Struggling with managing your test data effectively? We've compiled a list of the top 10 test data management tools available today, with a breakdown of their features and benefits.
From data generation to import, storage, usage, and management, these tools will help you streamline your test data management process and ensure high-quality testing.
One of the most critical components of software development is test data! Without test data, it’s just not possible to deliver any software that meets customer’s expectations. Test data ensures software’s security, integrity, and compliance. Since it plays a crucial role, it’s important to have an appropriate framework to handle it. That’s why we have crafted this post for you, listing the best test data management tools.
Before we get to the tools, let’s try to understand the basics of test data management.
Test Data Management (TDM) primarily aims at creating, managing, and maintaining the test data to perform efficient testing. This process enables the QA teams to follow a streamlined approach to control a project’s data, files, and policies.
It is through test data management that QA engineers can segregate the test data from the production data, track bugs and perform other QA processes. A primary benefit of leveraging test data management is its ability to minimize the test data size as well as centralize the testing documentation.
Generally, a test data process has the following five stages:
Planning: At this primary stage, the QA engineers plan the types of tests to be executed, analyze what may be required to perform these tests, and create comprehensive documentation.
Analysis: QA engineers collect the data and consolidate critical information at this stage. These include info regarding data access, backups, and storage.
Design: The design stage takes place before the test data management implementation. At this stage, the strategy for data preparation is designed. Here, QA engineers can choose from synthetic data or clone the available databases to perform testing. Testers should be able to identify the sources of data, its providers, as well as the environment in which the data will be loaded.
Build: At this stage, the QA engineers finally implement the plans and strategies discussed in the design and analysis phase.
Maintenance: To achieve successful test data management, QA engineers troubleshoot and fix the errors necessary to perform effective QA while responding to the test data request and making updates on the go.
TDM tools are software solutions designed to facilitate the creation, provisioning, and management of test data for software testing processes. These tools help ensure that testing environments have the necessary and realistic data to execute test scenarios effectively. TDM tools offer various functionalities, including data generation, which involves creating synthetic or anonymized test data to simulate diverse scenarios. Additionally, these tools incorporate data masking or anonymization features to protect sensitive information in compliance with data privacy regulations.
Integration with test automation tools is a notable feature of TDM tools, ensuring that automated tests seamlessly access the required test data during execution. Furthermore, these tools often include features for data governance, empowering organizations to maintain control, compliance, and auditability of their test data processes.
IBM InfoSphere Optim
CA Test Data Manager
Delphix
Informatica Test Data Management
Data Discovery and Creation
The tool identifies and profiles existing data sources, understanding the structure,
relationships, and constraints of the data.
Data Masking and Anonymization
Sensitive or personally identifiable information (PII) is masked or anonymized to protect
privacy and comply with data security regulations.
Subset Creation
Another step is to create subsets of production data tailored for specific testing needs
that ensure only relevant portions of the data are used in test environments.
Data Cloning and Refresh
This step is to create replicas for testing to maintain consistency and integrity in test
environments.
Data Versioning
Test data management tools support the versioning of datasets, allowing teams to trace and
control modifications made to test data.
Integrations
These tools easily connect with test automation tools, providing access to the data during execution.
Monitoring and Reporting
These tools include monitoring and reporting features to track the usage, modifications,
and health of test data is a differentiating factor.
Today, a plethora of test data management tools are available for QA engineers to leverage. Every tool comes with its specialties and drawbacks.
To ease the process of selecting the best possible tool for test data management, the following comparison criteria may be taken into consideration:
Should have an engaging User Interface (UI): A user-friendly UI is a common trait for any tool. This is important for a test data management tool as it becomes easier to create and manage test data.
Ease of usage: The test data management tool should be easy to use. Everyone, including the developers and the managers, should be able to use this tool without special training.
Ease of integration: The test data management tool has to work with many third-party extensions. Hence, the tool of choice must seamlessly integrate with standard CI/CD tools such as JIRA, Azure, etc.
