Test data management tools and best practices
The tech industry is constantly looking for optimization in testing, and one such area is test data management (TDM). It is essential because testing completeness and coverage depend mainly on test data quality. It is a known fact without high-quality data; testing assurance is unattainable. The practice of not including test data management steps in the testing life cycle usually results in ignorance of TDM on the part of the software development team.
The best data is found in production; these are actual entries the software application uses. While deploying production data, developing a subset of the data is always prudent. This lowers the efforts entailed in test planning and execution and assists in attaining optimization.
This post will explain why software development teams in the performance, non-functional, functional, and automation test areas need TDM service. We will also discuss the test data challenges faced by testers.
Table of contents:
- What is test data?
- What is test data management tdm?
- How test data management works?
- Benefits of test data management
- Risks and challenges of testing data
- What are the steps for test data management?
- Best test data management practices
What is test data?
Test data is the data deployed to test the software application. For instance: to test the username, login functionality, and passwords required. So the values of password and username are test data. Test data are two types. Namely:
- Transactional data
- Static data
Static data comprises counties, currencies, and names, which are not sensitive. However, when it comes to transactional data, it entails data such as debit card or credit card numbers, medical history, or it can be information about bank accounts; there is always a risk of data getting stolen.
What is test data management?
Test data management is the process of maintaining, storing, designing, and planning software quality-testing methodologies and methods. It gives the software quality and testing team the authority over the policies, rules, files, and data generated throughout the software testing life cycle.
Another name for test data management is software test data management.
In simple words, Test Data is the information and data that we provide in our application to test it. Every firm or project saves or stores test data in excel sheets so that Quality Assurance (QA teams and testing teams) can use it for manual test execution or execute tests, which will be valuable for future references.
How test data management works?
- Four essential TDM techniques empower software testing-
- Automating TDM tasks to accelerate the testing process
- Building test data for reusability
- Validation of test data
- Exploring the test data
Benefits of test data management
Test data management assists companies in creating better quality software that will work reliably on deployment. It creates a more cost-efficient software development process and prevents bugs fixes and rollbacks. Additionally, it lowers the organization’s compliance and security risks. Let us have a deeper look at these benefits:
- Version controlled data requirements and test data ensure complete traceability and simpler replication of results.
- Optimal data coverage is attained via intelligent tools and techniques depending on data analysis strategies.
- Synthetic data can be developed from the ground up for new apps.
- Detailed analysis and review of data requirements
- Errors and data corruption can be lowered by including defined TDM processes in the testing cycles and implementing a test data management solution.
- Well-defined process and controls for data storage, retrieval, and archival support future testing requirements
- Clear data security policies increase data recoverability and safety.
- Detailed analysis of data requirements ensures early identification of problems and resolution of queries.
Reduced resource cost
- Team also wears the system architect hat, therefore understanding data flow across systems and provisioning the right data.
- Data preparation effort and condensed test design help achieve cost savings.
- Professionals with specialized skills and expertise, sharp focus on Test Data, and access to industry-standard tools contribute to the success of TDM
- The data management team’s database or file access facilitates data privacy and reuse.
- TDM practice should attempt to consolidate storage and save costs by sharing common data across environments, not only those deployed for testing but also those used for production support, reporting, development, and other use cases.
Another clear benefit of mastering data is not just for the test but for the whole organization. The benefits include increasing income by exploiting quality data, lowering the danger of heavy fines, and reducing the chances of security breaches to drive effective decision-making. With data privacy rules like the GDPR test, data management becomes more vital because it assists organizations in complying with regulations via compliance analysis and data masking strategies.
Better data coverage
Test data management helps trace test data to test cases and requirements. This gives you a birds-eye standpoint of the test data coverage and defect trends.
Data is provisioned depending on the testing type
A unique functionality offered by a TDM process is that the data is controlled in one place. From the same repository, correct data can be provided for different testing types- Functional testing/ functional tests, integration testing, automation testing, performance testing, regression testing etc. This lowers redundant data copies, and thus the cost of storage is reduced.
The main significance of test data management is good data coverage and quality data. The bugs are unraveled early, with these qualities present during the data testing phase. This results in a stable and high-quality application that has minimum production defects. Customer’s trust level in the organization increases when a client or customer sees such enticing of implementing a TDM process.
To reduce copies of the data
In a project, multiple teams can create numerous copies of the same production data for their use. This leads to redundant copies of the same data and storage being misused. When a TDM is deployed, the same repository is deployed by all the teams, and thus the storage space is utilized diligently.
Data and continuous delivery
Relevant, accurate, high-quality data is vital to the cornerstones of continuous integration- Test coverage, test automation, and continuous testing. We can discover defects earlier in the development life cycle with quality data for a less costly fix and less danger of bugs in production. The end-product fails if testing and QA flop because of poor data quality. The knock-on effect is unhappy customers who complain to millions of social media users and switch their service to another brand, taking family, friends, and followers. Contrarily, incredible information cleanliness improved security and streamlined data management, resulting in digital happiness, improved customer experience (CX), customer loyalty, higher income, and a better brand name.
