4 Common Data Migration Problems and How to Resolve Them

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Business data is an essential asset regardless of the type of your business. You need to store customer information and employees’ data accurately. You can store your information on a computer, a database, or a dedicated data center. Regardless of the technology you’ve deployed for data storage, technology evolves and will soon become outdated. If you’re using legacy computers, software, or data centers, you may need to leverage modern technology for efficiency and security and migrate your data.

There are different types of data migration: database migration, data center relocation, application migration, and business process migration. Occasionally, you should perform a data center audit to ensure that it’s at the optimum working capacity, or it still meets your evolving business needs. A checklist for data center audit should look into security, quality control, cooling and power efficiency, operational performance, and energy efficiency, among other aspects. If your data center no longer guarantees this performance checklist, you’ll have to establish a new data center and migrate your data to the latest one.

However, beware that you may face some data migration problems. Here are some of the issues:

1. Data Loss

Some of your data might be lost in some transfer instances, especially when you’re transferring your data from legacy systems. Some legacy systems may not be secured with seamless transfer procedures for safe data migration. You should know that data loss is probably the highest challenge you might face during data migration. The financial implication that you suffer from data loss could be very high. If you lose your customer data, it’ll affect your marketing efforts, which, in return, leads to low sales.

To secure your business from such a scenario, you can carry out a count reconciliation. The count reconciliation counts the number of records transferred to the target system from the legacy system to ensure that the number is the same.

However, suppose during the data migration, you imposed some rules to restrict the transfer of certain records. In that case, you should check whether the transferred and untransferred record numbers match with the count in the legacy system.

2. Data Corruption

Corrupted data is different in both the legacy and target systems. Data in the target system may have some anomalies, redundancy, or duplications leading to integrity issues during the data migration. Data integrity issues and corruption will affect business operations.

It would help if you did data validation between the target and legacy system to solve this problem. There are different kinds of data validation:

• Sample Data Validation: This validation rule involves picking a random record from the transferred data and comparing it with the original data. However, this method isn’t entirely effective since some unvalidated records might have data problems.
• Subset Data Validation: With this strategy, you pick a subset of the whole data, such as the first 10,000 records and compare the original data with the transferred data. As the sample data validation method, it’s also not error-proof.
• Complete Data Validation: It’s the safest method of data validation. Here, you test every record from the legacy system with the destination system for anomalies. If no abnormalities are detected in the entire data set, your data is clean.

It’s advisable that when you’re doing data validation, consider the execution time, efficiency of the queries, stability of the process, and data coverage. The broader the scope of the data validation, the better the data will be. However, always strive to do complete data validation.

3. Semantics Problems

Usually, after data transfer, the meaning of the records in the destination should be the same as that of the original records. However, sometimes the data might develop semantic issues. This could happen due to data loss or when your data has been corrupted.

To guard your data from this issue, the subject matter experts and real-time users should do a feasibility study to detect the semantic problems before your data is used after migration. When doing testing, the scope of the test should use test cases to see incompatibilities and inconsistencies in the two sets of data.

4. Data Mapping Challenge

You should know where to store your transferred data in the new database during data transfer. Some legacy systems may foster data mapping challenges, especially when moving data into a more sophisticated knowledge base system. However, if you work with reputable experts, they’ll specify how your data should be mapped so that you can transfer it straightforwardly.

Final Thoughts 

At some point in the operation of your business, it might be unavoidable to do data migration. Data migration may be done because of the need to deploy advanced systems or because you’re relocating your data center. Whichever the case, you should protect your data from corruption, loss, and developing semantic problems. By following this article, you can ensure the integrity of your data after data migration.

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