Build: At this stage, the QA engineers finally implement the plans and strategies discussed in the design and analysis phase.
Maintenance: To achieve successful test data management, QA engineers troubleshoot and fix the errors necessary to perform effective QA while responding to the test data request and making updates on the go.
Testsigma is an exceptional tool for managing test data, offering a wide range of features and benefits specifically designed for data-driven testing. Here's why Testsigma stands out in this area:
Simplified Data-Driven Testing: Testsigma simplifies data-driven testing by enabling testers to effortlessly create and manage test cases that can be executed with different sets of test data. This ensures comprehensive test coverage and validates various scenarios effectively.
Flexible Data Sources: With Testsigma, you can easily import test data from diverse sources such as spreadsheets (like Excel or CSV files) and databases. This flexibility allows seamless integration with your existing data sources, making it convenient to work with your preferred data sets.
Dynamic Data Binding: Testsigma empowers testers to dynamically bind test data to test steps. This feature simplifies parameterization of tests, eliminating the need for manual data manipulation. It saves time and effort during test creation and maintenance.
Data Generation and Randomization: Testsigma provides built-in capabilities for generating dynamic and randomized test data. By leveraging this feature, testers can create test cases with varying data values, facilitating better test coverage and identification of potential data-related issues.
Data Validation and Assertions: With Testsigma, you can define data validations and assertions within your test cases. This ensures that expected data values or patterns are verified, promoting accurate and reliable data validation.
Test Data Reusability: Testsigma promotes test data reusability, allowing you to create and manage reusable data sets across multiple test cases. This eliminates duplication of effort and simplifies test maintenance.
Intuitive Test Data Management Dashboard: Testsigma provides a centralized dashboard for managing test data, providing clear visibility and control. With this feature, you can easily organize, edit, and update your test data, enhancing efficiency and collaboration within your testing team.
K2View is a data product platform that democratizes data access in an organization. It continuously syncs, transforms, and gives access to data via data products. The tool delivers a real-time and holistic view of business enterprise (device, customer, order, claim, etc.) to any data consumer. Every day, almost 5+ billion data queries are executed on this platform, delivering 200ms of response time for each data consumer query. K2View Data Product Platform also manages multiple workloads (like data pipelining, data tokenization, customer 360, and cloud migration) at a fraction of cost and time and at a massive scale.
Users can deploy multiple architectures, such as
Centralized data fabric
Multi-domain data hub
Federated data mesh
It can also be deployed in the cloud as an integration platform as a service (iPaaS), on-premises, or on a hybrid platform. Testers can integrate and organize data from different data sources into micro-databases and deliver them in real time to the target system using any delivery method (messaging, CDC, API, virtualization, ETL, and streaming).
It is a programmable data infrastructure that automates the data in digital transformation programs. Delphix, with its multi-cloud data platform, allows enterprises to modernize outdated apps 20% faster, release software 50% more quickly, and facilitate cloud migration 30% faster. It does all these tasks while maintaining compliance with CCPA, HIPAA, GDPR, and other data privacy regulations.
Delphix can make a complete clone of databases like MS SQL, PostgreSQL, Oracle, DB2, MySql, and Oracle EBS, SAP, in minutes without any storage cost. Its capabilities include:
Virtualization: Allows users to use up to 10 times of storage allotted to Delphix.
Masking: Offers profiling and discovery options for sensitive data, which should be masked before forwarding for testing, UAT, and other environments.
Replication: Allows users to replicate any data from the production network to the test network.
Time Travel: Syncs with production databases, allowing users to create database clones anytime.
Automation: Users can call Delphix operations via REST API because the Delphix DataOps Platform is an API-first product.
