What is Data Migration?
Data migration is the process of transferring data between different data formats, storage systems, or computer systems. Organizations may initiate a data migration process for several reasons:
- To replace or upgrade servers or storage infrastructure
- To move on-premises infrastructure to cloud-based platforms
- To move data between third-party cloud services (cloud data migration)
- To carry out infrastructure maintenance
- To consolidate websites
- To migrate databases or applications
- To move data during a data center relocation or merger
- To install software updates
The data migration process requires data preparation, extraction, and transformation following a customized plan based on the company’s objectives and the budget for the project.
Why is Data Migration Important?
Data is the king of modern business competitiveness. Companies rely on proper data management to acquire services, manage business processes, and ensure customer satisfaction, business continuity, and a consistent revenue stream.
Data migration ensures that the organization’s critical data is securely transferred to another application, storage system, or the cloud. Migrating data between platforms can be challenging, but it provides businesses with many benefits. A successful migration process can increase productivity, reduce storage costs, upgrade applications and services, and more.
What are the different types of data migration?
There are six main types of data migration. Let’s explore them below.
Storage migration
Storage migration refers to moving data from one computer storage system to another. The process often involves migrating physical data from a hardware storage system to a target system.
Storage migration is typically performed to upgrade existing storage equipment to a more sophisticated storage infrastructure. In such a scenario, the process involves moving data from an old system to a new one: paper to digital, tape drives to HDD, HDD to Solid State Drives (SSD), and physical storage to a cloud computing environment (virtual storage ).
Often, storage migration is not due to insufficient storage space, but rather the need to upgrade storage technology. Typically, this approach does not alter or format the data. However, companies can take the opportunity to perform data validation and reduction by detecting corrupt or obsolete data.
Application data migration
Application migration (or “application migration”) involves the transfer of application or program data from one computing environment to another. The process typically occurs when a company changes application software or switches to another application provider. If the new application requires different application interactions, migrating the application may require radical data transformations.
A major challenge for application migration comes from the source and destination systems having specific data models and using different data formats. Vendors can provide application programming interfaces (APIs) to protect data integrity. Additionally, organizations can benefit from vendors’ web interfaces to facilitate application migration and middleware to fill gaps between the application and operating systems.
Database migration
Databases house and structure data in an organized way to enable more efficient storage technologies. Databases are managed through database management systems (DBMS).
Database migration involves moving data from one database management system to another or upgrading from an older version of DBMS to the latest version for the same DBMS. The first scenario is more challenging, since the source and target systems often use different data structures.
Database migration is commonly performed when a company changes database providers, moves the database to the cloud, or upgrades database software. It is essential to back up all databases before migration.
Business process migration
Business process migration involves moving business application data and data related to business processes and metrics to a new environment. Business process metrics typically include product, customer, and operations data.
A common reason for business process migration is business optimization and reorganization or mergers and acquisitions (M&A). This approach to data migration is necessary for many organizations to enter new markets and remain competitive in an ever-evolving field.
Migration to the cloud
Cloud migration has become a common form of data migration. Cloud migration refers to moving data or applications from local (on-premises) storage to the cloud or from one cloud platform to another. (cloud storage migration)
The cloud environment provides on-demand scalability and flexibility and reduces capital expenditures (CapEx) for on-premises infrastructures. Cloud service providers offer various features related to your storage, application, database, and cloud migration needs.
Data center migration
A data center houses an organization’s data storage infrastructure necessary to maintain critical applications. The data center comprises network routers, servers, computers, switches, storage devices, and all related data equipment.
Data center migration refers to the migration of data from an on-premises data center to a new physical location or a new system (the cloud) or from old data center infrastructure to new infrastructure equipment in the same physical location).
Planning and preparing for the data migration process
Data migration projects can lead to unexpected costs and downtime if planned poorly. Unplanned issues can cause customer frustration and dissatisfaction during and after the migration. During data migration planning, companies should define specific goals and a timeline, and prepare for potential challenges that may arise before beginning the migration process.
There are three critical factors to consider when outlining your data migration plan. Let’s explore them below.
Workload type
Backups, databases, and virtual machines (VMs) can usually be migrated through data migration software provided by the respective vendor; The data migration tools provided are explicitly designed to adapt to the type of data being moved.
However, if you are migrating different types of data or do not have access to a dedicated data migration solution, your teams should plan for potential downtime accordingly.
Typically, organizations can migrate critical workload data in stages. After each step, the responsible team must test the migrated data to ensure its validity and integrity, while keeping the source and destination system running simultaneously. These approaches are commonly called “drip migrations.”
