The Top 7 Test Data Management Tools — Syntho
- Published: March 26, 2024 [UPDATED]
Organizations face problems with a scarcity of quality test data and slow provisioning processes for software testing and quality assurance (QA). Even worse, datasets with personally identifiable information (PII) create privacy risks. Masking data is essential for compliance, and coordinated test data management (TDM) can be a game-changer.
TDM tools help configure secure testing environments and generate synthetic test data that complies with data privacy laws. This automates the acquisition of test data and removes privacy risks. Reliable software allows testers and QA experts to generate necessary datasets to cover more testing scenarios while ensuring compliance and limiting data access.
This article illustrates the importance of efficient TDM for modern software development and QA. We also describe how the top data management tools can broaden test coverage, avoid privacy risks, and improve overall product quality.
We’ll introduce you to the critical features and capabilities of TDM software. To help you choose the best software, we have analyzed some of the most popular TDM software solutions on the market. But basics first.
Table of Contents
What is test data management?
Test data comprises manually or automatically generated data for software testing and quality assurance. It should help validate the application’s functionality, user interface, and performance. Companies need this data to improve overall software quality.
The test data should also exclude personally identifiable information and replace or supplement it with artificial data. The testing infrastructure is usually not as secure as the production environment. Without proper management, there are privacy risks and concerns, which can lead to fines, breaches, and data leakage.
Test data management (or TDM) is the process of providing production-like high-quality, relevant data to testing teams. Organizations with a robust test data management process ensure timely delivery of datasets that meet strict technical and legal requirements.
Effective test data management requires software that automates test data creation, management, and distribution. When properly implemented, TDM tools can strengthen your software development lifecycle.
Why is test data management important?
Test data management empowers companies to shift left, moving the testing phase to the earlier stages of software development. By doing so, companies can identify potential software issues sooner, reducing bug-fixing costs and improving the quality of the end product.
Let’s talk about the benefits of strategic test data management processes.
Automated test data delivery
Traditional manual test data provisioning is way too time-consuming for Agile practices. TDM integrated into the continuous integration and continuous deployment (CI/CD) automates this process. Instead of manually requesting and processing data, you can access existing data and synthesize new datasets when needed. This means faster data generation, no queued requests, and a more accurate representation of business logic.
Expanded test coverage
DevOps and QA professionals can explore more code paths and unexpected use cases that venture outside the expected behavior of software systems. An expanded test coverage ensures a higher software development quality and reduces potential issues after deployment.
Improved regulatory compliance
An established TDM process reduces non-compliance risks with data privacy regulations, such as GDPR, CPRA, and HIPAA. Techniques like data masking and synthetic data generation allow you to create test data without personally identifiable information and intellectual property infringement. This means fewer data leaks or risks of legal fines.
Enhanced software security
Effective TDM is critical for DevSecOps — a practice that embeds automated security checks into the software development lifecycle. Rich and diverse test data allows you to remediate more security vulnerabilities with comprehensive testing of security features and protocols. Realistic datasets accurately reflect production environments, allowing for more effective simulation of real-world threats so the end product will be better protected against risks and exploits.
A test data management strategy is a fundamental part of high-quality software development. However, only the right tools can help organizations leverage these benefits.
Why do companies use test data management tools?
Incorporating the right TDM tools allows companies to strengthen their software development lifecycle. The key reason organizations adopt them is their ability to facilitate testing with the following capabilities:
Self-provisioning test data. TDM tools enable users to independently access, manage, and generate relevant data for tests. This results in faster feedback loops and accelerates the development process.
Testing environment creation. You can create dynamic testing environments in TDM software, greatly reducing the time and resources needed to set up and maintain test infrastructure. The tools can support on-premise and cloud environments, allowing various testing scenarios.
Storage consolidation with data subsetting. Data subsetting transforms datasets into smaller, manageable subsets for specific scenarios. This method reduces the data volume and complexity, letting you waste storage for testing.
