The process of data validation ensures the data quality of transferred data across the source and target systems. It’s about ensuring that the data on the target side matches the data on the source side in order to avoid business disruption when the system goes live. Data validation is an important phase in the cloud migration process since it ensures that everything has migrated correctly and will perform as intended, resulting in accurate results.
One of the most essential variables determining the success of the data migration process is data validation. Data validation can be done in stages to catch mistakes in migrated data as soon as possible. The functional experts and migration team must ensure that the migrated data is accurate and that the prescribed system transactions are completed successfully on the new platform.
Data movement processes are quite a problem in projects that include phases of migration, integration, or updating of information in today’s IT context, which is defined by a variety of sources, systems, and repositories. Data validation is critical in almost all of them if we want to have dependable data that is consistent, accurate, and complete.
It is necessary to have solutions that optimize validation tests through different options and automation, such as Informatica’s Data Validation Option (DVO), a complementary tool to PowerCenter that combines different benefits in this regard, in order to achieve efficient, easy-to-execute validation tests in line with current demands.
For a variety of reasons, such as infrastructure upgrades, old technology, and optimization, large amounts of data held in the Source storage are migrated to alternative Target storage in Data Migration projects.
The fundamental goal of such initiatives is to migrate data from a source system to a target system in such a way that the data in the target system is highly useful without causing any business disruption.
Data validation is necessary once again to ensure that the data on the source is the same in the target following the migration.
Advantages of Optimising Data Validation
Because data validation has undeniable benefits for the success of these projects, we can enhance the benefits by optimizing it. In other words, we will get qualitative advantages by executing it based on the advantageous characteristics supplied by the leading market proposals, rather than having to program or do the already obsolete manual efforts.
These are some of the primary reasons why you should optimize your project’s data validation:
Complete and Effective Validation – Without having programming experience, data validation cuts the time it takes to audit and validates data by up to 90%. Data validation testing will be more comprehensive and give more data accuracy than manual systems.
Reduce Risks – Traditional data validation approaches can result in additional errors and are harder to implement. As a result, optimizing also entails lowering risks during the data testing and validation processes, resulting in improved data correctness and consistency.
Ensure accuracy and reliability of Data – We shall have a reliable and independent mechanism to verify that the data integrity has not been harmed by the migration and/or alterations.
Better business decisions – Obtaining reliable data facilitates trust-based decision-making, which is vital when it comes to critical business decisions that might be harmed by inaccurate data. Because the load can be conditional on passing the test in automated data validation, we ensure that the upgraded systems have reliable data and thereby improve decision-making.
Simplicity and fast application – Its optimization will make it considerably faster and easier than manual implementations. We will be able to dramatically minimize data testing as well as validation time and resources by 50 to 90%, among other benefits. Business analysts and data validation developers will have a simpler time designing rules because no programming is necessary.
Fewer Resources – More tests can be completed in less time and with fewer IT resources, resulting in cost savings that make testing all of your data much easier.
Lower costs – The expenses associated with data validation processes are also decreased, thanks to the resource and time reductions provided by optimization. The IT team will be more productive (able to validate more data in less time) as a result of the help of pre-built operators, which remove the requirement for programming experience.
More versatility – Optimization entails adaptability. Tool-based tests, for example, can be shared and reused, whereas full test validation can be performed when time is limited. Automatic data validations can be scheduled as part of a process or script, while the production system is frequently transferred or altered before the system is upgraded.
Good data governance – When it comes to facilitating the successful implementation of the data governance program and, in short, enhancing data governance plans, optimal validation will be a crucial point in favor. This is, in fact, a crucial aspect of it. Similarly, avoiding risks will favor regulatory compliance. Reports on audits – Another benefit is the ability to generate reports on all tests and their findings. Finally, knowing that you can rely on the findings of each test as soon as they are completed keeps you informed about the proper operation of your systems.