Based on survey findings across various industries, it is estimated that only approximately 16% of data migration projects are successfully completed within the allocated time and budget due to the intricate nature and limitations associated with the process.
Data migration refers to relocating data from one system to another. It involves pulling out data from the source system, transforming and cleansing it as required, and loading it into the target system.
Data migration is an important process that can occur for various reasons, such as system upgrades or platform changes. This process must be carried out with utmost efficiency to ensure accurate data transfer without any disruption to business operations. Also, seamless data transitions avoid data discrepancies, enabling business owners to make accurate and timely decisions based on reliable information.
However, data migration projects face several challenges. Data quality and cleansing, data security, system compatibility, business downtime, etc., drastically hamper migration. Overcoming these challenges requires careful planning as well as a systematic approach to achieve a successful data migration with minimal disruptions. As per statistics, 83% of all data migration projects either fail or exceed their stipulated budget.
Therefore, all businesses need to have a proper data migration strategy. In today’s blog, we will discuss the data migration process in detail and the steps you can take to ensure the best outcome.
Understanding the Types of Data Migration
In general terms, data migration is the process of transferring existing historical data to a new storage system or file format. It is a slightly complicated process as it involves a lot of preparation and post-migration activities, such as quality testing, backup creation, and validation of results. The entire process of migration ends only when the old database or system is shut down.
Moving on, there are different types of data migration. Understanding these types can help businesses leverage the most suitable data migration tools and adjust their migration processes. Here are some of the most prevalent types:
- Storage migration: This is transferring data from an old software system to a new one, allowing organizations to upgrade to newer and better technology.
- Application migration: This is a common type of migration that occurs when organizations invest in a new system that uses different data formats and models.
- Database migration: Upgrading an existing database or shifting to a completely new vendor and database are two common instances of database migration. This is a complex process, especially when companies have to migrate their data to a target database that supports different data structures from the source database.
- Cloud data migration: This process involves moving data from an on-premises data center to a cloud platform. It may also involve transferring data from one cloud to another.
Depending on the volume of data and differences between the source and target locations, the migration process may take approximately 30 minutes to even a couple of months to complete.
Data Migration Steps
Every business has different needs, and every data set is unique. Thus, there is no one comprehensive data migration strategy that can work for every kind of organization. It is important to evaluate your precise data migration objectives and build a strategy that is tailored to your specific business needs.
Below are the crucial data migration steps that you can follow as a checklist to prepare a data migration strategy for your organization.
- Develop a comprehensive data migration plan
To begin with, you must carefully evaluate your current data status. Identify every fragment of the data that needs to be moved, its exact storage location, what format it is in right now, and what format is required after the migration. This is important to ensure everything works out as per the plan.
Furthermore, you must choose the appropriate data migration approach for your project. The two most commonly used approaches are as below: - Big Bang Data Migration:
This technique moves all data from the source to the target destination. This approach is faster, less complicated, and less expensive. The only disadvantage of this is that it requires a lot of downtime. - Trickle Data Migration:
With this strategy, the migration process is divided into smaller, easier-to-handle stages, and the data is transported in separate chunks. The migration continues in parallel without any downtime. The old system doesn’t require it to be shut down, making it less prone to unanticipated breakdowns and mistakes. The primary disadvantage of this approach is that it is more expensive and takes more time and resources to keep both systems running simultaneously.
Apart from this, make sure to check the data’s quality and eliminate any irregularities such as data duplication. Also, determine whether your team has the required resources to complete the migration project within the stipulated timeline.
- Create a backup of your data before migration
Although this is not a mandatory step, it is advisable to create a full backup of your content before you migrate it. This provides an additional layer of protection in case of any unexpected migration issues or data losses. Using the cloud is one of the most effective ways to handle data backups. - Design the migration process
The migration process design delineates the migration and testing rules, specifies acceptance criteria, and assigns roles and responsibilities to the team members. At this stage, you need a data migration specialist or an ETL developer, especially if your project involves handling large volumes of data. A specialist will help with data mapping, the data loading process, and establishing recovery measures for each stage of the migration. He will also provide a timeline for the entire process to complete. - Execute the data migration plan
At this stage, the ETL process goes live, i.e., Extraction, Transformation, and Loading activities. The amount of data that has to be migrated, and the method of data migration chosen, determines how long the process will take. A good ETL specialist will ensure the right system permissions are used to conduct successful data migration. It is also important to ensure that the data is thoroughly cleansed before converting it into a suitable format for migration. - Test and validate the migration design
You must test every section of the migrated data to ensure no errors. Frequent testing of the data that enters the target system provides quality, security, and compliance with requirements. - Audit the outcome of the migration process
Even with frequent testing, there may be instances that an error might occur during the migration. Therefore, conducting a thorough audit is indispensable to ensure everything is accurate after migration. At this stage, you can also use your backup to replace the files that contain errors or incomplete data.
Overcoming Challenges in Data Migration
The data migration process can encounter several challenges that can hamper its success. Here, we will look at the most common challenges and strategies to overcome them.
- Data quality and cleansing: Poor quality of data, which is characterized by duplicates, inaccuracies, and inconsistencies, can hamper the migration process. To avoid this, you must invest time in data cleansing activities before executing the migration. Implement data validation checks and correct inconsistencies to ensure the migrated data’s integrity and accuracy.
- Business downtime and disruption: Another one of the significant challenges of data migration is system downtime. This can negatively impact business operations. To mitigate this issue and minimize disruption migration during weekends or off-peak hours is advisable. Also, you can implement trickle data migration or phased migration that helps to maintain business continuity.
- System structure and compatibility: Often, issues such as incompatible systems, discordant data structures, or significant differences in data formats can create problems during migration. It is important to thoroughly analyze source and target systems pre-hand to detect any discrepancies. You can establish data mapping rules that will help to ensure enhanced compatibility between systems.
- Data privacy: Data migration involves migrating confidential and sensitive information. Therefore, compliance with privacy regulations is important. You must deploy high-security measures and access controls during migration. Also, conduct frequent audits to verify the protection of data during and after migration.
These are some of the most common challenges involved in the migration process. Organizations can easily overcome these obstacles and ensure a successful migration.
Use Cases of Data Migration
Data migration is a common requirement for organizations across different industry verticals. Here are some of the most common use cases when organizations require data migration:
- Upgrading their software systems
- Embracing cloud computing
- Consolidating their data centers
- Replacing legacy systems due to outdated technology
- Data archiving to free up storage space
- Switching to a new platform or vendor for specific applications
Partnering with Experts for Data Migration
Data migration is a crucial business process. However, most organizations view it as a necessary evil rather than a value-adding process. And this results in many serious complications that endanger data integrity and accuracy.
Considering migration as an important task worthy of special focus is half the battle won. That is why you must partner with specialized experts who possess wide-ranging knowledge of data migration tools and strategies. At Algoscale, we offer innovative tools that you need to perform smooth and secure migration. Our experts analyze your data, develop a robust migration plan, and establish clear migration policies to make your migration easier overall. Get in touch with us to know more.
