The Processes Involved in Data Migration

What Is Data Migration?

Data migration is the act of shifting data from one format or location to another or one application to another. The result of data migration is the introduction of a new location or system for the data. The process is driven by the business' desire to migrate or consolidate data by replacing or augmenting legacy systems with new applications that will use the same dataset. In most cases, data migrations are initiated by organizations that want to move their data from on-premises applications and infrastructure to cloud-based applications and storage to transform or optimize IT operations.

Data Migration Vs. Data Conversion Vs. Data Integration

Data migration entails shifting data from one system, location, or format to another. It involves profiling, cleaning, validating the data, and continuous quality assurance in the target system. On the other hand, data conversion is the first step in the data migration process.
Read more about optimizing data cleansing

Data conversion involves converting the data from one format to another. This is an important step when transferring data from a legacy application to an improved version of the same or another type of application with a different structure. Conversion involves extracting the data, altering, and loading it into the new system by following a set of protocols.

Data integration involves combining data located in different sources to provide users with a unified. Data integration is necessary for data analytics. Integration can involve creating data warehouses, which automatically tier on-premise and cloud-based data centers.
Read more about Integration

Why Is It Seen Difficult and Risky?

Data migration is complicated by data gravity. Data gravity relates to the fact that data attracts more data as it grows, integrating data into a business is a complex process, and that data requires customization over time. The challenge is especially high for firms migrating to cloud infrastructures.

The problem is that every application adds complexity to data management by adding new elements of application logic to the data management hierarchy while each tier is unrelated to the next data application case. Business processes utilize data in isolation then produce their own formats while pushing integration to the next process.

Consequently, data architectures, application design, and business processes must be responsive to each other, but in most cases, these groups are unwilling or unable to change. This setup forces application administrators to bypass the simplest and most ideal workflows leading to suboptimal designs. Even though sidestepping processes may be necessary during the design stage, this technical problem must be resolved during data integration or migration projects.

Data managers can overcome data gravity by disentangling the data and related applications and putting them in safer and more favorable environments. Firms can set aside time at the beginning of a migration project to resolve data and application complexes and improve data management while enhancing data governance and application mobility.

Considering the complexity caused by data gravity, data administrators should consider presenting data migration as a strategic weapon to get the appropriate level of support and awareness from the client firm. They should ensure that the project gets the support it needs by focusing on the most delicate component of the migration- the fact that they will turn off the legacy system. This is bound to get the attention of key stakeholders.

Types of Data Migration

Upgrading data systems or taking the data center to the cloud has several business advantages. Most organizations see this as a part of the natural evolution of their business. Companies migrating to the cloud aim to focus their human resources on business priorities, increase agility, drive top-line growth, pay for the services on demand, and reduce capital expenses. Ultimately, the type of migration a firm undertakes determines how much IT staff resources will be freed to focus on other projects.

What are the different types of data migration?

Storage migration: storage migration involves shifting data from existing arrays into modern arrangements that allow other systems to access it. This form of migration leads to a significant improvement in performance and cost-effective scaling. It also enables superior data management features such as snapshots, cloning, disaster recovery, and backup.

Cloud migration: cloud migration entails moving applications, data, and other IT resources from a physical data center to a cloud service or from one cloud to another. In most cases, it involves storage migration.

Application migration: this involves moving an application from one system to another. It also includes moving an application from a physical IT center to a cloud, shifting between clouds, or moving the underlying data of the application to a new type of application supplied by a software provider.

How to Plan A Data Migration

What is data migration process?

  • Data extraction
  • Data transformation
  • Data loading

Moving sensitive or critical data or decommissioning legacy systems is an important undertaking for all stakeholders. Even though it is imperative to have a solid plan, data administrators can rely on existing systems. There are several data migration templates and checklists available online. For instance, Data Migration Pro, provides a comprehensive 7-phase checklist, which is an open-source resource developed by data migration specialists.

Premigration planning: assess the data to be moved for stability.

Project initiation: identify key stakeholders and brief them on the process.

Landscape analysis: develop a robust data quality policy management process and inform the client of the goals of the project, including the need to shut down legacy systems.

Solution design: determine the data to be moved and establish the quality of that data before and after moving it.

Build & test: create a code for the migration logic and test the migration by simulating the production environment.

Execute & validate: ensure that the migration complies with the quality requirements and that the new dataset is viable for business applications.

Decommission & monitor: shut down and get rid of the old systems.

The task may appear to be daunting, but you don't need to include all the steps in all migrations. Every situation is unique, and each firm approaches the process differently.

Top 10 Challenges

Failing to contact key stakeholders: there is always someone in the organization who has an interest in the data being moved, irrespective of the magnitude of the migration. Find such people and explain to them the importance of this project and the impact it will have on the data before proceeding to implement the migration. If you fail to do this, they will confront you at some stage, and there is a high chance that they will impact your timeline.

Failing to establish communication lines: after explaining the details of the project to the stakeholders, you should keep them informed of the progress. Prepare a status report once a week, and alert them when things get off track. Regular communication will help you build trust with people who are affected by the project.

Lack of data governance: while preparing the project plan, clearly identify the people who have the right to create, edit, remove, or approve data retrieved from the source system. Document these details to establish proper protocols.

Lack of experts: moving data is a complex task that needs to be handled by people who understand the intricacies. Recruiting a team of experienced professionals ensures that the process moves smoothly.

Poor planning: IT teams allocate as little as 10 hours to planning for small data migration projects. Allocating sufficient time helps produce a solid data migration plan that saves time when moving the actual data.

Insufficient IT resources and skill: if the project involves moving huge data troves, the administrator should invest in quality data migration software and hire a specialist firm to assist with the process.

Delaying until you achieve perfect specs for the target: if the implementation team is already working on the design criteria, proceed with steps 2 and 3. Target readiness is important, but you shouldn't let slow the project.

Unproven migration methodology: Before implementing a migration method, research to confirm that it has worked in other firms with a similar profile. Avoid the allure of generic procedures from vendors.

Supplier and project management: vendors are critical to the success of a project. Make sure you have sound project management strategies that account for the input of vendors.

Cross-object dependencies: even with the advanced capabilities of modern data management tools, it is still common to find dependent datasets that were omitted in the original plan. Develop a contingency plan for cross-object dependencies to avoid delays if they are discovered late in the migration process.

Data Migration Best Practices

Backup the data: create a backup of the data to be transferred so that you will have a fallback dataset to use if the implementation goes wrong. Test the backup resources before proceeding with the implementation.

Migration methodology: identify an appropriate migration methodology and ensure that it is suited to the project and the organization.

Test multiple times: test the systems during the planning, design phases, and implementation stage to ensure that each step achieves the desired outcome. Ideally, you should take an incremental approach by testing the system with every iteration.

Stick to the program: the data migration process is often complex and protracted, but you should always stick to the plan to ensure timely project delivery.

Future Outlook

The global data migration industry is projected to be valued at $21.1 billion in 2027, which is an 18% growth rate. The growth will largely be driven by firms moving consumer data to the cloud to improve utilization capabilities. The traditional markets such as the US, Japan, and Europe will dominate the sector though China is expected to make significant gains. Large tech corporations such as Amazon and Microsoft will continue to dominate the market for software and services.

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Our data quality management and data test automation ensures that clients get error-free data quality solutions.

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