conduct regular compliance audits.

How to do that

A healthcare provider migrating patient records to a new EHR system:

• Step 1: review and adhere to HIPAA guidelines for data migration.
• Step 2: use encrypted channels for data transfer.
• Step 3: implement access controls in the new EHR system.
• Step 4: conduct post-migration audits to ensure compliance.

Challenge 11: performance degradation

This occurs when system performance drops due to migration-related changes.

Cautionary points

• Slower response times can frustrate users and customers.
• Reduced productivity and efficiency.
• Potential loss of data or transactions in the process.

Preparation steps

• Assess performance benchmarks before migration.
• Optimize data and infrastructure for the new environment.
• Test performance under various loads.

How to do that

E-commerce website:

• Step 1: analyze current website traffic and performance metrics.
• Step 2: migrate to a scalable cloud service.
• Step 3: optimize images and databases for faster load times.
• Step 4: conduct load testing.
• Step 5: monitor performance metrics post-migration.

Challenge 12: incompatibility between old and new systems

This challenge arises when there is a mismatch in data formats or protocols between the old and new systems.

Cautionary points

• Loss of data functionality.
• Increased costs due to additional customization or software purchase.
• Delays in migration process.

Preparation steps

• Conduct a thorough analysis of both systems.
• Plan for data conversion or integration solutions.
• Test the compatibility extensively.

How to do that

A CRM system in sales:

• Evaluate data structures in both old and new CRM.
• Develop a mapping strategy for data fields.
• Use middleware for integration, if necessary.
• Test with sample data sets.
• Train staff on new CRM functionalities.

Challenge 13: security vulnerabilities

Migration can expose data to security risks, both during transfer and in the new environment.

Cautionary points

• Risk of data breaches.
• Non-compliance with data protection regulations.
• Exposure of sensitive information.

Preparation steps

• Implement strong encryption methods for data transfer.
• Conduct security audits of the new system.
• Train staff on new security protocols.

How to do that

A Healthcare Record System:

• Use encrypted channels for data migration.
• Ensure the new system is HIPAA-compliant.
• Test security measures with mock data.
• Conduct regular security audits post-migration.
• Provide training on security best practices to healthcare staff.

Challenge 14: user adoption challenges

This involves difficulties in getting users to adapt to the new system post-migration.

Cautionary points

• Resistance to change from staff can hinder effective usage.
• Reduced productivity during the learning curve.
• Potential for errors or data mismanagement in the new system.

Preparation steps

• Involve users in the migration planning process.
• Provide comprehensive training and support.
• Gather feedback and make adjustments accordingly.

How to do that

An educational institution’s Learning Management System (LMS):

• Step 1: involve faculty in selecting and testing the new LMS.
• Step 2: offer extensive training workshops for both faculty and students.
• Step 3: create user guides and FAQs for the new LMS.
• Step 4: set up a helpdesk for migration-related queries.
• Step 5: collect feedback post-migration for improvements.

Challenge 15: integration difficulties

This involves challenges in ensuring the new system integrates smoothly with existing systems and workflows.

Cautionary points

• Disruptions in business processes.
• Data silos due to ineffective integration.
• Increased complexity and maintenance costs.

Preparation steps

• Map out all existing systems and their interdependencies.
• Develop an integration strategy that aligns with business processes.
• Test integrations thoroughly before full deployment.

How to do that

Manufacturing ERP system:

• Step 1: assess compatibility with existing supply chain management tools.
• Step 2: develop custom APIs if necessary for integration.
• Step 3: test integration with a pilot project.
• Step 4: train staff on the integrated system.
• Step 5: monitor and tweak the system based on operational feedback.

Challenge 16: data integrity issues

This challenge is about ensuring the accuracy and consistency of data throughout the migration process.

Cautionary points

• Risk of corrupt or incomplete data.
• Potential legal and compliance issues.
• Loss of trust from stakeholders if data integrity is compromised.

Preparation steps

• Implement data validation checks before, during, and after migration.
• Keep backups of original data.
• Regularly monitor data integrity throughout the process.

How to do that

A retail inventory system:

• Step 1: run data quality checks before migration.
• Step 2: use tools to ensure data consistency during transfer.
• Step 3: perform spot checks on migrated data against original data.
• Step 4: keep a rollback plan ready in case of data integrity issues.
• Step 5: train staff on data validation techniques in the new system.

