Data migration is the process of transferring data from one storage system, format, or application to another. Oftentimes, data migration takes place to optimize business processes, efficiency, and competitiveness.
As prospect, lead, and customer data are essential to the success of your business, a solid data migration plan is required to ensure that no data gets lost or erroneously manipulated during the migration process.
Data migration might seem like the simple act of moving data from one place to another, but as you transfer data between different file formats or databases, the risk of data loss and the creation of inaccurate data is real.
In this article, we’ll go over the different types of data migration projects and we’ll outline the steps for a data migration strategy that’ll allow you to migrate data in a way that won’t interfere with your daily business operations.
When to run a data migration project
Data migration often occurs as part of a larger business optimization project. Some examples of data migration include:
- The replacement of legacy systems with newer, more efficient solutions.
- Data storage capacity expansion.
- Moving data from on-premises data storage and management to a cloud environment.
- The implementation of a new system to work with an existing system.
- The consolidation of data structures due to a merger or acquisition.
- Transferring data into a centralized database.
What are the different types of data migrations?
There are four main types of data migration. Your migration project can involve one or more of these types.
Exports from customer relationship management (CRM) migration
CRM migration is when a business moves data from one business system to another. This type of migration occurs when a business chooses to either feed data into another system or to use and manipulate its data outside of the source system.
Common examples include moving marketing event lists, purchased lists, or sales spreadsheet data to CRM, or exporting data to create backups in other locations. These types of data migrations revolve around the company’s effort to consolidate information.
Storage migration happens when you move data from one storage location to another. This transfer occurs between physical storage devices with physical blocks of data.
An organization will often begin storage migration when they are upgrading their data storage equipment rather than due to a lack of data space. Storage migration encompasses different types of data movement, such as paper to digital or moving older (but still relevant) information from your CRM, where storage is at a premium, into a data repository.
When a new software solution is implemented, your IT teams need to transfer all of your data and information to that system — that’s database migration. This can be moving from one business system to another or migrating teams off of spreadsheets or other disparate systems onto a more unified business system.
Database migration is one of the more frequently used types of data migration because most organizations are consistently upgrading their software to keep up with competitors.
However, there can be issues when the original data system operates in a different format and model from the new one. In this case, your team will need a data specialist to ensure proper database migration. Most businesses will opt to upgrade their current database rather than the more difficult process of migrating to a new vendor. This is why some companies still choose to struggle with their original database systems.
Business process migration
Business process migration usually takes place during mergers and acquisitions, though it can also occur when businesses seek to enter new markets or need to meet new customer demands.
In each of these scenarios, organizations need to transfer the current data and information into a new system or environment, which is business process migration.
DemandTools maintains data integrity even while moving data into and out of Salesforce.
What are data migration strategies?
If your organization needs to start preparing for a data migration, the first step is determining which approach is right for your project. Your main priority is to ensure that you’re choosing a data migration strategy that will run smoothly and maintain data integrity with minimal to no setbacks.
Below are two of the main data migration strategies:
Big bang migration
This strategy, similar to its namesake, refers to an all-at-once approach: you initiate a complete migration of your data assets in one operation, in the smallest possible timeframe.
As big bang data migration is underway, systems and platforms will be down and unavailable to customers, employees, and any other users. Because of the downtime, it’s best to schedule a big bang migration overnight, during a legal holiday or on a weekend when the data won’t be needed.
This type of migration allows companies to complete data migration in a short time frame, which enables a quick turnaround. Similarly, users won’t have to deal with simultaneously working with both the original and new systems while waiting for the data to be transferred.
However, it does have some drawbacks, including higher costs and an increased risk of failure. As mentioned, while your systems are moving, your users won’t have access to the database, which can also hurt productivity if the organization is unable to schedule the migration during downtime.
Smaller companies, or those with small amounts of data, will probably have the most success with big bang migration.
The trickle data migration approach breaks the data migration project down into sub-migrations. Instead of moving all of your data to the target system at once, the data migration occurs in different stages over a period of time ranging from a few months to a year.
Trickle data migration is also known as phased or iterative migration and comes with many competitive benefits. The biggest one is that it eliminates downtime. Since both the old system and the new one are running during the migration, there is no downtime, and employees and customers can access their data at all times.
Your company can choose two different ways to handle trickle migration.
- Data is migrated to the new environment and then users gain access to its new location.
In this scenario, your migration team is tracking what information is moving to the new system and then making sure that users are still able to access it. This can be complicated for your teams as they work to keep the two systems accurate and accessible.
- Data is migrated to the new environment, but users don’t switch to the new system until the project is complete.
On the other hand, your team can choose to keep the original system operational throughout the entire data migration project. This way, users only have to switch to the new system once it’s completed. This can also be a difficult task for your engineers as data must be updated in real-time across both platforms. If any changes are made to the original system, they must also be applied to the new system.
The trickle migration approach works best for midsize to large companies that work with large amounts of data. It’s also a great option for companies that can’t afford to have any downtime for their users during data migration or that work irregular hours.
Edmentum uses DemandTools to cleanly migrate thousands of records into Salesforce
What does a good data migration process look like?
This is the fundamental review stage before you migrate your data. Here you determine exactly what is being migrated and where it will be placed. This includes knowing how much of your data actually needs to be migrated. Sometimes, there may be information that can be left behind — for example, if it’s inaccurate or incomplete and shouldn’t be transferred to the new system.
This step helps your organization avoid wasting unnecessary time and money on your data migration. It can also flag any issues or concerns that might affect the transfer later on.
Auditing and profiling
This process requires a full examination and data cleansing of the data that you plan to move. Although time-consuming, auditing and profiling are essential to a successful migration.
In this stage, any possible conflicts or data quality issues, such as duplications or anomalies can be identified and removed. Most organizations will use automation tools to complete this step.
This stage should also identify which data is related and how to ensure those relationships aren’t lost during migration.
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In this step, your organization should:
- Detail migration and testing rules
- Create acceptance criteria
- Assign roles and responsibilities within your migration team
You may choose to work with an ETL developer, data engineer, system analyst and/or business analyst to then create scripts for data transition.
Data migration testing
Although we’ve listed data migration testing separately, after migration execution, it should actually be performed throughout every phase of a successful data migration — from design to execution to post-migration.
Testing will ensure that your data quality remains high as it’s extracted, transformed and loaded to the new target system.
What to look for in a data migration solution
Although data migration can be a complex process, it also adds significant value to your data assets as it can help with growth and data improvement.
When considering a data migration solution, make sure that it checks each of the below requirements for your company:
- Performance and scalability
Additionally, your business’s data migration solution should help manage the quality of your data during the migration process through duplicate identification and merging, standardization, record ownership management and the ability to understand where data may overlap between systems in different areas.
Each of these considerations will help to maintain high-quality data. For example, during data migration, Leads may be created as Contacts in the system that you’re moving from with a particular type to denote whether it’s a customer or prospect — with the right tool, this information will be migrated over to the Lead object in the new system. Operational tools that can fulfill all of these requirements are key to successful data migration.
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