How AI Can Streamline Data Migration

Whatever the reason, data migration can be a complex, time-consuming and headache-inducing project. The process is further complicated by growing volumes of data stored on a wide range of platforms, both on premises and in the cloud. Diverse data sets and format inconsistencies increase the risk of error.

To be successful, data migration should start with an in-depth assessment of the data and careful analysis of the safest way to move data from the old system to the new. There are AI-enabled tools that can simplify and streamline data migration, but extensive planning is still required to prevent data loss and minimize disruption.

Data Migration Challenges

Discrepancies in data formats represent one of the most challenging aspects of any data migration. The source and target systems can have different formats, architectures and encoding standards. IT teams must develop rules for converting data to meet the target system’s requirements, a complex and error-prone process.

Many organizations already struggle with data quality problems, which can be exacerbated by data migration. Inaccurate, incomplete and duplicate data can cause problems with the target system. As a result, data migration should include efforts to cleanse and verify data to ensure that it meets both technical and business requirements.

IT teams must also take steps to ensure data privacy and security, particularly when migrating data to or from the cloud. APIs should be secured and data should be encrypted during transit and at rest in the target system. Otherwise, the exposure of sensitive data could lead to regulatory compliance violations.

How AI-Enabled Tools Can Help

AI-enabled tools can automate much of this process. AI is adept at analyzing data, and can perform transformations with much greater speed and accuracy than humans. It can also identify inconsistencies in real time and handle the task of standardizing and cleansing the data to improve accuracy and streamline integration.

Some AI tools are able to “learn” as they go, assessing the data to predict challenges. They can adjust schedules and optimize the migration process in real time to minimize downtime. AI can also monitor for security threats to ensure the privacy of sensitive data.

The Role of Synthetic Data

A key benefit of AI is its ability to generate synthetic data, which simulates the properties of datasets without the need to use the data itself. Synthetic data is often used to train machine learning models and test systems, particularly when real data is difficult to obtain or there are security and privacy concerns.

In the data migration context, synthetic data can be used to test data transformation and mapping rules to reduce the risk of errors and other issues during migration. IT teams can generate diverse data sets to test various scenarios. When it comes to the actual migration, the data is more likely to import seamlessly into the target system, minimizing the need to adjust the process midstream or perform extensive manual work. Synthetic data also helps preserve the integrity of the source data during the migration process.

Planning Still Needed

Despite its benefits, AI introduces a new set of challenges. IT teams will need to standardize data formats and integrate AI tools with existing infrastructure. They will also need to test and validate the AI model to ensure it will work reliably and accurately. All of this requires technical expertise that many IT teams lack.

Furthermore, IT teams will still need to plan thoroughly to ensure the success of the data migration. AI isn’t a magic bullet — it’s a useful tool that can help speed up the process and minimize errors if it’s applied thoughtfully and carefully.

Technologent’s data management team has extensive experience in the planning and execution of data migration projects. Let us help you determine if AI can assist in your data migration, and develop a strategy that minimizes disruption and ensures a successful outcome.