Data migration may sound simple to the uninitiated—it’s just moving data, right?—but what might seem self-explanatory from the outside is actually a deeply complex process that requires careful planning. After all, any successful data migration requires watertight security, pinpoint accuracy, and smooth transition. That’s why, for many companies, setting up a data migration strategy and process can be a daunting task. There are so many things that can go wrong.

For lots of organisations, an internal lack of knowledge around data is the first major obstacle. Data literacy is important for adequate data management and to prevent overwhelm when new tools or systems require data to be migrated. Vendor applications and their data structure can often be incompatible with each other and while workarounds can bridge the gap for a while, these are rarely viable as long-term solutions. Data silos often see teams working with incomplete or inaccurate datasets which render meaningful data analysis impossible. The absence of an SSOT (single source of truth) aggregated data system also means that the organisation cannot accurately gauge its data needs due to the lack of a proper overview. This also gets in the way of data mapping because it’s hard to match fields when the criteria vary so widely. The absence of a universal schema can also lead to data duplication and complex validation thresholds.

All of this can make data noisier, harder to work with, and difficult to migrate should the need arise. However, picking the right data migration tools and software can significantly ease the process. So now we know the risks and challenges companies face when migrating data, let’s look at how it can be achieved successfully.

But first, let’s review the basics.

What is Data Migration?

Data migration is the process of moving data between formats, applications, or locations. A company might decide to migrate their data if, for example, they start using a new product that requires different file formats or if they choose to shift from an on-prem data centre to the cloud.

Data migration can be complex and delicate, especially when large quantities of data—Big Data—are involved. It’s rarely simply a question of moving data from A to B. The entire data migration process involves a series of steps where teams need to select, prepare, extract, and transform data before permanently moving it between locations or apps. That’s why having a data migration plan from the start is essential.

What’s the Difference Between Data Migration and Data Integration?

Data migration and data integration both involve moving data, but differ in fundamental ways. Data migration is the process of repackaging and moving data and data integration is the process of sharing data between systems. Migration tends to be a one-way journey, whereas integration allows data to flow back and forth. Integration can be important in verticals like e-commerce where many simultaneous processes require data to move flexibly between different internal systems.

What About Data Migration Versus Data Replication?

Data migration is a one-time process where data is transformed and moved from one place to another, as in a database migration. Data replication is when you copy the data source at a particular point in time without altering the original. This allows you to have several working copies of the same data available at once. This supports seamless access to data without slowing down servers or other users’ access. It also means that multiple users can work with the data which can then be synced to update at the source.

How Are Data Migration and Data Conversion Different?

The key difference between data migration and data conversion is that migration transfers while conversion transforms. Data migration moves data from an origin point to a destination, whereas data conversion is the process of extracting and transforming data into a desired format.

The 2 Most Common Data Migration Strategies

As with any iterative process, there are many different approaches to data migration. However, most organisations will opt for one of two more popular data migration strategies, depending on their specific internal needs. Let’s have a look at each one and their main differences.

Big Bang Migration

As its name suggests, Big Bang Migration happens all at once. While it’s appealing to many organisations to get all data migration steps completed during a period of planned downtime, the company needs to be in a position where it can absorb the effect of having its main systems offline for a period of time during the ETL (extract, transform, load) process. This adds pressure and increases the speed requirements of the data migration process. While all data migration strategies need to be planned out in advance, it’s of utmost importance that Big Bang data migration goes through a dry run so that teams can iron out any crinkles in the system before they undertake the real migration.

Trickle Migration

For organisations that can’t operate with data systems offline for any period of time, trickle migration might be a better option. As this data migration strategy runs in phases, both the old and new systems run in parallel until such time as the data migration is fully completed. This means downtime is limited and migration can run continuously for an extended period of time. Trickle migration adds complexity given that it happens over a longer period of time and requires multiple systems to run at once. Planning each step in advance is essential to minimise risk and ensure smooth migration.

Step-by-Step Best Practices for Data Migration

Regardless of which strategy you choose, there are some data migration best practices that should be observed. Knowing exactly what you’re working with ahead of time will lessen the risk of error and make sure that you can feel confident in the approach you’ve chosen.

So let’s run through a few data migration best practices that you should bear in mind.

• Plan the migration carefully.

Planning is the name of the game when it comes to data migration. As data security is key, you need to know exactly how you’re going to migrate your data, when you’re going to do it, what potential risks are, and how you plan to mitigate them.

• Clean up the data before migrating.

As if you were moving to a new apartment, it’s important to sort and clean before you move from one place to another. Cleaning up data in advance saves you a lot of work in the long run, and increases your chances of a smooth, successful migration. Instead of running into unexpected roadblocks, you’ll know exactly what data you’re dealing with and how to best approach it.

• Test the migration process.

Running through the process before you start is also an important data migration best practice. Testing the migration process allows you to plan more wisely, understand the advantages and limitations of your data migration software and tools, and can help you plan your workload. You wouldn’t fly in an untested aircraft, so don’t take the same level of risk with your data. To test the migration process, begin with a small batch of data and monitor its progress closely. When you feel confident that everything works smoothly, you can scale up your approach to migrate the whole system.

• Migrate data in small batches.

Even with Big Bang data migration which happens during a planned one-off window, it’s generally considered smart to migrate data in small batches. This means that if something does go wrong, it will be easier for you to find and fix the problem without risking the whole system or adding unnecessary frustration or risk to the data migration process.

• Monitor the migration process.

Once your planned data migration steps are running, monitoring progress is key. As the process is complicated, you need to be on-hand and aware so that you can step in if it throws errors. If you have properly cleaned your data and planned your process ahead of time, problems should be minimal.

• Validate the data after the migration.

Data is only useful if it is high-quality, cleverly organised, and efficiently maintained. Therefore, data validation after the migration process is completed is an important step that must not be overlooked. Unvalidated data is useless as good data analysis needs accuracy.

Why nuvo might be the data migration tool you need

We started this article by outlining the challenges that companies face when contemplating data migration. As you now know, successful data migration needs a lot of planning and a smart choice of tools. So set yourself up for success and eliminate any potential frustrations by choosing the right tools and getting it right from the very first step.

nuvo can make your data migration faster and easier while reducing cost and the likelihood of mistakes. AI-supported column matching makes data import and mapping a breeze. Advanced data validation alerts users to errors that need to be corrected before they become a problem. Automate data cleaning and enable easy transformation to the format your system requires. Getting your data right from onboarding will make it easier to work with, make analysis more meaningful and enable seamless migrations whenever you need them. Contact us to find out more.