Test Data Management (TDM) is a critical aspect of the software development and testing process. Poor test data can miss critical bugs, delay releases, and frustrate engineering teams. Test data management aims to effectively handle and control the test data for use in testing, which means that it should:
Before delving into the intricacies of Test Data Management, it's essential to understand the pivotal role that test data plays in software testing. Test data serves as the various inputs needed to simulate real world examples in lower environments, like local development environments and staging environments. The quality and relevance of test data can directly impact the accuracy and comprehensiveness of testing and development efforts. In other words, poor test data will miss real world applications that could potentially break your software in production. For example, if you do not factor in names with special characters, like Zöe or Eleña, then your application may break when it encounters those fields. More commonly you won’t account for all of the structural variations that can exist in your application. In the case of a property ownership in your database, you would have to account for the range of:
TDM solutions typically include one or more of the following components:
1. De-identification/Masking
De-identification or data masking is crucial for protecting sensitive or personally identifiable information (PII) during testing. It involves replacing sensitive data with fictitious or masked values while preserving the data's format and structure. The objective is to generate data that cannot be tied back to any real world individuals. In order to avoid breaking the application you are developing, your de-identified data must maintain referential integrity.
2. Data Orchestration
Data orchestration involves the seamless flow of test data across different testing phases and environments. It ensures that the right data is available at the right time for testing. This often entails automating processes to bring together data from multiple sources, combining, and preparing it. It can also include tasks like provisioning resources and monitoring.
3. Subsetting
Subsetting involves creating subsets of production data to reduce storage and resource requirements while still representing critical data scenarios. This component enhances efficiency in managing and utilizing test data, while also minimizing your data footprint from a security perspective. It can be challenging to pull all of the necessary dependencies while keeping the dataset size small, making subsetting one of the more complex components to deliver effectively within TDM.
4. Database Virtualization
Database virtualization involves creating virtual, isolated copies of databases to provide a controlled and consistent test environment without having to worry about data formatting or where it is physically stored. It allows testing teams to work with real data without affecting the production database and without requiring additional data storage space.
5. Data Versioning
Data versioning ensures that different versions of test data are available for various stages of testing. Each version represents a change in the structure, contents, or condition of the data. This component helps in maintaining data consistency across different testing environments and iterations.
Test data governance is crucial for ensuring data quality, security, and compliance. While the most realistic data sits in your production database, as a result of recent advances in data privacy regulations, that data should not be accessible to everyone in your engineering organization. For example, if you have a 50 person engineering team for an ecommerce platform, using production data in development and testing puts you at risk of exposing sensitive information like credit card numbers to the entire department. With regulations like GDPR and CCPA, organizations are increasingly limited in how they can process and use production data. For software development and testing, masking or de-identifying production data prior to use in lower environments is now a legal imperative. In other words, your test environment should be stripped of all personally identifiable information.
Other ways to ensure compliance and privacy is to set data ownership practices, limiting who has access to PII, setting policies to enforce data masking, and incorporating auditing as a regular part of the data pipeline. Role based access and other features of TDMs can make this process easier.
Test Data Management offers a wide range of benefits to different stakeholders within an organization:
Developers benefit from TDM by having access to consistent and reliable test data, enabling them to identify and fix defects early in the development process.
Devops can leverage TDM to streamline the deployment pipeline by ensuring that test data is readily available and compatible with automated testing processes.
Quality Assurance teams can generate datasets for different testing phases and reduce time it takes to run through all of the test cases.
Engineering Orgs gain efficiency and productivity as it empowers them to execute comprehensive test suites, reduce resource costs (such as storage), and ultimately introduce stability into releases.
There are some common problems with test data that test data tools aim to remedy:
When deciding to go with a test data management platform, here is a checklist for features and functionality that you’ll want to evaluate.
Given technology’s continuously accelerating evolution, the future of Test Data Management lies in its ability to adapt and integrate with ongoing developments. The dynamic nature of the tech landscape necessitates a TDM approach that is flexible and responsive, capable of evolving in tandem with the technologies it supports. This means both incorporating novel capabilities in the generative AI space and integrating with the latest technologies when it comes to data stores and infrastructure. Going forward, effective Test Data Management will depend more and more on the use of automation, Cloud technologies, and of course, AI and machine learning techniques under the hood.
Test data automation streamlines the process of generating, transforming, and managing test data, reducing the risk of human error and drastically improving efficiency.
From sensitive data detection and de-identification, to scheduled test data generation to automatically provision refreshed data to your lower environments, automations eliminate repetitive manual tasks and ensure that developers have the data they need, at the moment they need it.
AI will play an increasingly critical role in TDM, advancing our capacity to handle complex data structures and dependencies. By incorporating machine learning algorithms for AI-driven test data creation, AI can be used to generate synthetic data that simulates real-world scenarios with a level of nuance that unblocks more complex testing, as well as opening the doors for secure data analysis.
The role of Cloud technologies in TDM cannot be overstated. From efficient data storage solutions to data transformation platforms, Cloud technologies offer scalable, cost-effective, and secure solutions for storing and accessing test data. The elasticity of cloud storage allows for improved handling of fluctuating data volumes, making it ideal for TDM. The increasing shift toward the Cloud for data storage and cloud-native applications has heightened the need for cloud-based TDM solutions.
Just like they do for data storage, Cloud technologies provide a more modern, scalable approach to TDM. They offer unprecedented flexibility, allowing teams to access, share, and manage test data across geographical boundaries. Moreover, cloud-based TDM solutions can quickly adapt to changing testing requirements, ensuring that teams always have the right data at the right time.
Test Data Management is a fundamental aspect of modern software development and testing, ensuring that the right data is available at the right time to support efficient and effective testing efforts. By implementing TDM strategies and leveraging its core components, organizations can enhance software quality, reduce costs, and expedite their time-to-market while adhering to data security and compliance standards.
The future of TDM will be defined by its capacity to adapt and integrate with emerging technologies. The incorporation of Automation, AI, and Cloud technologies into TDM strategies will not only redefine the way we manage test data but will also pave the way for more efficient, accurate, and effective software testing processes.
The Tonic test data platform is a modern TDM solution built for today's engineering organizations, their complex data ecosystems, and the CI/CD workflows that require realistic, secure test data in order to run effectively. Built with data synthesis at its core, Tonic’s forward-leaning approach to TDM prioritizes workflow automation, performant Cloud solutions and integrations, and generative AI capabilities to optimize your lower environments and accelerate your engineering velocity with quality, compliant test data. To learn more, explore our product docs, or connect with our team.
