Wanted for Legacy Application Modernization Services: Test Data

Legacy application modernization services replace outdated software systems with modern solutions to enhance functionality, maintainability, and usability.

Table of Contents

What are Legacy Application Modernization Services?
The 5 Rs of Legacy Application Modernization
Legacy Application Modernization Services Require Data Testing on Many Levels
The Entity-Based Approach to Test Data Management

What are Legacy Application Modernization Services?

Legacy application modernization services update older (mainframe or client/server) software programs to newer (cloud or hybrid) computing frameworks, languages, and infrastructures.

Imagine renovating an old house to reap the benefits of modern efficiency, safety, and structural integrity. Instead of retiring a current system, or replacing it, legacy application modernization services extend the lifespan of enterprise software, while taking advantage of emerging technologies along the way.

Much of the go/no-go debate around legacy application modernization is about monolithic, on-prem applications – typically maintained and updated by waterfall development processes – and how these systems can be migrated into more agile cloud environments, to leverage today’s service-oriented applications and data.

The benefits of legacy application modernization services include:

• Accelerating the delivery of new features

• Discovering the functionality of current apps for consumption by APIs, or other services

• Migrating systems from on-prem to cloud environments, for increased performance

The 5 Rs of Legacy Application Modernization

Legacy application modernization may be a top trend, but it isn’t a new concept. In 2011, Gartner introduced the “5 Rs model” for application modernization strategies. To understand why application modernization is a driver for test data management, you should get to know each strategy’s implications on testing, which range from light to heavy-lifting projects.

1. Rehost
   The redeployment process of apps is a relatively easy strategy to implement, mainly if your app already executes on the cloud. The process doesn’t require in-depth architecture altering, making it faster on the one hand but less scalable on the other. If you're looking for a quick and effective solution, this may be an excellent first step.

2. Refactor
   This strategy is focused on cloud infrastructure and requires code refactoring to unlock new business use cases. The code may require certain updates, and teams often choose this strategy when a custom application is involved. 

3. Revise
   For this strategy, teams first have to modify the code to support the modernization process and then rehost or refactor the app to be moved to the cloud environment. The app must be cloud compatible or re-architected, which may be more complex than other strategies.

4. Rebuild
   We often assume that legacy apps must remain the starting point to every data migration to the cloud, but sometimes they simply don't serve the purpose. When choosing your rebuilding strategy, discard the existing code and focus on redoing it. The process is longer and more demanding, but can also yield improved scalability and additional cloud-native capabilities. 

5. Replace
   Once again, holding onto existing apps doesn’t always make sense. Replacing your current code with the most updated technology may seem out of sync and daunting. But if your team believes that the optimized result is worth it, investing in replacement – rather than keeping the outdated app alive – is the way to go.

Legacy Application Modernization Services Require Data Testing on Many Levels

Applications, of any kind, require extensive test data before they can be released into production. In a legacy modernization program, the modernized software components require continuous testing.

Here’s why application modernization is a top driver for test data management tools:

• Test data management via data pipeline
  Legacy application modernization services are, by and large, costly and time-consuming. Testing the modernized applications requires a data pipeline, to move the data from old formats, structures, and technologies to new ones. Data migration and data masking with referential integrity – from production systems to the test environments – is labor-intensive, error-prone, and takes too long. Select test data management tools that include data pipeline tools to provision test data from a legacy system to a modern application of a different technology, architecture, and data schema
  

• Integrate test data management into your CI/CD pipelines
  Modernization is typically incremental, and follows a continuous application modernization approach. Choose a test data management tool that can be easily integrated with your agile development methods, and embedded into your CI/CD pipelines.

• Protect user privacy
  Today’s regulatory environment demands that enterprises comply with data privacy laws by anonymizing all clearer picture data. The right test data generation solution should have inflight data masking built in, in order to protect sensitive data before it is delivered to the appropriate test environments.

• Synthesize data for new functionality
  Enterprises are frequently interested in adding new features, above and beyond the functionality that has been modernized. In such cases, synthesized test data is often used for the new functionality. Find the test data management approach capable of synthetic data generation, thus assuring relational integrity.

The Entity-Based Approach to Test Data Management

The entity-based test data management approach improves efficiency on all levels – where the entity can be a customer, product, order, and more. The data from such entity instances is unified and stored in a Micro-Database™, which makes managing and harnessing the data for testing procedures easier and more accurate.

Test data is collected and unified from the source systems as a business entity, obfuscated via data making tools, data tokenization tools, or synthetic data generation tools, and then provisioned to the target test systems. This approach greatly simplifies test data management, ensuring referential integrity, efficiency, and control of the entire process.

With an entity-based approach to test data management, business entity data is ingested into a centralized test data warehouse, enabling testing teams to apply selection criteria to the business entities to create data subsets – and then, provision them, on demand. The warehouse also supports data versioning to enable test data rollbacks, and segregation.

The business entity/Micro-Database/test data warehouse combination allows for significant data compression, so massive amounts of test data can be handled, while maintaining full relational integrity. The result is a faster, safer, friendlier test data management process, delivering more accurate results, more quickly.