test data management best practices

Test Data Management: best practices and common mistakes

Test Data Management best practices have become one of the key processes to ensure successful software developments. In the midst of an ever-growing quantity of data, Test Data Management best practices provide guidance in key decisions and actions such as how to discern the quantity of data that is required and ensure privacy norms are complied with.

The result of Test Data Management best practices is more efficient Quality Assurance processes and a framework to generate reliable and planned testing operations.

What is Test Data Management

Test Data Management (TDM) involves all processes and operations aimed at creating and managing data for software testing. As such, it involves actions related to the provisioning, generation, masking, anonymization, and maintenance of test data sets. 

The goal of TDM is to mimic real-world scenarios and conditions in the analysis of data, generating high-quality sets in order to fulfill the needs of testing teams. Thus, these data sets present the right quantity and format and can be included as part of testing processes.

Among the benefits of Test Data Management is the fact that it facilitates a smooth production testing further down the line, allowing developers and testers to be confident in results during production. 

This way, software applications are validated and tested efficiently, through real-world data scenarios, while also maintaining data privacy and security.

Some operations typically involved in Test Data Management include the provisioning of test data sets, the generation of synthetic data, data masking and anonymization efforts, as well as data maintenance, security and compliance.

Why is Test Data Management important?

  • Makes testing more efficient and productive, as quality data is delivered in a timely manner. This is the reason why TDM is involved in improving Quality Assurance processes.
  • Enables more reliable and accurate test results. Because it guarantees realistic and representative data, it thus facilitates the generation of high-quality software
  • Ensures data privacy via anonymization of sensitive data or personally identifiable information (PII)
  • Minimizes potential dependencies on production data, allowing testing teams to work in isolated and controlled environments
  • Serves data integrity and consistency, ultimately having an impact on the quality of testing
  • Is a step forward in terms of comprehensive testing, so that diverse scenarios and variations are covered
  • Enables data consistency, thus allowing for the reproducibility of tests through different cycles, environments and teams

How are Test Data Management best practices performed: the key steps

  1. Requirements analysis: teams must first understand the testing objectives, scenarios, and data requirements that will be involved in the project. This is also the time to identify the types of data that will be needed
  2. Test data sourcing: next comes the identification of the sources of test data, which typically include production databases, data warehouses, third-party systems, or data generators. 
  3. Provisioning: this refers to the process of extracting the required data from the source systems and transferring it to the testing environment. Some actions typically involved in this step include exporting data, creating data snapshots, or setting up data integration. When real data is not available or sufficient, synthetic data is generated.
  4. Data masking or anonymization: these operations protect privacy by modifying or eliminating sensitive or personally identifiable information (PII) from data sets, all while maintaining data integrity.
  5. Subset creation: data subsets that represent specific test scenarios or conditions can be created when the full data sets are not needed in order to optimize storage and processing resources.
  6. Data refreshing and maintenance: this step refers to the need to periodically update test data to ensure its relevance and accuracy. 
  7. Test environment setup: before performing any tests, Test Data Management best practices indicate the right operations should take place  to incorporate the test data within the testing environment.

Through these steps, Test Data Management best practices can help provide answers to questions like “how much data will the process require?”, “what types of data are needed?”, “when and where will the data be needed?”, “what types of tests will be performed?” or “what are the potential dependencies that should be solved?”. All across these questions, the issue of who will be responsible for applying Test Data Management best practices should also be considered. 

Common mistakes when performing Test Data Management

  • Using data from a multitude of sources in an unplanned, unorganized way
  • Generating dependencies on data source stakeholders, thus extending the time needed for Test Data Management
  • Ignoring data masking of sensitive data, leading to potential legal issues and sanctions as well as unsafe data testing operations
  • Generating centralized approaches, which turn into generating dependencies in DevOps and Agile teams that cannot perform at their highest potential, slowing down all processes
  • Ignoring good data profiling at the very beginning of processes, so that cleaning and updating operations can be performed seamlessly.

Test Data Management best practices

The following Test Data Management best practices can help overcome some of the most common challenges encountered by software development and testing teams. 

These challenges often include having access to unsuitable data with limited coverage, the need for large volumes of data in a short time, data corruption issues or dependencies, among many others. To counter these potential issues, here’s a list of Test Data Management best practices: 

1. Use the data you need at each stage of testing

A well-designed testing process with Test Data Management best practices at its center understands it as an iterative process. From the perspective of data, this means that not all data will be required during all testing operations.

As such, when designing the testing process and the different needed test cases, determine the quantity and type of data that will be required. By defining your test data requirements, you’ll establish the type, volume, formand and quality of the data you need for testing purposes. Through that, you ensure, firstly, that you have access to the right data; secondly, it allows you to only add more testing data as iterations take place, through using small data sets, synthetic data or performing data subsetting operations.

This analysis of data needs greatly reduces the complexity of testing, as well as the related costs. The result is also a safer data testing environment, which is also easier to share.

2. Choose the right method to generate or procure test data

As we’ve mentioned above, there are different ways in which test data can be obtained: from extraction, to using third-party providers to creating synthetic data. When considering the right method for your project, you’ll face questions around the quality and complexity of the data you need, as well as its availability and the legal requisites surrounding it. Cost and time should also be considered in the equation to determine the right data sourcing.

3. Ensure data accessibility, integrity and security all across the testing operations

Test data must be stored and managed in a way that it’s protected and secured at all times. Some actions in this respect include backing up data, deleting or archiving test data when it’s no longer needed, or organizing test data according to each case. This will help you avoid data losses or leakages, thus ensuring you do your best to generate a reliable and secure testing process that is safe against unauthorized access, use or disclosure.

Among these Test Data Management best practices are all operations to comply with relevant laws, such as the General Data Protection Regulation (GDPR) in Europe.

4. Set up frameworks to monitor, review and improve test data

The quality of test data must be monitored and measured throughout its lifecycle, ensuring it remains accurate, consistent and relevant. This involves actions such as data validation and cleansing, all by following appropriate quality and secure standards. Finally, Test Data Management best practices also involve improving processes regularly, attending to the received feedback and the results of data monitoring and reviewing.

All in all, the benefits of applying Test Data Management best practices stand out as a way to guarantee Quality Assurance operations have access to the right data.

By employing tools like icaria TDM, Test Data Management is made easy: this platform allows teams to have access to test data on demand, all while protecting sensitive information and complying with GDPR. 

As such, at icaria Technology we’ve worked to help companies and organizations have access to a tool for better test coverage that anticipates fault detection, while also being able to produce synthetic data, generate subsets and all other Test Data Management best practices.

Get in touch with our team and request a demo to find out how icaria TDM can help accelerate and secure your testing procedures.