Challenge 17: scalability concerns

This refers to the ability of the new system to handle growth and increased demand.

Cautionary points

• Inability to accommodate business growth.
• Performance issues under increased load.
• Additional costs for scaling up later.

Preparation steps

• Assess future business needs and potential growth.
• Choose a scalable architecture for the new system.
• Plan for scalable storage and computing resources.

How to do that

An online streaming service:

• Step 1: predict future user growth and data volume.
• Step 2: migrate to a cloud infrastructure with auto-scaling capabilities.
• Step 3: implement load balancing to manage high traffic.
• Step 4: regularly review and adjust the infrastructure based on usage data.
• Step 5: ensure content delivery networks are scalable to handle global traffic.

Challenge 18: complexity in data management

This involves the increased complexity in managing data across different systems and formats.

Cautionary points

• Increased time and resources required for data management.
• Potential for data errors and inconsistencies.
• Difficulty in extracting actionable insights from complex datasets.

Preparation steps

• Implement robust data management tools and processes.
• Train staff in data management and analysis.
• Regularly review and simplify data management workflows.

How to do that

A marketing analytics platform:

• Step 1: consolidate data sources into a unified format.
• Step 2: use data integration tools for seamless management.
• Step 3: implement data quality checks.
• Step 4: train marketing team on data analysis in the new system.
• Step 5: set up regular data audits to identify and address complexity issues.

Data migration best practices

So what does the data migration process look like and what are the data migration best practices?

Data migration process

To make sure that data migration will be smooth and seamless, the process should contain the following steps:

Planning

• the evaluation of existing data sets,
• a careful analysis of both source and target systems,
• defining the data migration project scope,
• the development of data standards,
• deciding on a strategy for data migration,
• preparing the budget, schedule, and timeline.

Data inspection

• analyzing the type and size of data, the operating source, target systems, and the database platform,
• determining what kind and how much data needs to be migrated,
• inspecting the data in terms of anomalies, qualities, and potential duplication,
• clearing the data and preparing for transfer.

Data backup

• preparing the backup of the data that needs to be transferred to protect it against loss due to migration failure.

Migration design

• deciding on the structure and flow of the entire migration process,
• introducing migration testing procedures,
• defining roles, responsibilities, and approval criteria,
• hiring data engineers to take care of the process (if necessary). 

Migration execution

• performing data extraction and transformation, 
• monitoring the process to detect potential failures.

The lengths of this stage may vary depending on the scope and data volume and the chosen strategy.

Audit and documentation

• auditing the migration process to make sure it was performed successfully and completely,
• shutting down the old system,
• preparing the documentation of data migration.

<span class="colorbox1" fs-test-element="box1"><p>Read also: Don't believe the hype. Microservices aren't always better than monolith. Plus, there are also other options.</p></span>

Data migration - next steps

So how to prepare your organization for data migration? The process should always be carefully planned and preceded with the right definition of project scope, schedule, budget, and risks. This approach will help to avoid extra costs, exceeded deadlines, and business loss.

If you’re not yet sure how to plan the data migration process on your own, feel free to contact us. We will help you navigate through data migration challenges and assist you directly with our advice and data migration best practices.

FAQ - data migration challenges and risks

What is data migration?

To put it simply, data migration is a process of transferring data from one system to another. It typically involves transferring information between different data formats and applications.

There are various reasons behind the need for data migration, such as:

• legacy system modernization 
• expansion of system and storage capacities
• migration from local storage to the cloud
• implementing new systems and processes
• technology updates
• data center relocation
• website and IT infrastructure consolidation
• aligning tech and business strategies
• improving the software delivery process as well as internal processes.

What are the typical stages of data migration?

The typical stages of data migration are:

• Planning: Defining the migration strategy, scope, resources, and timeline.
• Data analysis: Assessing and understanding the structure, quality, and dependencies of the source data.
• Design: Creating the migration architecture, including data mapping and transformation rules.
• Data extraction: Extracting data from the source system in a suitable format.
• Data transformation: Converting, reformatting, or cleansing the data to fit the target system's requirements.
• Data loading: Importing the transformed data into the target system.
• Testing and validation: Checking for data integrity, quality, and functionality in the new system.
• Monitoring and support: Providing ongoing support and monitoring to address any post-migration issues.