Cognitive QA and data
Additionally, data discovery and management are the first steps of reaping the advantages of Sogeti’s new smarter approach to testing, Cognitive QA, which uses quality data to allow Artificial intelligence (AI transformation), Automation, Smart QA Analytics, and Robotic Process Automation. For testing, this indicates you can automatically identify the most effective test data sets in your regression suite, automatically prioritize the right test cases for automation and automatically provision self-adaptive test environments. Depending on a foundation of good data, Cognitive QA aligns the test function to your desired business outcomes, lowering risk and delivering quality software and products to market more rapidly for less cost.
The right Test Data management tools accelerate time to value for business-critical applications and build relationships and efficiency by allowing insights-driven decision-making across your entire organization.
Risks and challenges of testing data
Here are the risks you need to consider when creating your test data management strategy are the potential effects of the following:
- The fact that companies operate in a distributed multi-supplier outsourced environment across the SDLC and your data will be utilized in many locations by many users, usually at the same time.
- The time available for generation, discovery, and management
- Different types of data are required for the types of testing, from performance tests to User Acceptance testing, so you need to organize your data correctly from the outset to keep within test time constraints and avoid spiraling testing costs.
- Compliance with all the data privacy laws and regulations, including the new GDPR
- Data security is predominant, so new security processes and cultural shifts are needed to ensure that confidential data and information is not copied into emails and live production data is not compromised in a testing environment.
- IoT, SMACT, new databases and legacy, and all the unstructured and structured data they lied to create a sophisticated data landscape with different types of data stored in multiple environments. It is hard to access and increases the potential for a data breach.
What are the steps for test data management?
Analysis of data requirement
This test data could be required on different interfaces of the software application. The type of data and format may also be different on these interfaces. So the first step of the test data management process is to understand the data requirement of the organization depending on the test cases that will be run. This will require knowledge of the domain, organization, and all the applications entailed in the whole end-to-end process.
For example, a banking system will have customer relationship management (a CRM system), a financial application for transactions, and messaging systems for OTP and SMS. Here, the individual analyzing the test data requirement should have expertise in CRM, domain, and financial application knowledge and messaging system.
Data subset creation
Typically this is the most widely used data creation technique. The real production data is copied to offer different subsets which accommodate all the test data requirements. The consistency, uniqueness, accuracy, and referential integrity of all these features and functionalities of the test data should be attended to while copying the data. Negative testing and data for boundary values are also developed by modifying the subsets or adding some data.
Generally, we are dealing with sensitive production data; it is essential to hide the customer data like credit card or debit card, phone numbers, bank login information, and medical history. Any failure to protect sensitive data might lead to regulatory and compliance issues.
Data testing automation tools
In test data management, automation can be deployed to perform the above tasks of data cloning, subsetting production databases, data generation, and data masking.
Note that manual testing is time-consuming and error-prone since we deal with huge data.
Automation scripts could be developed, or licensed test data management tools such as Delphix DATPROF, information, iBM InfoSphere Optim Test Data management, Informatica Test (and cloud) Data management, Dataprof, and CA Test Data Manager can be deployed. Advanced tools also assist in reporting to help the company make better decisions about test data.
Maintenance and refresh
Generally, there is a central test data repository, which has regulations for privileges and access. The test data requires a periodic refresh to reflect the latest and most-relevant test data. If multiple modules in a project deploy the same test data repository, a properly managed refresh cycle is necessary.
Together with data refresh, the maintenance of the repository is also very essential. Over time, the test data becomes redundant or obsolete. Proper maintenance of the test data has to keep it correct, consistent, and available over time. Contrarily, such data will hold unnecessary storage space in the repository, and the search for relevant test data might take longer than anticipated.
Best Test Data Management Practices
|Subsetting||This practice creates realistic test databases that are small enough to support test runs and large enough to accurately reflect the various production data.|
|Clarifying requirements||Businesses should identify their test data requirements depending on the test cases to optimize the effort necessary to create test data.|
|Refresh test data||Upgrading test data assists in streaming the testing process and maintaining a consistent, manageable production environment, which improves testing efficiencies.|
|Automate test data result in comparisons||Companies should use an automated tool for comparing the baseline test data against results so that companies can rapidly identify problems that might otherwise go undetected.|
|Masking||Ensure that leveraging production data sources and sensitive information is obfuscated or masked.|
Test data creation is carried out by the testing team; often, the testing team does not have direct access to the production data. Even if the production data is offered, it is a large chunk of raw data.
This raw data cannot be deployed directly for testing purposes; considerable data testing efforts are required to sort, manage and tailor the data. High-quality data is a basic need if you plan to have high-quality software testing. Average data quality will offer mediocre results after testing, and no one ever wants that. To solve all these problems, TDM is the best solution.
With DevOps test data and Agile software development, the testing cycles are getting smaller. Developing quality data within that cycle and performing software testing can get complicated. To reduce efforts, time, and cost in the testing cycle- Test data management seems to be an ideal solution, with visible results. This instills a sense of satisfaction and trust in the customer, and better business is the outcome.