On the other hand, companies can plan a mass transfer of data outside of business hours as long as the schedule does not extend the available window. This approach is also called “Big Bang migration.”
Data volume
When migrating data less than ten terabytes (TB), migrating the data to a new system or storage location using a customer-provided storage device is typically the simplest and most cost-effective approach.
Transferring data of larger volumes (more than one petabyte (PB)) would require a specialized data migration method provided by a dedicated data migration service or cloud provider. In theory, companies can use online data migration for unlimited amounts of data; However, the time limit for large volumes of data is likely to extend beyond the stated budget and objectives of the entire data migration process.
Completion speed
An offline (physical) data migration project depends on efficient shipping to meet deadlines. On the other hand, online data migration timers depend on the volume of data being transferred and the speed of your network connection.
If companies are concerned about the migration speed of the entire data migration process and have enough available bandwidth to spare, online migration could be the most efficient option. However, offline migration may be more convenient if your organization has set a flexible deadline or is experiencing bandwidth or network limitations.
Overview of the steps involved in a data migration project
Moving data from one system to another may seem easy until you’ve tried it. Your strategy will depend on the specific needs and objectives of your organization, coordination with all responsible teams, and evaluation of project success during and after the migration.
To give you a head start, below is a common pattern for all data migration projects. You can use it as a baseline for your unique data migration planning process.
Importance of a data migration strategy
A data migration strategy is crucial to minimizing costs, data loss, and potential downtime while maximizing utility for all users who depend on the migrated systems.
During strategy creation, companies should evaluate data sources and target systems, choose an appropriate migration approach (Trickle or Big Bang), plan and approve budgets, create and test migration solutions, and create backups. data security before the migration phase.
Extracting data from the source system
Data extraction refers to the extraction of all necessary data to migrate it from its current storage. Companies usually need to extract data from multiple sources, especially if the goal of data migration is to form a more unified and complete data storage system (data integration). Data sources can include spreadsheets, legacy system applications, customer relationship management (CRM) software, analytics tools, marketing tools, registries, and different types of databases.
Data extraction can be done manually, but the process is incredibly cumbersome and carries a higher risk of error. This is why most organizations use an automated or hybrid approach to extract data through a dedicated software solution.
Data transformation and cleaning
Once data is successfully extracted, data migration specialists will audit and refine all data to ensure it reaches the new system or environment with minimal error counts. Data transformation is critical when understanding different types of data from multiple sources.
Data cleansing is the process of correcting incomplete, incorrect, duplicate, or incompatible data within a specific data set. Its goal is to identify data errors and then delete, change, or update the data to correct them. Improves data quality and provides data management teams with more accurate, reliable, and consistent information.
Data transformation relies on data cleansing to ensure that all data can be classified and formatted according to the same standardized rules.
Loading data to the target system
After extracting, cleaning, and transforming all the required information, the data is moved to the new system through an online or offline migration.
Data can be loaded all in a single instance (full load) or incrementally. Incremental loading (used by the “drip” approach) allows teams to clean or transform data in stages or compare it with already loaded data for inconsistencies or duplications. This is why most organizations would choose the incremental load approach.
Validation and testing of migrated data
Data migration validation can be done manually or automatically. Manual sampling typically inspects a random subset of the transferred data. Although sampling is an effective data migration validation method, it cannot guarantee that 100% of the data is migrated without errors or inconsistencies.
On the other hand, automatic testing tools can be used on 100% of the data, depending on the requirements of the migration project.
In post-migration testing, teams should compare the number of records on the source and destination system. They should then check the source data to ensure that it has not changed during the migration.
If the migration requires schema changes, they must be validated on the target system. Additionally, teams can perform NULL validation and reconciliation checks between the source and destination fields.
Finally, teams should perform data integration testing with the target system and any complementary systems.
Common challenges in data migration
Even though data migration has been a part of the IT landscape for a long time, many modern businesses still face significant challenges.
Below are seven of the most common.
- Poor data governance
Enterprises should define who is responsible (and has the rights) to create, edit, approve, and delete source data and document all authorized actions as part of their data migration plan.
- Bad planning
Many IT teams spend limited hours planning a small data migration project. Spending a lot of time planning doesn’t guarantee a successful migration, but a solid migration plan can save you hours (if not days) when you begin your migration.
- Lack of experience
Although data migrations are simple, moving data from legacy systems to new data systems can be complex. Companies should have a professional team to operate the data migration tool, handle data conversion, understand different data migration strategies, ensure disaster recovery is available, monitor the physical transformation process (if necessary), and minimize the risk of data loss and corruption.
