Data migration and integration are frequently confused since they both deal with organizing and clarifying data. They protect your data against leaks and help you manage your business and make informed decisions. However, they are two separate processes that utilize different tools and methods, each with a different purpose.
In this article, we take a closer look at data integration and data migration. You’ll learn the difference between data integration and data migration and discover how to take advantage of both.
What is Data Integration?
To kick things off, let’s define these two processes. So, what is data integration? Simply put, data integration is a process of gathering data in one place from many sources, unifying and standardizing it. It allows you to establish data governance and increase data quality.
This is usually done when a corporation has a massive amount of data from various sources to process to understand the market and make informed decisions. Naturally, processing data that is scattered across hundreds of data sets, especially when it is not standardized, takes a lot of time. In this case, developers employ a specific method known as big data integration.
The process can be done manually, but data integration tools are typically much more efficient. It helps you preserve the accuracy, consistency, and value of the data.
Data integration techniques include batch processing, real-time data integration, and hybrid approaches. The repository you get from data integration is called a data warehouse.
What is Data Migration?
Data migration is a slightly different beast. The process also involves moving data from one place to another, but it does not mean consolidating or standardizing it. It is a simple data transfer, typically done to move to a more modern database or system.
For example, you could move data from an on-premise database to a cloud-based one. Or you could move from legacy systems to new ones to leverage new functionality, security updates, and efficiency.
Data migration can also be done manually, but using tools like APIs, scripts, and software can be better. It is especially challenging to move large amounts of data or transfer it to a system that is vastly different from your initial one.
Difference Between Data Migration and Data Integration
There are many differences between data migration vs. data integration. While they both deal with transferring data, the various factors differ vastly.
Purpose
The main reason for data integration is to consolidate and standardize data. It allows entrepreneurs to analyze data faster and make better-informed decisions. Integrated data has better governance, quality, and structure.
When we compare data migration vs. integration, we see that it can have the same end goal but would be reached differently. The data has to move to a storage with more functionality and faster infrastructure.
But this might not be the only reason for migration. For example, if you have an ERP system, you might choose ERP system migration instead of integration because the new platform costs less and has fewer security problems. However, if you just want your data to be more uniform, ERP system data integration might be a better choice.
Frequency
It is vital to consider the frequency of data integration vs. data migration. Integration is a continuous process: you are never truly done. Thankfully, data integration solutions can automate the process to make it barely noticeable. On the other hand, migration is a one-and-done thing, but it can take lots of time, especially if done manually.
Process
The process of both migration and integration differs.
Integration looks like this:
- Data discovery and mapping
- Data cleansing and validation
- Data transformation
- Data loading
- Testing and quality assurance
- Data Governance
- Data maintenance
Migration starts similarly but then veers off in a completely different direction:
- Planning and preparation
- Data cleansing and validation
- Data conversion
- Data loading
- Testing and quality assurance
- Data cut-over and go-live
- Data archiving
Regardless of your choice, a data integration or migration solution can make the process faster and easier.
Use Cases of Data Integration
To better illustrate the differences, let’s look at use cases of data integration and migration, starting with data integration:
- Creating a single unified database. Consolidating and standardizing data from multiple sources allows organizations to create a single data warehouse with proven information for more accurate reporting, analysis, and decision-making.
- Improve data quality. Data quality increases thanks to data cleansing, enrichment, and standardization.
- Strengthening data security. Having all data in one place can help with data security by limiting access to the database to certain columns for specific groups or people.
- Merging with another organization. If two organizations merge, they may also want to combine their databases. But they’re not always compatible, so data integration might be in order.
- Empowering decision-makers. Having high-quality data helps key decision-makers do their job better. They can use it to improve customer experience, supply chains, or any other aspect of the business.
And in the other corner, we have typical use cases for data migration:
- Upgrading or replacing an old system. Moving data to a more sophisticated platform can have many benefits, from better performance to stronger security and a more comprehensive selection of functions.
- Migrating to the cloud. Cloud-based storage has proven advantages in cost-efficiency, security, and scalability, so moving to the cloud might be a good reason for data migration.
- Archiving. If your business handles tons of data, some of it may become outdated at some point. Deleting data may not be the wisest decision, so moving it to an archive storage for potential future use is a great solution.
- Strengthening data security. Like with data integration, you can use migration to enhance your security, but this time by choosing a platform with better security measures.
- Merging with another organization. If two organizations merge and are lucky to work with the same infrastructure, you could simply migrate data from one database to another without any additional changes.
- Crisis management. It is always a good idea to keep secondary data storage in case something happens with your primary one.
- Empowering decision-makers. Some systems are better suited for data analytics and BI, so your platform choice might influence the capabilities of those specialists.