Why Application Modernization is a Top Driver for Test Data Management

Tali Einhorn

Tali Einhorn

TDM Product Manager, K2View

Application modernization stands out as a top data management trend for the enterprise. This article discusses what it is, and why it’s driving test data management initiatives.

Table of Contents


Application Modernization Background
The 5 Rs of Application Modernization
Application Modernization Requires Data Testing on Many Levels
The Entity-Based Approach to Test Data Management

Application Modernization Background

Application modernization is all about updating older (e.g., legacy mainframe or client/server) software programs to newer, more modern computing frameworks, languages, and infrastructure.

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, application modernization extends the lifespan of enterprise software, while taking advantage of emerging technologies along the way.

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

Application modernization benefits include:

  • Speeding up the delivery of new features

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

  • Migrating systems from on-premise to cloud environments, for simpler scaling and increased performance

The 5 Rs of Application Modernization

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 TDM, you should get to know each strategy’s implications on testing, which range from minor 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 cloud migration process, but sometimes, they simply do not serve the purpose. When choosing the rebuilding strategy, teams discard the existing code and focus on rebuilding 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.

 

Application Modernization Requires 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 must be tested continuously.

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

  • TDM with data pipelining
    Legacy modernization projects are, by and large, costly and time-consuming. Testing the modernized applications requires pipelining data (from old formats, structures, and technologies) to new ones. Moving and masking data with referential integrity – from production systems to the test environments – is labor-intensive, error-prone, and takes too long. Select TDM tools that can include comprehensive data pipelining facilities to provision test data from a legacy system to a modern application of a different technology, architecture, and data schema.

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Continuous testing of apps, on-premise or in the cloud, enhances efficiency, security, and functionality. 

  • Integrate TDM 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 TDM method should have in-flight 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 TDM approach capable of generating synthetic test data, this assuring referential integrity.

The Entity-Based Approach to Test Data Management

TDM based on business entities 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™ (mDB), which makes managing and harnessing the data for testing procedures easier and more accurate.

Test data is collected from the source systems, unified and masked as a business entity, and then provisioned to the target test systems. This approach greatly simplifies TDM, 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 (TDW), enabling testing teams to apply selection criteria to the business entities to create data subsets – and then, provision them, on demand. The TDW approach also supports data versioning to enable test data rollbacks, and segregation.

The business entity/mDB/TDW combined approach allows for up to 90% data compression, so massive amounts of test data can be handled, while maintaining full data integrity. The result is a faster, safer, friendlier TDM process that delivers more accurate results, more quickly.

Learn more about app modernization and other trends driving TDM.