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7 Data Migration Best Practices to Power Your Data Strategy

7 Data Migration Best Practices to Power Your Data Strategy

Data is often believed to provide an unbeatable edge to businesses. The more information they can gather and analyze, the more insights they can have to innovate, create value, and exceed their competitors. However, this process of turning big data into big decisions leaves businesses craving for more data than what they can handle. 70% of businesses have been gathering overwhelming amounts of data faster than they can process or analyze. Companies can overcome this situation with better-organized data housed in one place in a single format. This is where business processes like data migration take the lead and figure prominently in building more agile and effective infrastructure. But, there is a glitch: most data migration projects witness dismal success rates, where budgets go off the rails or operations function below expectations due to overwhelming data processes.

A complete and well-thought data migration strategy promises a rescue.

Whether implementing a new system, adopting a cloud strategy, shifting data from inputs to data lakes or from a data warehouse to a data mart, or simply to more secure storage locations, the data migration strategy considering the business impact it might have once the data is moved from one system to another.

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It is vital to adopt the best practices to minimize the failure rate and make the most of the migrated data. Below are best practices that must be considered during the migration process:

Analyze the data source

Analyzing the source of data and understanding how the transferred data will fit into the new system is critical before migration. It is also necessary to determine the type of data migration (storage, cloud, or application) and address data conversion issues. Data is extracted from the source, altered, and transferred to the target system based on specific requirements or data fields.

Missing fields within a source may require fetching from another location.

In addition to meeting the data fields, businesses should audit the data to avoid insufficient and incomplete information. It would be wise to reconsider migrating data if there are inadequately populated fields, flawed data pieces, inaccuracies, and errors. Organizations cannot afford to avoid this source review segment in the migration process. Skipping this practice may cause a critical flaw that impairs the organizational data mapping, disrupting the entire chain of migration procedures.

Define the migration roadmap

The success of a migration strategy depends on effective data mapping. The effects of a single mistake on data mapping can cascade throughout your organization, leading to recurrences of errors and ultimately to inaccurate analysis. While moving data between different systems, organizations should envision a clear-cut roadmap for moving and consolidating data for analysis or other purposes to ensure it reaches its destination correctly. They must clearly define the data to be migrated, match the source fields with their destinations, code the transformation formula or rule, and test the data mapping.

The roadmap focuses on the technical architecture and other detailing required to enable the migration processes. This also allows organizations to choose from data migration methodologies like big bang and trickle. Apart from documenting the project plan, the design phase must also include security considerations to protect their data throughout the process.

Design the migration solution

Data migration success depends on knowing, cleaning, and protecting the data. The time-consuming and multi-step migration process must introduce the right technical architecture and migration tools.

Is the data migration solution compatible with the currently used systems and software?

Is the software scalable, and will future data needs exceed its capacity?

Are there any security features built into the software platform?

These are some of the questions organizations should ask before implementing a data migration solution. Apart from the scalability and security aspects, an ideal data migration solution should also have an intuitive design for delivering a user-friendly experience and aligning with the organization’s budget and support requirements. In addition, the solution must be compatible with the chosen data migration methodology (big bang or trickle) and offers various tools to orchestrate, schedule, preview, and monitor the migration flow.

Take care of the data governance rules

A strategic and comprehensive data governance plan is crucial to getting data migration right. As an extensive set of practices, policies, and roles pertaining to data collection, management, and utilization, data governance frameworks should cover strategic, tactical, and operational roles and responsibilities.

Data integrity requires tracking and reporting the quality of all company information while also ensuring adequate data protection. Standardized rules and regulations should be followed to protect the data and use it in compliance with all relevant external regulations (such as GDPR). Organizations need to know the data that requires careful attention for privacy protection and highlight it in their governance strategy to create the basis of their data governance framework.

An effective data governance strategy facilitates the data migration process as it provides a general understanding of the data by creating common data terminology and a consistent view. Governance also increases the overall data quality while boosting consistency, accuracy, and wholesomeness.

Execute and test the migration phase-wise

It is essential to get it right the first time since migration is complex. A common technique is to break up the data into subsets, build out one category at a time, and then test and validate the sample sizes. Building and testing can be done simultaneously if a company is planning a large migration. Even after testing the code during the build phase, the testing process is incomplete. It is crucial to test the migration design with actual data to ensure the successful implementation and completion of the process. Once the final testing is complete, the execution can proceed according to the plan.

Pre-migration and post-migration testing are crucial, along with backward compatibility verification and rollback testing. It is essential to understand the migration strategy outlined by the team before performing pilot testing. This involves knowing the migration process, the changes occurring in the back-end, and the scripts responsible for these changes.

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Scale the complete data migration process

When it comes to scaling the data migration process, organizations need to consider the data limits for their solutions to meet the data needs in the foreseeable future. To improve scalability, organizations are increasingly migrating their data to the cloud, boosting their speed to market and decreasing the reliance on multiple technical resources. Customers can accelerate cloud data migration projects by deploying a highly scalable and secure Cloud integration platform-as-a-service (iPaaS). It is possible to simplify complex mappings using cloud-native, open-source tools that enable drag and drop functionality. Cloud-based solutions empower organizations with the much-needed flexibility to scale up or down with ease.

What is more impressive is that these solutions meet the growing organizational needs and eliminate the expansion of the IT team. From the scaling point of view, multi-tenancy is another essential factor to consider. Organizations can adopt a backup as a service solution that supports multi-tenancy.

Measure results and improve the process

Setting up a process to measure and assess the data migration results is crucial for achieving the best possible results. Businesses must establish the right practices and ensure they have the right people to lead a successful migration project. This process should cover two areas: the project assessment and the competency of the staff. An evaluation can be carried out independently or simultaneously to prepare for data migration. In line with the assessment results, organizations can adopt best practices strategies for their projects following open standards and published migration methodologies. Employee competency assessments ensure that people with the appropriate expertise will be involved in the project.

A competency framework explicitly designed for data migration roles can help identify and resolve issues as they arise or are likely to occur in the future. Organizations can also benefit from a scoping review that establishes the size and scope of a project, its risks, and potential difficulties.

In conclusion, to become data champions, organizations need to embrace agility in their infrastructure to eliminate the barriers that prevent them from extracting maximum value from their data. To begin with, they need a renewed focus on data migration. It costs billions of dollars for businesses to migrate data between information-intensive applications. However, knowing the hidden challenges, implementing migration with best practices, and managing it as part of the investment will produce accurate data supporting business needs without delays, budget overruns, or scope reductions.

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