Let’s begin with some numbers! According to IndustryARC, the global data migration market that emphasizes Cloud-based servers over on-premises ones is predicted to reach an estimation of $10.98B by early 2022. In addition to this stat, the Cisco Global Cloud Index shows that Cloud traffic is expected to reach 7680 Exabytes in North America alone! Similar enhancements in modern data management technology bring more efficiency and transparency, which will directly surge the adaptation of application and data migration in small-scale and large-scale enterprises.
Given the risks associated, the question “Is data migration really important?” isn’t unusual. And the answer must always be “Yes!” Delaying data migration while holding onto outdated IT infrastructure isn’t an option with increasing market intrusion from non-traditional competitors who can create more nimble and responsive approaches towards delivering unique products. Because monolithic application systems weren’t designed to quickly adapt to business dynamics, they have to be replaced. Failing which, may pose further risks of losing market share and retention.
Let’s understand data migration first
At its core, data migration is the process of transferring data from one location to another, from one application to another, or from one format to another. This crucial step towards improvising an outdated IT infrastructure is generally taken during the installation of new systems or upgrading legacy ones, which will share the same dataset without affecting live operations. In recent years, the majority of data migrations are executed for transferring actionable data from on-premises infrastructure to Cloud-based options, that too, while undertaking data migration testing.
Concerns with legacy systems
The primary focus of IT infrastructure has already shifted towards better performing, more efficient, cost-effective, and secure solutions. CEOs and IT admins are struggling to maintain or support legacy systems as common challenges in legacy designs are time-consuming to tackle while the technology is mostly unfamiliar to new-age IT personnel. Some of the key concerns of using legacy systems include:
- Heavy Maintenance Costs: Legacy systems are now obsolete, primarily, because of higher maintenance and operational costs. Further, the poor performance of such legacy systems cannot support new business initiatives.
- System Failures: With legacy IT infrastructure, system failures are a daily occurrence. Since the professionals who implemented such systems have retired, new-age IT admins lack the skills to maintain legacy systems.
- The Inability of Processing Complex Data: Within legacy systems lies old technology and computer systems that are fundamentally unable to execute complex enterprise operations with enough speed or reliability.
The increasing challenges to using legacy systems in today’s tech-driven world has led to migrating to new-age systems to keep up with the trend. However, migration to new systems may come with a set of potential risks which the organization should be able to mitigate and yield the best outcome from the migration.
Potential risks of data migration
- Lack of Transparency: Not allowing key stakeholders to input in the undergoing data migration process is the mistake often made by enterprises. At any stage, someone might need the system to remain operational or would care if the data is being migrated, therefore, it’s vital to maintain complete transparency on the process.
- Lack of Expertise or Planning: The primary cause leading to unsuccessful data migration is lack of expertise. With modern systems getting complex with millions of data points, it’s essential to evaluate which data points must stay operational. As data migration is more about risk mitigation, any disruption may leave IT admins clueless.
- Addressing Data Privacy with Proven Migration Plans: When an enterprise doesn’t assess how many people might receive access to the data in the migration process, potential data breaches can occur. Conducting any data migration always requires proven migration strategies that further raise the probability of its success.
- Defective Target Systems: Projects and vendors must be managed parallelly while flipping the switch from legacy systems to new-gen infrastructure. Suppose an error occurs in either the source system or the target system, it may derail the migration process in the middle of transferring vital data, raising the risk for data corruption.
- Trying to Execute Complex Data Conversion: Unnecessarily making the migration process complex without any tangible increase in outcomes must be avoided at all costs. Complex conversions add extra steps to the process that just makes it challenging to execute. Only undertaking essential migration steps will surely get it done fast.
Why is data migration more about risk mitigation?
As legacy systems are growing organically, the need to adapt to modern business applications are raising concerns with their data quality. There might be millions of data points that must be assessed before concluding which ones must stay operational for any enterprise-scale migration. Along with regulatory and analytical needs, the data must be Extracted, Transformed, and Loaded (ETL) to modern systems without disrupting major business applications. As datasets get complex, things are no longer so simple!
The importance of conducting data migration testing
Once the data has been Extracted, Transformed, and Loaded (ETL) into new-gen systems, what stops it from being deployed? The answer is Data Migration Testing! As enterprises are swiftly migrating their operations to the Cloud, ensuring the integrity of data is key to ensuring further business applications. Here’s how enterprises achieve it:
Data-level validation testing
With certain data migration testing tools, data-level validation testing ensures that the dataset has been efficiently migrated to the target system without any disruptions. With data-level validation testing, data will be verified at:
- Level 1 (Row Counts): Verifies the number of records migrated from the legacy system to the target.
- Level 2 (Data Verification): Verifies data accuracy from any selected portion of the total migrated database.
- Level 3 (Entitlement Verification): Verifies the destination database setup for users and selected data samples.
Application-level validation testing
In contrast, the functionality of the sample application is validated with application-level validation testing. This ensures the smooth operation of the application with the new infrastructure using specific data migration testing tools.
If you are concerned about the risks associated with data migration, you’d be relieved to know that the benefits far outweigh the risks. The importance of expertise and planning is still evident in data migration and data security concerns. In addition to having an efficient and rock-solid data migration strategy, enterprises must also practice data migration testing. Data migration processes remain an activity with potential risks, successfully testing can drastically reduce the migration errors while optimizing future data migration processes.