Data Migration Challenges and Solutions for 2024

In 2020, the world contained 44 zettabytes of data. It has been projected that by 2025, global cloud storage will hold more than 200 zettabytes of data, with 463 exabytes created daily. Given this huge amount of data, it is vital to store it properly for optimum usage and retrieval.

Data migration is moving data from one system, database, or storage type to another. It is essential for businesses to keep in mind the various data migration challenges and keep looking to upgrade their systems or consolidate data centers to ensure better data management and data quality.

10 Data Migration Challenges and Solutions

A recent study by Gartner shows that 83% of data migration projects either fail or exceed their budgets and schedules. Cost overruns average 30%, and time overruns happen on an average 41%. In this article, we will cover the major data migration challenges and how we can mitigate these for a smooth migration process.

Challenge 1: Data Security and Compliance

• Risk: Most businesses contain data that might be sensitive, personal, or confidential. Lack of safeguards could lead to data loss through theft and breach various laws governing data migration. Breaches during data migration can compromise sensitive information, leading to legal action and loss of customer trust.
• Solution: To avoid security breaches, you should improve the encryption method used to secure the data and monitor the identity and access control. You must also perform compliance tests throughout the migration to protect data and ensure legal compliance.

Challenge 2: Data Integrity and Accuracy

• Risk: Inaccurate or inefficient migration processes may cause migrating data to fail to capture all details from the original sources. When data is migrated, data corruption or loss will naturally compromise it and make it unreliable. Using this incomplete and inaccurate data, you will likely get wrong results, leading to potentially wrong business decisions.
• Solution: To manage data consistency during data migration, you should use validation checks, compare the schema, implement data quality checks, and use rigorous quality assurance measures.

Challenge 3: Downtime and Business Continuity

• Risk: In most data migration cases, the systems have to be turned off during the process. This may mean that the system is down for quite some time, which will, in turn, affect the business and its customers, leading to revenue loss.
• Solution: One way to reduce system downtime is to schedule the migration activity at times when business usage is not very high. You can also use parallel systems to maintain business continuity during the migration.

Challenge 4: Legacy Systems Compatibility

• Risk: Another risk associated with old legacy systems is that they may experience compatibility issues with modern platforms, making the migration process more complex. If one tries to migrate with older hardware and software, it might result in loss of data or corrupted files.
• Solution: The system and processes should incorporate the latest software updates and cloud-based data. You must also address the compatibility issue by implementing middleware or custom scripts to provide an interface to integrate the new platforms with older systems.

Challenge 5: Data Mapping and Transformation

• Risk: If the existing data formats or database are not compatible with the new data, critical information may be lost due to inaccurate data mapping or transformation. This can provide incorrect results, ultimately affecting the business outcomes.
• Solution: A detailed plan should be developed for data mapping and transformation to ensure compatibility between the data and the transformations. At times, data cleaning or mapping might be necessary to maintain data integrity and quality. You also need to perform extensive testing to ensure that the data translates correctly into the new environment.

Challenge 6: Handling Large Data Volumes

• Risk: Migrating large volumes of data may result in long downtime and potential data loss. We need to be more cautious when selecting which datasets should be migrated and which should remain as is since excessive volumes can lead to increased testing and execution times and additional costs.
• Solution: Knowledge of the data and its business understanding is crucial when undertaking the migration process. You should use highly scalable migration tools and plan the migration in stages to avoid data loss or incompleteness.

Challenge 7: Managing Migration Costs

• Risk: Data migration costs depend on the scope of the overall application, including the number of workloads to be migrated and their complexity level. Sudden and unforeseen complications during the migration can cause huge costs over and above the funding that was set aside initially for migration.
• Solution: To solve this problem, we must plan adequately before initiating the data migration. There should be provisions for contingencies, and the migration tools should also be cost-efficient and reliable to avoid the overall cost exceeding the budget.

Challenge 8: Change Management and User Adoption

• Risk: Sometimes, the users resist data migration due to their unwillingness to work with unfamiliar methods and tools, which may result in low usage of the new platform. This may result in user dissatisfaction and reduced efficiency after the migration process.
• Solution: To address this, stakeholders’ involvement in the transition process is essential for an easier migration. We must provide extensive training and support, integrate the new system with the user requirements, and offer adequate assistance to facilitate the transition for the system’s end-users.

