Mastering Data Migration: Risks, Challenges, and Proven Strategies for Success

Businesses that want to become efficient, effective in their decision process and relevant in the current market must necessarily consider data migration as a top priority. It allows them to implement higher forms of technologies, gather all its data, and produce accurate insights in real-time. In this blog, we would discuss about importance of data migration, problems of isolated data, and what strategies are available for enterprise data migration. This blog will also compare data migration with data integration and data replication, the primary data migration risks to consider and how to manage them to enhance the chances of a successful migration process. 

What is Data Migration?

Data migration is the process of moving data from one place to another, or from one format, technology or database to another. The primary goal of data migration is to keep the data accurate and secure during the migration process. Data migration is most commonly executed during the process of system upgrade or when organizations are consolidating databases such as MySQL or PostgreSQL or trying to switch over to new methods of data management where organizations are changing from traditional local based systems to cloud based systems. Data migration can include steps such as data extraction, cleaning, transformation, and loading (ETL) to guarantee the given data is formatted suitably for reception with the new system.

What is the difference between Data Migration, Data Integration, and Data Replication?

Data migration, integration, and replication are used interchangeably although they have distinct functions in the management of business information.

• Data migration refers to moving data from system-to-system, application-to application, and storage-to storage. It often happens when a company implements or integrates new technologies into its infrastructure.
• Data integration is the act of integrating data derived from multiple sources into unified view. This process also facilitates the integration of different systems in an organization.
• Data replication is the copying of the data from one location to another location, often in real-time. This provides backup of data which can be used for disaster recovery.

What are Data Migration Types?

Data migration can be categorized into five types:

• Storage Migration is a process of relocating data from one storage to another typically in response to Storage Upgrade or Storage Consolidation often as part of an upgrade or consolidation effort.
• Database Migration means transfer of data form one database platform to another platform. Data from one database system to another, often as part of an upgrade effort or cloud adoption.
• Application Migration is the process of moving data between two software applications.
  This takes place when businesses switch to new software solutions or cloud-based applications.
• Cloud Migration is the act of moving data from onsite systems to cloud systems. This is so popular to adopt scalable, cost-efficient and flexible systems in an organization. 
• Business Process Migration addresses one of the critical areas in organization transformation where data and the set of processes are migrated between different systems.

Top 10 Data Migration Risks and How to Avoid Them

Data migration forms a critical part of a business strategy in records management and comes with several challenges that if poorly handled can cause disruptions in business processes. The following are the ten most common data migration risks, and ways of reducing such data migration risks:

1. Data Loss

Risk: 

• One of the leading data migration risks during migration is the loss of data. Errors in the process, corruption or partial transfers may result in loss of data that are devastating to a business.

How to Avoid: 

• You should back up all your essential data before performing the migration process. 
• After migration, data integrity is also important to verify that the data has remained the same as compared to the original data.

2. Data Corruption

Risk:

• When the data is being transferred, data corruption occurs because of distortions or alterations making the files useless.

How to Avoid: 

• Only use reliable tools to move or transfer data with support for checking errors beside the data. 
• Use checksum validations to ensure the data is not corrupted before, during and after its migration. 
• Performing the migration in phases also can contribute to identifying corruption. 

3. Downtime and Disruption

Risk: 

• Data migration is a time-consuming process and can result in a high amount of downtime or operational loss – not ideal for companies who are heavily reliant on data.

How to Avoid: 

• Migration should be done during off-peak hours in order to reduce the amount of time spent with no operation. 
• Employ gradual approach in order that critical systems may be maintained operational during the transferring of data. 
• Cloud-based migrations or the hybrid implementation models also offers possibilities of the reduction of the downtime.

4. Incompatibility Between Systems

Risk: 

• Migrating data that are stored in a previous database whose structure does not support the previous one.

How to Avoid: 

• Considerable analysis should be done on the source and target systems before migrating. 
• Find the target system has the capability to correspond to the data types, structures and formats in existence. 
• Data can be transformed into the structure of the target system using transformation tools or services.

5. Security Risks

Risk: 

• Sensitive and confidential business data can be breached, accessed by unauthorized people, or even lost.