This test data management tool helps users generate relevant and reliable production-like test data quickly. The data generated with AVO iTDM allows testing experts to speed up their entire testing process. It automatically identifies and manages PII (data discovery), offers data provisioning and generation, and secures sensitive data for PII compliance. AVO iTDM supports open architecture and custom modules. It is built and deployed using open-source technologies and container frameworks and can handle billions of records simultaneously. With the AVO Test Data Management tool, users can:
Fasten application delivery by expediting testing
Identify and resolve non-compliant data issues in non-production environments
Generate and deliver relevant data downstream
Comply with configurable and continuously evolving on-demand data privacy regulations
It is a SaaS-based end-to-end test case management tool that facilitates the entire QA process. PractiTest:
Supports documentation
Provides a roadmap from requirements to test cases
Has the ability to create/run/edit test cases
Integrates the log defects into the defect management system
Provides better control and visibility for all the interactions via easy-to-understand reports and dashboards
Whenever you create a test case in PractiTest, any changes made afterward from any platform location will be reflected automatically throughout the application. It saves a considerable amount of effort and time during the QA process.
Testim.io is an AI-powered test automation platform that offers authoring and execution, troubleshooting, reporting, maintenance, etc. of automated test cases . Because of Dynamic locators, the platform focuses more on UI testing, functional testing, and end-to-end testing. Testim.io improves the stability and extensibility of the test cases. Besides, it allows QA teams to extend the functionalities via complex programming logic by utilizing HTML and JavaScript.
This platform allows testers to:
Run tests locally and in the cloud
Integrate with CI/CD systems
Capture logs and screenshots of test runs
Prepare detailed reporting of each test run with graphs, stats, and other information
Testim can easily align with a shift-left paradigm where QA experts and developers can write their test cases right from the beginning without overwriting each other’s changes. This gives them the flexibility to collaborate and write automated test cases via built-in functionalities.
It is an enterprise test data management tool with the facility to integrate with DevOps and third-party tools. As a test management tool, qTest enables the following:
Run tests with centralized control and orchestration of workflows
Ability to share and reuse test cases for ease of creating and maintaining test cases.
Integration with open-source and third-party tools such as Jira, GitHub, Selenium, etc.
Provides customized and shareable test data reports from 60+ report templates.
QA Touch test management tool offers a natural and intuitive method of execution and comes with an in-built allocation and tracking feature. Users can copy and move the test cases within modules in a project as well as across projects. QA Touch allows the creation of test cases, test suite bundling, third-party integrations, and test reporting.
Other key features include:
Requirement mapping with appropriate test cases.
Mind Map feature to scribble and visualize future testing strategies.
Creating and managing BDD test cases.
Custom project reports, test coverages, and real-time status of release cycles.
Integration with tools such as Jira Server, Bugzilla, and Lighthouse.
The TechArcis Test Data Management automation allows testers to reduce build errors. Additionally, the tool provides service virtualization, which eases test data creation and management.
Key features of TechArcis include:
Test data and test environment automation
Provides accurate data for effective test results
Enhanced test coverage with better-quality test data
Reduced data exposure for risk mitigation
Data masking to enable the privacy of data
Regulatory compliances such as HIPAA, SOX, PCI, etc.
This synthetic data platform allows you to generate synthetic data for different testing use cases. Synthesization is a great way to anonymize sensitive customer data and you can also use the process to subset or upsample datasets into smaller or bigger, realistic versions of themselves. Synthetic data can be used in the following ways:
to replace production data
to subset large datasets into smaller, more manageable ones
to create bigger datasets from small ones for performance and stress tests
to generate erroneous and diverse data for reliability testing
Easy-To-Use User Interface: Professional QA engineers are aware of the importance of attractive and user-friendly app UI. A test data management tool with an attractive UI makes it easier for team members, including Dev, QA, and all other stakeholders, to overview the project quickly. Teams spend their time on something other than specialized training.
Enable multiple and customized access: A good application is made when individuals come together from diverse backgrounds, such as project managers, developers, stakeholders, testers, and so on. Some of these individuals would require read-only access, whereas others would need master access, depending on their roles and responsibilities. The test data management tool should provide the facility of multiple as well as customized access so that permissions can be granted accordingly.
