In today’s dynamic and ever-evolving environment, businesses have to adopt new technology at a snowballing speed to keep up with the competition. Data migration strongly supports these high-tech updates, enabling organizations to modernize their infrastructure and optimize their business processes.
In this article, we’ll list down ways that can help you ensure the success of your data migration project. We’ll also explore some common challenges associated with data migration so that you’re well-equipped to tackle them whenever the need arises.
The 7 R’s of Data Migration
When planning your data migration strategy, it’s imperative to keep in mind these seven critical characteristics:
- Robust: Efficiently manage all phases of data migration ranging from data extraction and transformation to authentication and loading into the destination system, even when data volume is high
- Resilient: Easy to modify and handle problems in the operating environment
- Rapid: Efficient process execution, leveraging the present source or destination facilities to support quick processing
- Reporting: Progress pointers during the migration process should be available
- Reconciliation: Reconcile the completed process
- Recoverable: The data should be retrievable from the point of failure when needed
- Reusable: Should have reusable process modules, including transformation utilities, error control, and data cleaning procedures that can be used in other tasks without impacting the already migrated data
Steps to Execute A Successful Data Migration Process
To execute a successful data migration plan, follow these seven steps:
1. Explore the Source Systems
Before you start migrating the data, the first step is to recognize the source systems and classify the source data. It’s best to begin with grouping data, such as customer names, addresses, etc. based on the target model.
The source data can include various fields; however, some data might be duplicate or useless to the target system. Thus, it’s crucial to classify which data is needed and where it is located. Also, you need to ascertain which data has redundancy and is not desired for migration.
Once you perform this step, you’ll identify all the source data that’ll load into the target system. Also, you’ll identify any data gaps at this step and preferably take account of additional sources to fulfill your business needs. Ideally, you can divide the data into groups. It will allow you to carry out multiple tasks in parallel.
2. Assess the Source Data
The second step is to evaluate the quality of the source data. After all, there’s no use in moving data if the new system goes down because of data discrepancies, erroneous or duplicate data in the source system. To evaluate the data, it’s recommended to opt for data profiling.
Data profiling helps you methodically scan and analyze the contents of each column in tables of interest. It lets you identify data faults at tabular and columnar levels. Moreover, it helps evaluate data conformity and ensures compliance with the target system requirements.
This way you’ll have an exhaustive understanding of the quality of data in the source systems. You’ll also identify critical data concerns and develop a list of rules to rectify them.
3. Design the Migration Process
In the next step, outline the procedural planning and implementation of the migration process. Also, describe the testing procedures and define whether it’ll be a parallel process or a zero-downtime migration.
At this stage, it’s best to take account of data transfer schedules, technical information, or authorization requirements to document the whole process.
4. Develop the Migration Sequence
When it comes to developing a migration sequence, it’s best to divide the data and test one group at a time (for instance, product or customer). This method is in line with the first step in which you explored source systems to classify the data. You can also create and test migration in batches with groups running in parallel for larger projects.
Migration process testing is typically an iterative method. You begin by inspecting the modules discretely in small subgroups to make sure the mappings, transformations, and data cleaning procedures are operational. Next, upturn the data volume and ultimately connect all modules into one migration task.
By the end, you’ll have a verified data migration process that is scalable, consistent, and can relocate the improved data within the allotted time.
5. Execute the Migration
After testing comprehensively, the next step is to execute the migration. Most commonly, the source systems are stopped during the execution of the migration process. However, if the source systems have to be continuously operational, you may need a zero-downtime migration method.
In this method when the initial load is processed, synchronization is used to capture changes made during the initial load. Once the initial load is complete, the source and target systems are synchronized to ensure data completeness.
6. Transition to the New System
After migrating the data, it’s time to choose when to switch to the new system. At times, you may even have to retire the previous system.
To make sure that the complete data has been properly migrated, audit logs and records are created during the execution stage. In the light of these reports, you’ll have to decide the transition of users to the new system.
7. Manage and Monitor Ongoing Improvements
During the data migration process, there will be continuous data quality improvements. If the source systems are not retired, there might be some data quality problems which will have to be rectified in the source data. Plus, you’ll have to oversee continuing enhancements and monitor the data quality of the migrated data to ensure optimum data management.
Challenges in Data Migration
Migrating enterprise data from one system to another is always challenging, especially when you have to move millions of records across systems.
To help simplify your data migration process, we’ve identified the following most common data migration challenges you could face and how to avoid them:
1. The Complexity of Source Data
One of the main challenges in data migration is the complexity of source data. Often, you’ll have to perform data integration on the source data to work through any intricacies. For example, if you were using a legacy data storage system, then you will have to perform data transformation to make the data compatible with the target system.
2. Loss of Data or Corrupt Data
While migrating data from one system to another, you might face data loss or corruption. To tackle this issue, make sure that the number of source records matches with the number of migrated records. Various tools are available in the market that can help you authenticate the migrated data.
3. Need for In-Depth Testing and Validation
For complex data migrations that involve thousands of records, the cost of failure is simply too high. Therefore, the need to perform in-depth data validation and testing cannot be ignored. Keep in mind any events that could have affected data quality. Also, test a large data volume to assure quality. It’s recommended to pull at least 10 to 20 percent of your data to make sure you include an extensive range.
Remember, testing should be a continuous process. So, continue to perform tests during your entire data migration process.
Once you’ve successfully executed the migration process, conduct a full assessment to ensure that the process is working. Document everything in your management software to ensure the transparency of the process.
If you’re looking for a robust, agile, and code-free data migration tool, give Astera Centerprise a try!
Originally published at https://www.astera.com on September 23, 2019.