How to Avoid: 

• Use strongly encrypted algorithms when implementing your migration process.
• Make sure that only people who are supposed to have access to the information are allowed to do that.
• Use secure transfer methods like VPN or transfer specific migration networks. 
• Conduct a security audit prior to, at the time of, and after the migration process.

6. Cost Overruns

Risk: 

• Data migration is usually expensive based on time and may escalate more than the expected cost.

How to Avoid: 

• Set a clear financial plan and time frame when you start with the migration project. 
• Dispel any data migration risks and include costs that may be a bit hard to project since they bring about contingency money. 
• Stakeholders should be informed of any development that might impact scope, timeline, and costs.

7. Loss of Data Quality

Risk: Data that is moved from one system to another undergoes some quality loss.

How to Avoid: 

• Conduct data cleansing to get rid of such data as obsolete, useless and incorrect data.
• Companies need to put mechanisms that uphold quality in data handling and managing in the process of migration. 
• Enhance the degrees of observation by the parameters and compare the results of the migration with the standards.

8. Lack of Skilled Resources

Risk: 

• Qualified personnel are needed as there are many crucial issues and differences that show the source and target systems.

How to Avoid: 

• Make sure you have the right team of people to do the migration – people who really know what they are doing. 
• Deliver training or materials so that team members can meet with the migration’s demands. 
• Hire a third-party consultant especially one who has been trained on how to migrate data.

9. Unforeseen System Errors or Bugs

Risk: 

• With the migration to this new system, there are many issues that may cause problems such as software bugs, the system may slow down or some other application may interfere with the new system.

How to Avoid: 

• Carry out thorough tests before migration to determine the problems in the new system. 
• Perform migration on a small subset of data first then follow it with a full migration of all the data. 
• Align the operation of the new enterprise system with the current organizational configurations of the structural foundation and organizational business models.

10. Post-Migration Performance Issues

Risk: 

• New system’s performance may not be as desired. Some issues such as data may not be as accessible, queries may take longer to be executed, or even if the reports are inaccurate.

How to Avoid: 

• After migration the performance testing should be carried out to facilitate the determination of how well the new system is performing. 
• Over some time of migration, they should observe its performance and make corrective measures that may be needed. 
• Prepare post migration support and a troubleshooting matrix for cases of poor performance. 
• Continuous monitoring and enhancing of this new system will assist to avoid the degradation of their efficiency in the future.

Tips to Mitigate Data Migration Risks

1. Thorough Planning: To carry out the migration effectively one has to start by clearly identifying the objectives of the migration as well as the area of coverage. List possibilities of negative events and develop responses in case of their occurrence. Creating a detailed timeline of migration process.
2. Data Backup: Before carrying out the migration, ensure that you do a full backup of all essentials data that may be needed in the future.
3. Testing: Conduct thorough testing at each stage of the migration. Run pilot migrations with a small subset of data to identify potential issues early.
4. Use the Right Tools: As the process is long and complex, it is advisable to carry out testing at every stage of the migration. It should be possible to run pilot migrations with only a small amount of data to spot any problems.
5. Expertise: Use efficient and accurate data migration tools that can easily detect errors, encrypt the data and are compatible to the industries standard.
6. Monitor and Validate: It is required to implement permanent control regarding migration process to conform data and systems’ coherent. Another important step is post-migration validation that will also make sure that the migrated data is accurate and of high quality.

Conclusion

The process of data migration is fundamental, but it’s risky and a critical one since it may harm business, if not well implemented. Only when IT project managers are aware of the data migration risks that can occur such as data loss, software incompatibility, and system security issues they’ll be able to prevent these challenges. Testing, secure transfer mechanism, skilled manpower, and post migration analysis are the basic requirements for the successful migration. In conclusion, when performed to perfection, data migration benefits not only increase its operational capabilities but also leads to business development as the firms can get better access to the complicated technologies and gain better data insights to ensure the long run success and competitiveness.

There’s a wide range of migration tools available in the market to relocate your data depending upon the use scenario. The right data migration tool such as Hevo Data will help you move data from source to destination.

Take Hevo’s 14-day free trial to experience a better way to manage your data pipelines. You can also check out the unbeatable pricing, which will help you choose the right plan for your business needs.