Preventing data overrides. The TDM software assigns unique identifiers, timestamps, and labels for test datasets, so you can always track changes and revert. Additionally, you can set permissions for users to prevent data tampering.
Compliant data generation. Anonymized and synthetic data creation features can produce datasets without identifiable information or sensitive corporate data, lowering the risk of leaks or regulatory fines.
Reusability of test datasets. You can store, version control, and reuse the data for repeated testing, regression purposes, or other development projects. This is especially useful when verifying fixes for previously identified bugs.
Having delved into the capabilities of test data management, it’s critical to understand how to select the appropriate software for your teams.
How do you choose test data management software?
The choice of TDM tools depends on your business needs, integration capabilities, and data privacy requirements. While every organization is unique, we have highlighted the critical criteria for selecting TDM software.
Compatibility
A TDM tool should work with your systems and consolidate compatible test data from several databases and services. It should offer open APIs and documentation to help your team integrate it into their CI/CD pipeline. Proper integration allows the team to self-provision and generate data in a testing environment, accelerating development.
Scalability
Choose a test data management tool that can handle your growing data volumes, data quality requirements, and operational needs. A scalable test data tool adjusts its resource usage based on the data load, ensuring consistent performance. To ensure reliability, you should assess the software’s capacity to perform under various loads and scenarios.
User experience
An intuitive interface with visual dashboards and easily understandable menus is essential for productivity, and TDM software often includes video guides and manuals. In today’s fast-paced development environment, versatility, usability, and speed are paramount. TDM tools that combine these qualities excel in configuring and generating data efficiently. They enable teams to streamline the testing process and accelerate development cycles, ultimately leading to faster time-to-market for software products.
Data identification
Ensure the tool can identify and categorize various data types in your systems. Advanced TDM systems have a built-in PII scanner that automatically identifies sensitive datasets. You should be able to customize which sensitive data to substitute for testing purposes.
Data masking
The data masking approach includes features that replace sensitive information with fictitious data. For example, it can preserve the format of and utilize the original data but replace customer names with synthetic information.
Synthetic data generation
This synthetic data type refers to artificially created data that imitates the structure and format of real data but doesn’t necessarily reflect actual information. It helps developers ensure their applications can handle various inputs and scenarios without using genuine, private, or sensitive data and, most importantly, without relying on real-world data. Companies rely on synthetic data generation when they need to quickly create high-quality test data.
Consistent mapping
While generating datasets, the TDM tool should maintain the referential integrity between pieces of data. It’s necessary that the datasets reflect real-world conditions and mirror the production data as closely as possible for more accurate test results.
Payment model
Select a tool with a payment model and pricing structure that aligns with your needs. A flexible tool may scale based on your active team members or the amount of generated data. A reliable vendor won’t have any hidden fees, such as charges for data overages or additional users.
Support and maintenance
The vendor should provide regular security updates and functionality improvements. Reliable vendors also help integrate the software, address technical issues, and provide documentation and training to teams.
Online review platforms like PeerSpot, Capterra, and G2 can help you select the right TDM tool. They let you compare features and pricing models, as well as read feedback from other users.
The top 7 test data management tools
The following software embodies the essential features of top-grade test data management. They cater to diverse business needs and functional requirements, but each can make managing test data much more efficient for DevOps and QA teams.
1. Syntho TDM tools
Syntho’s test data management tool offers a comprehensive solution for organizations seeking faster and higher-quality testing processes. The data subsetting feature enables the creation of smaller representative subsets of relational databases, ensuring efficient testing without compromising data integrity. Organizations can also generate data for specific scenarios by adjusting rules, constraints, logic, and other parameters with rule-based functionality.
The AI-powered PII Scanner provides an added layer of security by automatically identifying and replacing sensitive information, safeguarding against privacy breaches and compliance violations.
Syntho allows for efficient test data management with the following capabilities:
- De-identification and synthetization: Generates test data that reflects production data for comprehensive testing and development in representative scenarios.
