Data Migration Strategy 6 Steps to Success

Types of Data Migration Strategies

Data migration is a complex project that requires expertise, dedication, and the correct data migration software. An organization should incorporate a set of best practices within any migration project to achieve the best results.

Without a strategy and the correct migration software to handle big data, engineers can compromise valuable information. It's critical to maintain data integrity and data quality before anything moves from an old system to its target environment.

An effective data migration plan will ensure a company maintains compliance, protects sensitive enterprise data, and doesn't waste any money throughout the data migration process. Here are some of the top strategies to ensure a successful data project outcome.

1. Big Bang Migration Strategy

An organization has a limited time frame to complete every type of data migration project. Too much downtime costs time, money, and compromises the quality of customer relationships. In big bang migration, each data transfer takes a short period. Customers and employees cannot access information while the organization is migrating data into a target system.

While it doesn't take long to transfer data out of a legacy system in big bang data migration, major systems are still unavailable for use. This can be difficult for organizations that operate on a 24/7 window and have no alternative way for customers to access source data. Users will have to wait until the new application(s) go live, which can cause a loss in revenue.

2. Trickle Migration Strategy

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Trickle migration works exactly the way it sounds. The migration process occurs in separate steps and phases, according to the data management policy of an organization. Before moving data out of a source system, the source target and new system operate simultaneously. This decreases the need for downtime and other money-wasting interferences.

All business processes continue to run as planned while information moves into target systems. This type of database migration is more complicated than it is in the big bang, but it's usually more effective. It minimizes human error and potential risks because engineers follow quality rules and perform migration testing throughout each phase.

6 Key Steps in Developing a Data Migration Strategy

Regardless of the approach, an organization takes, each project plan usually follows a similar pattern. When transferring data, it's critical to test for data validation to ensure data quality and integrity.

Engineers must make sure to utilize the correct migration tool and set of migration best practices to prevent data loss and duplications. To ensure successful data migration, teams should follow these data migration best practices in order -

1. Explore and Analyze the Old System

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Engineers need to thoroughly understand the source data or cloud data before they migrate data into a new database. They also need to know how the migrated data will work within a new application. They can discern which information should stay, which can leave, and whether it is quality data, to begin with.

Most teams perform data backups to prevent data loss. They also run an audit to see whether there are duplicate records or incomplete data sets. Teams shouldn't transfer that type of information in data storage. A lack of data profiling can result in critical problems post-migration. In a worst-case scenario, the entire data conversion process could fall apart. This is a waste of time and money.

2. Define and Outline the Data Migration Process

Businesses will decide whether to utilize the big bang or trickle migration strategy in this stage. They may map out the entire infrastructure of a solution and document each step of migration projects.

In doing so, teams will decide how long the project should take, how much it will cost, and whether there may be any problems. They should document all of this information in case they need to reference it later on. It's also critical to establish data quality rules and security measures in this phase of the migration plan.

3. Build a Data Migration Solution

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It's better to over-plan then under-plan when it comes to time to move data. Because the process only occurs once, teams need to do it correctly.

Many engineers break down integration data into different categories and create one subset at a time. Then, they perform a quality test on that subset to maintain data integrity. For larger migration projects, teams work in parallel to expedite the process.

4. Perform a Live Test Prior to Data Migration

Engineers need to continue testing after they test the code in phase 3. They need to utilize real systems data to make sure execution is accurate and the application is working.

An organization should ensure that all internal or third-party teams have the expertise needed to perform a live test. This will maintain data integrity and prevent any wasted time or effort.

5. Implement the Data Migration Plan

Engineers should reference the plan to determine which strategy they chose. Whether it is big bang or trickle migration, they can implement it after testing. It's important to make sure that all steps were performed correctly before proceeding to this phase. Once the plan is initiated, it's difficult to go back to square one and start over.

6. Perform an Audit Post Migration

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Teams need to enact an audit to continuously test the quality of data after migration. This will ensure the data migration projects are a success. The audit should not just stop after a couple of days. A good migration plan will produce healthy results, while a poor one will not.

If engineers followed all of the steps to the best of their ability, the audit should not pick up on too many problems. Some concerns arise through no fault of the teams. If this is the case, engineers should quickly resolve the problems as they arise to avoid more critical issues later.

Key Takeaways of Data Migration Strategy

In conclusion, here is what to know about a data migration strategy

  • Big bang migration occurs when an organization transfers all of its information at once. It requires more downtime but is completed in a shorter time frame. Trickle migration occurs in separate phases and requires no downtime.
  • An organization should explore and analyze the old system to ensure all information is transferrable and high-quality. Teams should also map out the entire data integration process to determine how much it will cost, what resources they need, and how long it will take to complete.
  • Teams should build a data migration solution to break down sets into separate categories. They then test the quality of each subset. After all of the tests are complete, the engineers can perform a live test.
  • Next, the teams can begin implementation. They should also perform a post migration audit to test the quality of data in the new data warehouse. This will ensure that all data migrations were successful.