Challenge 9: Integration with New Technologies

• Risk: For traditional systems, data migration can be relatively cumbersome and time-consuming, mainly due to the lack of adequate support and documentation or the need to transition legacy systems. Another problem that may occur is compatibility issues when transferring data from old systems after implementing newer and more advanced technologies, which could lead to system failures.
• Solution: Middleware should be implemented to ensure adequate testing and quality assurance. This will ensure proper working between the new and old systems. In addition, sufficient documentation of the migration process, especially the scripts, configurations, and other changes made during migration, should be provided.

Challenge 10: Post-Migration Audits and Optimization

• Risk: Data errors or performance issues can occur after the migration process. If left unaddressed, they may slow down business processes and adversely affect the performance and quality of the new system.
• Solution: You must ensure that the audit process is well planned and look for the most efficient ways to improve the system to avoid future mishaps. You should also implement post-migration audits and routine system health check-ups frequently to identify and resolve post-migration issues and maintain the systems’ performance and data quality.

Best Practices for Data Migration in 2024

• Data Governance and Compliance: It is important to have a well-established data governance framework to check on data quality, privacy, and security compliance.
  • This means that organizations must keep track of data lineage and metadata since they aid in creating transparency in the processes.
  • Organizations must ensure that they comply with legal standards, like GDPR for EU data.
  • Data handling strategies have to be reviewed regarding data ownership, data storage, data retention, and archival policies.

• Continuous Monitoring and Risk Management: To control the data flow and ensure security measures, organizations must establish a real-time monitoring system to detect data anomalies and address vulnerabilities.
  • A risk mitigation plan should be developed at the beginning of the project to avoid potential risks.
  • We should have regular security audits, as they can predict potential issues that may compromise the integrity of the data in the future.

• Post-Migration Validation and Testing: When data migration is done, it is necessary to have rigorous post migration validation and testing.
  • To do this, we may use automated and manual testing tools to verify the integrity, accuracy and functionality of the data in the new environment.
  • There has to be a thorough testing strategy with a reference list of test cases to guarantee the business value of migrated data and compatibility with current systems.
  • A continuous feedback loop should be in place to ensure that if a problem must be addressed, it is solved immediately.

How Can Hevo Help You from Data Migration Challenges?

Hevo is a real-time ELT No-code Data Pipeline that can assist you in integrating data from multiple sources and loading it into a location so that a BI tool can analyze real-time data. It guarantees effective and error-free data transfer between systems, simplifying your data migration process.

Hevo would be a great Data Migration tool for your use case for the following reasons:

1. Hevo helps with data migration to Snowflake, Databricks, BigQuery, and other contemporary data warehouses. It also supports more than 150 data sources for data migration.
2. Hevo has extensive features, including automation capabilities, error handling, monitoring, and an easy-to-use interface. Moreover, it allows automatic schema mapping, embedded DBT modeling, in-flight data transformation, and more.
3. Hevo is very focused on security and compliance. Some security features it provides are access control, compliance checks, and data encryption at both transit and rest.
4. Hevo’s engineers offer round-the-clock assistance. There is always someone available to assist you in times of need.

Learn More About:

Cloud Migration – Process, Types, and Strategy

Conclusion

Data migration is a tedious and challenging process but can be vital if you want to upgrade your infrastructure or data management process. Potential challenges include data loss and downtime, compatibility issues, and security concerns. Nevertheless, if properly planned and implemented, these challenges can be easily overcome with the help of the right tools.

One tool used in the data migration process is Hevo. Hevo does not require coding knowledge and is a fully automated solution, so you can focus on data migration without stressing technical aspects. Its stable infrastructure guarantees data consistency, allows real-time data transfer, and ensures that security levels are not compromised during migration. Thus, with the help of Hevo, businesses can transfer data without getting lost in the migration technicalities and instead focus on the value they can derive from the data.

FAQ on Data Migration Challenges