Understanding test data management: key challenges and solutions
When it comes to developing high-quality software, effective test data management (TDM) is a true unsung hero. Any data used during application testing needs to be realistic enough to be useful while also adhering to privacy standards and regulations. Test data management services ensure that test data maintains the correct balance between usability and privacy, allowing companies to build and test software effectively while maintaining peace of mind.
This article will guide you through the essential tools and strategies of TDM, from the careful masking of production data to synthetic data generation. We’ll demonstrate the vital role TDM can play in accelerating development processes and, ultimately, enhancing software quality.
What is test data management (TDM)?
Test data management services help developers create reliable, secure environments for testing. By mapping testing scenarios to real-world ones, TDM helps development teams predict how their applications will perform, allowing them to address potential issues before they affect users. Effective TDM benefits not only developers but also QA professionals, IT security teams, and compliance officers, to name a few. Using TDM strategically can enhance both the quality and performance of software products––and reduce the risk of costly failures in production.
Types of test data
Optimizing the efficiency and security of software testing requires understanding the different types of test data according to their source and preparation. Here’s an overview of the main categories:
Production data
Production data is actual data extracted from live environments. It's often used for high-stakes testing because it most accurately reflects real-world user interactions and scenarios. But in regulated industries, in particular, and as a result of strengthening data privacy laws, it is often off limits to developers.
Data subsets
Data subsets are smaller segments of a larger dataset. These can provide some financial savings on compute, storage, and licensing costs and are efficient for tests that do not require the full breadth of data but still benefit from real-world accuracy. They also help reduce the risk of data leakage by minimizing the data footprint in use.
Masked data
Masked data takes sensitive production data and obscures identifying details to protect user privacy and comply with data protection regulations, making it safe for testing environments.
Synthetic data
Synthetic data is artificially created data that mimics real datasets. It is useful when real data is unavailable due to size or security reasons or when testing needs to cover scenarios that are not included in existing data.
Each type of test data has its unique role in a comprehensive testing strategy, helping teams validate application performance under varied specific conditions.
Test data challenges
While TDM sounds like a no-brainer, it comes with its own challenges. Potential issues that can complicate TDM implementation include the complexity of the data structures and underlying relationships, working across a variety of data sources, and ensuring compliance with data privacy regulations like GDPR.
Ensuring that test data remains up-to-date and consistent with production data adds another layer of difficulty, as it requires continuous refreshes and synchronization. Test data should be as close to real production data as possible in order to allow developers to mimic user interactions and scenarios, but generating and maintaining such data without breaching confidentiality and security standards often proves difficult.
The costs associated with setting up and maintaining complex TDM systems can also be noteworthy, especially as systems and data volumes are constantly increasing. Limited access to isolated test environments and the need for scalable solutions further compound these challenges, emphasizing the need for strategic planning and careful investment in effective and efficient TDM solutions and processes.
Test data management techniques
Effective test data management employs several techniques to balance data security and compliance with data utility in testing environments. These techniques include:
Data masking
Data masking does exactly what it sounds like: it protects sensitive data by obscuring the original data with modified content. This technique is extremely helpful in environments where data privacy is paramount, such as when complying with GDPR or HIPAA regulations. Techniques for data masking include substitution, shuffling, encryption, or nullifying data. Each technique offers a different approach to obscuring sensitive data based on your specific testing and compliance requirements.
The main advantage of this technique is that it can protect sensitive data while still making it realistic and useful for testing purposes. However, masking can be resource-intensive to implement and manage without the right automation available. Additionally, if not properly done, data masking can result in data that is either too unrealistic for effective testing or still poses a privacy risk.
Data subsetting
Data subsetting creates a smaller version of a dataset designed for testing purposes. By reducing the volume of data handled, you can both speed up the testing process and make sure the subset is targeted to the use case at hand. This approach can help reduce data load times and the resources needed for data storage and management, boosting the performance of your testing processes.
The challenge with data subsetting is ensuring that it maintains referential integrity and includes all the necessary data within the subset to achieve effective testing.
Synthetic data generation
Synthetic data generation creates artificial data that mimics the statistical properties of real-world data. This technique can be used in tandem with masking, when additional data is needed for testing purposes, or when access to actual data is restricted.
Synthetic data generation can provide a high volume of data while mitigating privacy concerns, and it allows for testing specific data scenarios that may not be available in existing data sets.