Tracking and traceability: When working in an agile environment, it helps to trace every change made to the application. The test data management tool should be able to provide the required tracking and traceability. This enables the teams involved in the project to follow the changes made by other departments and provide the necessary accountability for every individual.
Facilitate scheduling and organization: Effective planning and scheduling are critical to every successful project. The test data management tool should be able to assist with scheduling, planning, resource management, and organization. A good test plan is necessary to ensure everything is in its place, which becomes even more critical in the case of complex and large projects. A reliable test data management tool should provide essential insights into how the dev and QA teams will execute the scheduled test plans and deliver the project on time and as per the desired quality.
Provide Metrics and monitoring: The test management tool should be able to provide real-time metrics and monitoring to ensure the project is being executed as per the defined timeline and budget. High-quality test data management tools feature customized configuration options to enable tracking on demand as well as determine the project goals to ensure timely releases. The tool can compare the progress of the project in real-time with the predefined timelines, hence reporting issues and delays to the stakeholders.
Flexibility: Every test plan and project has its unique requirements and goals. A practical test data management tool is able to adapt as per the project expectations and provide customized features to the teams. This means testers can add or eliminate the fields in a report or even optimize the access to the stakeholders as and when required.
A test data should have the following properties:
The test data should clearly declare the entity or scenario it is going to validate.
It should be able to complete the number of scenarios/cases to be tested.
It should be segregated based on quality to ensure more defects are found.
It should be easier to group specific scenarios or cases for better clarity and coverage.
There should be a good balance between positive and negative test cases.
Test cases should be ranked based on priority, so tests may be prioritized when teams run short on time.
Test cases should be automated effectively so that in case of test failure, no other tests stop the overall execution.
Enhances effectiveness: When the test data management tools are in place, the overall effectiveness of the testing, as well as app delivery, enhances significantly. By enabling high-quality test data, testers can increase comprehensive test coverage, accelerate high-quality app delivery, and improve process agility.
Saves time and cost: Test data management tools enable quick provisioning of the necessary test data. This allows testers to find bugs early and eventually save the cost of fixing them at later stages. Testers also save time generating QA reports for the stakeholders, giving them more bandwidth for innovation.
Ensures quality and privacy: Test data management tools are secure and half high quality. This allows the associated teams to adhere to the compliances and regulations of the organization. Prevention from a data breach also results in maintaining client trust as well as keeping the organization ahead of the competition.
Implementing an effective test data management strategy is critical for successfully creating and maintaining test automation scripts. Here are a few tried and tested methods that can be leveraged for effective QA implementation.
Externalize the data: Always externalize the automation data to files like .csv. Since the data may change with time, it is essential that this change does not affect the source code and that testers can update this data without struggling with the code.
Have unique prerequisite data for each automation cycle: Creating prerequisite data within automation whenever possible ensures the data is unique for every automation cycle and testers do not face issues with reusing the same test data.
Leverage all test environments: Testers should remember that most projects run on multiple test environments. When creating the automation strategy, it is essential to undertake this fact so that test data management becomes scalable and flexible.
The following are the common types of test data:
Blank data: This data measures how the application responds in case no data is entered into the system.
Valid test: This test identifies how the system reacts in case correct data is entered. A valid test is used to validate the primary feature of the app.
Invalid test: This test measures how the app performs in case invalid data is entered into the system.
Boundary conditions: This type of test validates multiple combinations of data.
Huge test: This test analyzes if the app is prepared for additional tests such as load testing and performance testing.
Effective test data management is crucial for successful testing, which is why Testsigma emerges is a top data-driven testing tool. With its comprehensive set of features and benefits, Testsigma simplifies the process of managing test data, making it easier to achieve accurate and reliable testing results.
From supporting data-driven testing to offering flexibility in data sources, dynamic data binding, and data generation, Testsigma empowers testing teams to enhance test coverage and efficiency. Its intuitive test data management dashboard and focus on reusability further streamline the testing process. With Testsigma, you can ensure the delivery of high-quality software by effectively managing your test data.