- Poor communication with key stakeholders
Regardless of size, migration projects depend on key stakeholders being involved in the process. It is best to inform all stakeholders of the data involved and keep them informed through daily/weekly status reports. Regular communication builds trust and can ease the data migration process.
- Untested migration methodology
Dedicated data migration software is convenient when migrating data from one system to another. However, even the most robust data migration tools benefit from unique configurations.
Companies must understand the different types of data migration, data conversion requirements, and the risks associated with each approach to minimize downtime and data loss.
- Insufficient preparation
If your systems require data migration for millions of records (or hundreds of tables), it is best to rely on dedicated data migration tools. Hiring a specialized company can also facilitate the process and reduce costs.
- Project and supplier management
Ongoing projects can rarely stop and wait for the migration process to complete. Both projects and their associated suppliers require management; It is essential to assign responsible people to manage current business-critical projects and any related vendors.
What are the benefits of data migration?
If done correctly, data migration can benefit system upgrades, technology consolidations, data center relocations, and much more. In addition, it provides long-term benefits to organizations of various sizes.
- Improved data security and quality
- Improving data accessibility
- Improved efficiency
- Increased competitive advantage
- Cost reduction
- Scalability
- Reliable disaster recovery
- Improved compliance
- Improved analytics
Data Migration Best Practices
Regardless of your data migration strategy, several best practices can significantly benefit all types of data migration. Let’s explore them below.
Developing a data migration strategy
As mentioned, moving data from one system to another system or environment can be challenging, especially for large volumes of data. Careful planning and strategizing are crucial to mitigating surprise costs and unexpected downtime. In turn, managing unplanned issues will keep end-user satisfaction and comfort high.
As mentioned, the three pillars of comprehensive data migration strategies are workload type, data volume, and speed of completion.
Creating a detailed data allocation plan
Data mapping is the process of matching fields from a source database to a destination database. It is a critical step to facilitate data management tasks such as data migration and integration.
Companies can migrate data from many sources; Different source data are likely to define similar data points in different ways. That is why data mapping is essential to homogenize data and facilitate decision-making based on business insights.
Mapping data can bridge the gaps between different data formats. The approach ensures that the information in the source data model is accurate and usable when it reaches the target system to create and implement a homogeneous underlying data format for the new environment.
As mapping data has become more complex, it is better to rely on automated tools to manage large data sets.
Establishment of data quality control measures
Data quality includes accuracy, consistency, completeness, and timeliness of data. It is essential that a data migration process ensures compliance and customer satisfaction and facilitates business decision-making.
Ensuring data quality for the migration process depends on several key processes:
- Defining data quality metrics
- Perform data cleaning
- Test data before migration
- Use of automated tools
- Assigning data ownership
Neglecting proper quality control steps can lead to customer dissatisfaction, compliance issues, and lost business opportunities.
Training and support for users during migration
After data migration, companies must train and support all users who access the new data in the system. Your organization can follow a step-by-step process to ensure maximum usability and customer satisfaction.
- Identify user groups and needs to understand current workflows, objectives, and pain points to provide relevant training and support tailored to each user group.
- Develop training materials and methods to help users understand and adopt the new system. Training materials may include guides, manuals, videos, FAQs, etc. Training methods include webinars, workshops, online courses, coaching, and more.
- Conduct training sessions and assessments to enable users to gain competence and confidence in using the new system. Training sessions can take place before, during, and after the migration. You should also assess users’ skills, knowledge, and attitudes before and after a training session to evaluate the effectiveness of the training and potentially improve the process.
- Provide ongoing, proactive communication and support to help users overcome any issues or challenges that may arise post-migration. Communication may include emails, webinars, newsletters, etc. Support may include chatbots, help desks, forums, communities, and other channels.
- Monitor user satisfaction and adoption to measure the value and impact of the new system and the overall success of the data migration process. You can measure user satisfaction through interviews, surveys, focus groups, reviews, and ratings. You can track user adoption through different metrics: login frequency, system usage, error rates, feature utilization, and completion rates.
- Review and improve support and training to ensure continuous improvement and learning for end users and the target system.
Continuous monitoring and evaluation of migrated data
After completing data migration, companies should evaluate the results to determine if the process achieved success criteria and objectives.
Migration outcomes refer to the impacts and end results of the migration process: data that is accurate and consistent with the target format or system, successful and complete data migration, data that is integrated and aligned with critical business processes and objectives, experience improved user experience and value, improved performance, system ease of use, and more.
Conclusion
Data migration is the process of moving data from one system or format to another, typically to improve functionality, performance, usability, and security.
Data migrations are not static events. They require extensive planning, data conversion and quality assurance, data security protocols, team coordination, and evaluation of success during and after the migration.
