Functional and non-functional testing: Data masking and data profiling

Introduction: The test data dilemma

Imagine this scenario: You're the lead tester at a large financial institution. Your team is tasked with deploying a critical software update to improve the online banking experience. You submit a request for test data, specifying the need for masked datasets that replicate the complexities of the production environment. Days pass without a response. When the data finally arrives, it's riddled with errors—customer IDs don't match, transaction records are incomplete, and the masking has corrupted some fields.

Frustrated, you send it back for corrections, knowing each iteration will take several more days. This iterative process of requesting, receiving, and correcting data delays your testing schedule and jeopardizes the project timeline. You wonder if there's a better way to handle test data—one that doesn't involve this inefficient back-and-forth.

This story highlights a common pain point in today's data management practices, where the focus is on fulfilling individual masking requests rather than adopting a holistic approach to test data needs. This blog explores the differences between traditional masking tools and a more integrated approach like Synthesized, which streamlines and enhances the entire test data management process, including data profiling.

The current approach to data masking: Fragmented and inefficient

The conventional method of data masking often involves several disjointed steps:

1. Requesting data: Test teams submit detailed requests for specific datasets needed for testing.
2. Receiving masked data: The data management team processes these requests, applying masking techniques to protect sensitive information.
3. Identifying errors: Test teams frequently encounter errors or inconsistencies upon receiving the masked data, requiring further adjustments.
4. Iterative corrections: This back-and-forth cycle of corrections and re-masking can take several days, delaying the testing process.

This approach is not only time-consuming but also prone to errors and inefficiencies. It focuses on masking data as a standalone task rather than considering the broader context of test data management.

Functional testing: Enhanced with Synthesized

Functional testing verifies that an application's specific functions work as expected. Two key types of functional testing are unit testing and integration testing.

Unit testing with Synthesized Unit testing involves testing individual components or modules of a software application. These tests require high-quality, representative data that mirrors production conditions for accurate results.

• Integrated data masking and subsetting: Synthesized ensures that only relevant data portions are selected and masked, maintaining the integrity and usability of datasets. This streamlined approach enables testers to focus on the functionality of individual components without worrying about data inaccuracies.
• Automation and intelligence: Automated workflows in Synthesized rapidly provision compliant test data, eliminating manual errors and significantly reducing setup times. This allows developers to run unit tests frequently and efficiently, accelerating the development cycle.

Integration testing with Synthesized Integration testing verifies that different modules or services in an application interact correctly. It requires data that accurately reflects the complex interdependencies within the system.

• Data profiling for precision: Synthesized scans and profiles the data to identify potentially sensitive columns and tables before running transformation jobs. This ensures that the data used in integration testing is accurate and comprehensive, reflecting real-world interactions between different system components.
• Consistency and accuracy: Synthesized maintains consistent masking across all environments, preserving functional integrity and ensuring that integration tests yield reliable results without data discrepancies.

Non-functional testing: Streamlined with Synthesized

Non-functional testing focuses on the performance, scalability, and reliability of a system. Two important types of non-functional testing are performance testing and security testing.

Performance testing with Synthesized Performance testing assesses an application's speed, responsiveness, and stability under various conditions. It requires large volumes of data that simulate real-world usage patterns.

• Dynamic configuration and real-time masking: Synthesized automatically configures transformation scripts and dynamically syncs with production databases. This ensures that performance testing environments are populated quickly and accurately, reflecting real-world scenarios without compromising sensitive data.
• Efficiency and accuracy: Synthesized reduces the setup time for performance tests by automating the provisioning of test data, allowing testers to focus on analyzing results and optimizing performance.

Security testing with Synthesized Security testing aims to identify vulnerabilities and protect sensitive data like PII or ERP data. 

• Compliance and security: With Synthesized organizations are able to codify data privacy regulations, providing robust governance and access controls. This ensures that all test data processes are compliant and secure, which is essential for effective security testing.
• Data profiling: Data profiling provides a valuable backstop before running data masking techniques, flagging potentially sensitive data in real-time. This protects the data during provisioning while ensuring that engineers are not left waiting for up-to-date test data.

Synthesized: An infrastructure approach to test data

With Synthesized, organizations replace fragmented processes and reduce costs by automating the provisioning of the right data in minutes, not days. Here’s how Synthesized revolutionizes test data management:

• Automated workflows: These workflows quickly provision compliant test data across any environment, scanning and profiling the data to flag potentially sensitive columns and tables before transformation jobs run.
• Dynamic configuration: Automatically configure the transformation scripts, dynamically sync with production databases, and populate the target environment—all through one connected layer of secure test data infrastructure.
• Efficiency and accuracy: Build test environments in minutes, reducing the cost of fixing bugs and delivering stable applications across regions, teams, and organizations. Ensuring masked data is done correctly the first time, Synthesized suggests configuration scripts based on data profiling results and automates regular test data jobs needed for scheduled testing.

By integrating traditional test data management tools with AI-powered workflows, configurations, and management controls, Synthesized provides a comprehensive platform that automates the access and provisioning of test data across environments. This infrastructure approach ensures that testing and development teams have the right data when and where they need it, reducing risk and accelerating software delivery.

For CIOs and senior executives, shifting towards a holistic approach to test data management is crucial. Organizations can overcome the inefficiencies of traditional methods by integrating advanced data masking, subsetting, and profiling techniques with automation and intelligence. Synthesized offers a comprehensive solution that streamlines test data processes, ensuring timely and accurate data delivery, robust security, and regulatory compliance. Embrace this future-forward approach to transform your test data management strategy and drive operational excellence.