- Rule-based Synthetic Data: Generates synthetic data to mimic real-world or targeted scenarios using predefined rules and constraints.
- Subsetting: Reduces records to create a smaller, representative subset of a relational database while maintaining referential integrity.
Overall, datasets created with Syntho are completely compliant and behave like real datasets, making them suitable for test data management processes.
2. K2view TDM tool
K2View is an operational data platform that centralizes test data management. Organizations can coordinate test data across multiple data sources and tables. You can subset data while maintaining referential integrity.
This TDM software lets you de-identification data and generate artificial datasets. Thanks to the intuitive interface, the tool is accessible to users without programming expertise.
However, K2View has a slow learning curve due to limited training resources and documentation. It’s also moderately challenging to deploy without the provider’s assistance.
3. DATPROF TDM tool
DATPROF lets teams provision test data that is compliant with privacy regulations such as GDPR, PCI, and HIPAA. The tool is versatile, supporting a wide range of databases, including Oracle, Microsoft SQL Server, and MySQL. This broad compatibility ensures it can integrate into diverse IT environments.
The platform’s capabilities extend to de-identification, synthetic data generation, and data subsetting. It also secures data transmission through encryption and implements role-based control to prevent unauthorized access.
On the downside, the data masking is a bit cumbersome. You can’t easily duplicate the configuration, which requires you to apply rules manually for different use cases.
4. Delphix TDM tool
Delphix leverages advanced virtualization technology that streamlines the provisioning of database copies for software testing and QA. It can completely refresh data for point-in-time recovery, which is essential for DevOps.
The platform features fully integrated data masking that de-identifies your data with advanced algorithms. You never risk breaking compliance laws or leaking sensitive information during the testing process.
Admittedly, Delphix’s pricing is on the higher end. You may also run into errors when replicating and sharing data across environments. Besides, some systems may face integration problems.
5. Informatica TDM tool
Informatica enables data discovery, creation, and subsetting. The product scales both horizontally and vertically and integrates with DevOps CI/CD pipelines.
This platform provides data masking for raw production data to ensure compliance. The onboarding process is smooth, thanks to comprehensive documentation and responsive technical support.
This support definitely won’t hurt during the complicated initial setup. Adding to that, the data subsetting process can be excruciatingly slow on larger datasets. And, although it’s not that much of a con, the interface feels slightly clunky and outdated.
6. YData TDM tool
YData data-centric platform enables the development and ROI of AI applications by improving the quality of training datasets. Data teams can use automated data quality profiling and improve datasets, leveraging state-of-the-art synthetic data generation.
7. MOSTLY AI
The MOSTLY AI synthetic data platform enables enterprises to unlock, share, fix, and simulate data. Although similar to actual data, its synthetic data retains valuable, granular-level information, while assuring private information is protected.
Enhance software testing with robust data testing software
Test data management tools are crucial for demands for software development and QA. They allow teams to get high-quality, diverse data sets for various test cases with little to no administrative bottlenecks. The result is the ability to deliver high-quality software faster with fewer security vulnerabilities.
A standout feature is the ability to generate artificial data. TDM software with a synthetic data module lets your teams create high volumes of compliant test data with zero data privacy risks.
Choosing the right TDM tool provider ensures robust, compliant, and efficient development. Our platform helps you generate, anonymize, and share quality testing data for an unlimited number of scenarios. Companies in the healthcare, financial, manufacturing, and many more industries choose Syntho for their TDM needs.
Want to learn more? For more information, download and explore our Product documentation or schedule a demo.
About the author
Business Development Manager
Syntho, the scale-up that is disrupting the data industry with AI-generated synthetic data. Wim Kees has proven with Syntho that he can unlock privacy-sensitive data to make data smarter and faster available so that organizations can realize data-driven innovation. As a result, Wim Kees and Syntho won the prestigious Philips Innovation Award, won the SAS global hackathon in healthcare and life science, and is selected as leading generative AI Scale-Up by NVIDIA.
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