Until it’s a problem, no one pays much attention to data migration.
The list of potential issues with migration records can get quite long, from missing records to duplicate entries, to broken integrations, to unforeseen delays.
Manual workflows are getting increasingly difficult to work with as businesses transfer greater amounts of data between platforms.
Machine learning is transforming the way organizations are tackling migrations by early detection of problems, efficient data cleansing and faster project completion.
AI can help companies minimize migration errors from the get-go, rather than having to spend a few weeks fixing them later.
Why Data Migration Causes Delays
Many organizations think that the toughest part of migration is getting data from one system to another. The quality of the data, in fact, is often the greatest challenge.
Older systems often have years of duplicate records, inconsistent formatting, and/or incomplete information. The problem could be something you are not aware of until it becomes an issue when migrating.
A customer profile that is stored in different databases, such as, can cause mapping issues and reporting inaccuracies. These issues become apparent later in the project are likely to lead to delays in the project.
The Problem With Manual Data Checks
The history of migration teams is that they are used to manually reviewing data to prepare for transfer. That’s fine for smaller data sets, but it’s more challenging when dealing with millions of records.
There are now many organizations looking for help in migration planning, and with the risks involved in large-scale migration projects, they are looking to AI tech consulting services.
Machine learning tools can process large amounts of data much faster than humans would take.
Small Errors Spread Quickly
If there is one wrong mapping rule, it could impact thousands of records.
They could be a formatting or field problem that doesn’t seem to matter at first glance, but will cause problems when the data is moved to its new home.
Often the effort required to find and correct those errors after migration will be much more than the effort originally required to prevent the errors in the first place.
What Machine Learning Spots in Seconds
Pattern recognition is one of the most powerful attributes of machine learning.
AI systems can review extensive information and pinpoint problems that a human might not be able to see. Machine learning analyzes data sets as a whole instead of one record at a time.
Can identify anomalies, missing information, and inconsistencies among related sources of information.
This provides migration teams with a better understanding of possible risks in advance of data being moved. It often makes sense to see the first signs as this helps to plan accordingly and avoid surprises later in the project.
Cleaning Data Before It Becomes a Problem
Another of the most common reasons migration projects fail is due to poor data. Copying incorrect data to a new platform is just shifting the existing issues to a new place.
Machine learning can enable teams to enhance the quality of data by identifying:
- Duplicate records
- Missing values
- Invalid contact details
- Formatting inconsistencies
- Unusual data patterns
Teams can concentrate on resolving only those exceptions that truly need human effort, rather than spending hours on record review.
The fewer flaws in their data quality before migrating, the fewer fixes they’ll need to make after it’s moved, and the smoother the migration process will be.
Faster Projects Without Cutting Corners
Effortless and quick projects without compromising quality.
It’s all about speed, but accuracy is more important. For most organizations, the challenge is to improve without compromising the other.
Machine learning can help with this by automating those tasks that require a lot of work.
With AI, data validation, mapping recommendations, anomaly detection, and data quality checks can be done significantly faster.
This means teams waste less time on finding problems and more on advancing projects. This efficiency typically means that the project’s costs are lowered and that deployments are achieved on time more often.
If there are large migrations, any time saving can make a big difference in the entire project’s success.
Conclusion
Data migration projects get more complicated with increasing data size and greater system integration.
Manual review and traditional methods can still be used, but can be hard to catch up with modern needs. Machine learning offers a viable solution to reducing error rates, enhancing data quality and shortening migration timelines.
Although human intervention is still needed, AI streamlines the team’s workflow and aids decision-making across the migration process. That mix is the new benchmark for an effective data migration for many organizations.
Frequently Asked Questions
Is it possible to avoid all migration errors?
No. AI can minimize risk and help to surface many problems early on, but there is still a need for humans to review the results and make business decisions.
Can machine learning be implemented with legacy systems?
Yes. There are some AI tools that are specially created to review data from legacy systems prior to migration.
Can AI be helpful for smaller migration projects?
Yes. Even for smaller migrations, faster validation, better data quality, and less manual review work can be a benefit.
What is the best feature of AI for migration?
Ability to detect data quality problems and potential risks earlier than with traditional manual data quality practices.
