Migrating data from multiple sources into a new information management system is a complex and often headache-inducing undertaking. Data migration is usually necessary to keep pace with technological advances and industry standards, but it requires a great deal of effort. Data from different storage areas – both on-premises and in the cloud – must be evaluated, analyzed, cleansed and organized before it can be combined and matched. Not to mention the new rules, responsibilities and practices that staff must adhere to. However, it doesn’t have to be as hard as you might think to overcome these challenges and successfully migrate your data. By adhering to the following top 10 data migration best practices, your organization should be able to:
1. evaluate data complexity
One of the most important practices in successful data migration is to evaluate what knowledge assets the organization has, where and how they are stored, and the complexity of the data itself. The complexity of the data strings and the complexity of the current data classification systems can significantly impact the direction your organization moves as you proceed with the migration and integration process.
2. establish data standards
Before your organization can move forward after assessing the complexity of your data, it’s a good idea to make sure you have a comprehensive set of data standards in place. Data is an integral part of business processes, but at the same time, it is difficult to manage because it is constantly changing. Establish rules and standards before migration to ensure successful use of your data in the future.
3. define current and future business rules
In addition to setting data standards, it is also important to define the current and future business rules that will govern your data usage. These rules should ensure compliance and compatibility with business and validation rules, not only for the current data migration, but also for any future policy requirements and regulations.
4. establish roles and responsibilities for information governance.
Establishing information governance for your new data system starts with figuring out: Who has the final say? Who manages the information? Who is responsible for supporting data quality, access and use across the enterprise? Your entire company will be affected by the data migration – so be careful when selecting the team members and managers who will take on these important tasks and technologies.
5. perform data quality assessment
Of course, data migration means much more than moving data from point A to point B. Before data can be transferred from one system to another, it must at first be assessed. This ensures a high level of quality once the new database is available to current and future users. During the data quality assessment, duplicate content should be removed, as well as any files that are not relevant to current or future business processes. If necessary, a master data file can be created at the same time.
6. gather migration requirements
Gathering migration requirements should be fairly straightforward once the complexity and quality of the data has been determined, rules and standards defined, and an information governance structure established. Carefully analyze how and where your organization’s data will be used, who will use it, and how this might change in the future.
7. evaluate and identify the right tool
After you’ve done all your homework, it’s time to evaluate and identify the right tool for your new data environment. TOLERANT Software’s batch tools are great for these tasks.
8. perform risk management
Risk management is integral to the data management process, so it makes sense that it is also integral to the migration process. Ensure that all data is readily accessible for potential audits and that all information systems are compliant with regulatory, industry, and company-wide requirements.
9. manage change
This may just be the most important practice in a successful data migration. Managing change in an organization requires careful consideration of the users, customers, vendors, and partners who will participate in the new system. Change management is about creating a successful transition for everyone involved and keeping everyone on board for the long term.
10. test migration
Migration testing should be conducted well before the migration is complete. Even throughout the migration process, these tests should be conducted to identify errors and problems up front – while they are still fixable. Once the migration is complete, your team of data professionals should conduct a series of more extensive tests to pre-assess and approve the new system.
11. go live
The actual data migration is technically implemented. This involves extracting data from the source systems, adapting it to the qualitative requirements of the target system via various transformation modules, and finally loading it into the target system.
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