That said, generating realistic data to accurately reflect real-world conditions can be complex and time-consuming. There is also the risk that synthetic data might not capture all the nuances of real data, which could lead to inadequate testing. Synthetic data can be very useful in early development stages or when real data is too sensitive to use, but it requires sophisticated algorithms to ensure that the generated data is as realistic and useful as possible.
Test data management tools
The right TDM tools are essential for successfully creating, managing, and securing test data throughout the software development lifecycle. By automating the processes involved in TDM, these tools can help maximize developer productivity while maintaining compliance with data protection standards. Organizations can use them to streamline their testing processes, enhance product quality, and accelerate time to market.
Here is a list of some commonly used test data management services and tools:
- Tonic.ai - Excels in generating high-fidelity test data that closely mimics production data, providing an enterprise-grade TDM platform for developers needing comprehensive and compliant data de-identification solutions.
- Delphix - Offers a legacy test data management platform, with a focus on data virtualization, and lacking in support for cloud-native data and complex data types.
- IBM InfoSphere Optim - Specializes in data lifecycle management, with some data masking capabilities.
- Informatica - Focuses on data integration, and acquired a test data management solution, adding TDM to its capabilities.
- Redgate - Offers data masking capabilities via a basic UI and CLI.
These tools represent a spectrum of solutions tailored to meet various TDM needs, from simplifying data transformation to ensuring thorough data privacy compliance.
Use cases
Financial services provider Paytient works with employers and health plans to offer employees and members a fee-free, interest-free line of credit to pay out-of-pocket medical, vision, dental, pharmacy, and veterinary expenses.
Paytient needed to protect customers’ sensitive personal and financial data in testing environments while also providing their software development team with safe, accurate test data. They knew that, given the complexity of the data they handle, building a custom solution would be too time- and resource-intensive––not least because their data included flat file data types along with relational databases, making it even more challenging to securely manage sensitive data across multiple formats.
Tonic.ai’s Tonic Cloud platform helped Paytient address these needs by enabling secure data de-identification in the cloud, which streamlined Paytient’s ability to conduct SOC2 audits and maintain compliance with other data protection regulations. The dev team could access production-like data quickly and securely, saving them hundreds of development hours. And, most importantly, Tonic Cloud allowed Paytient to ensure their customers’ data remains secure.
Overall, Paytient saw a 3.7x ROI after integrating with Tonic Cloud, making it an essential part of their software development and data management processes.
Get started with Tonic.ai
As we've explored above, effective TDM is essential for ensuring the security, compliance, and efficiency of software testing processes. Techniques such as data masking, synthetic data generation, and data subsetting not only safeguard sensitive information but also enhance the quality and speed of testing. By implementing these strategies, organizations can handle complex data scenarios more effectively and reduce the risks associated with using actual production data.
Tonic.ai offers solutions that simplify the creation and management of test data, aligning with all the key TDM practices discussed. Whether you're looking to improve your data security, comply with regulations, or accelerate your software development lifecycle, starting with Tonic.ai can help you achieve these goals efficiently. Embrace Tonic.ai to leverage cutting-edge TDM tools and techniques, and set the stage for successful and secure software releases.
FAQs
Copying production data poses significant privacy and security risks, as it can contain sensitive information that must be protected under laws like HIPAA and GDPR. Additionally, the scale of production data can make testing processes cumbersome and inefficient. Instead, TDM uses techniques like masking and subsetting to offer safer, more manageable alternatives.
TDM ensures that test environments are equipped with relevant, high-quality data. This allows you to validate software under conditions that mimic real-world operation without compromising security or performance, making for a more robust, reliable final product.
The reliability of testing results depends in large part on the quality of the test data used. High-quality test data helps simulate real-world scenarios more accurately, allowing for more effective, comprehensive testing. This helps to identify potential issues earlier in the development cycle and reduces the risk of defects making it through to production.
TDM helps ensure test data complies with data protection and regulatory requirements. By using techniques like data masking and synthetic data, TDM helps organizations avoid the legal penalties, costs, and reputational damage that arise from data breaches.
A bilingual wordsmith dedicated to the art of engineering with words, Chiara has over a decade of experience supporting corporate communications at multi-national companies. She once translated for the Pope; it has more overlap with translating for developers than you might think.
