Application testing has become increasingly complex in recent years. Evolving markets, changing consumer expectations, expedited releases, better customer experiences, stringent rules and compliances, and increasing competition have compelled companies to adopt the ideals of agility and shift-left testing.
But achieving true agility and shift-left testing without proper test data management can be a challenge. Fragmented production test data spread across many enterprise systems expands the testing cycle, mounts additional costs, creates redundant information, and forces testers to spend more hours debugging, while battling the constant risk of a security breach.
Add to this the need to mask and synthesize datasets without ridding them of their production value, as dictated by the GDPR, and one can see why test data management is integral to any agile strategy.
In this blog post, we will discuss the importance of test data management for agile software development. We will also explore some of the best practices for managing test data in an agile environment.
Why is Test Data Management Important for Agile Software Development?
Test data is essential for any software testing effort. Without proper test data, it is impossible to effectively test software and ensure that it meets its requirements.
In an agile environment, where software is developed and released in short cycles, it is even more important to have access to accurate and up-to-date test data. This is because agile teams need to be able to test new features and functionality as soon as they are developed.
Usually, QA teams face a number of problems while trying to keep up with the rapid software development cycles, including:
- Increased testing time and costs
- Reduced test coverage
- Defective software releases
- Security breaches
Here is how efficient test data management can help QA teams combat these challenges:
- Improved testing efficiency: A well-managed test data environment can help to reduce the time it takes to create and prepare test data. This can free up testers to focus on more important tasks, such as designing and executing tests.
- Reduced testing costs: A well-managed test data environment can help to reduce the costs associated with testing. This is because it can help to reduce the need to purchase and maintain expensive test data sets.
- Mitigated risks: A well-managed test data environment can help to mitigate the risks associated with testing. This is because it can help to ensure that test data is accurate, complete, and consistent.
A powerful test data management system can maximize the agility and effectiveness of your test cycles. You can have your in-house team build a test data management system or choose one of the SaaS solutions for test data management. But before you take the leap toward efficient test data management, it is important to understand the best practices for managing test data in your Agile software development lifecycle. An ideal test data management system complements your Agile processes and boosts their efficiency.
Best Practices for Managing Test Data in an Agile Environment
There are several best practices that can be used to manage test data in an agile environment. These include:
- Centralizing test data: Test data should be centralized in a single repository. This will make it easier to manage and access test data.
- Using data virtualization: Data virtualization can be used to create virtual copies of production data. This can help to reduce the risk of data breaches and security vulnerabilities.
- Masking sensitive data: Sensitive data should be masked before it is used for testing. This will help to protect the privacy of users and customers.
- Versioning test data: Test data should be versioned so that it can be easily rolled back if necessary.
- Automating test data management: Test data management tasks can be automated to save time and effort.
5 Steps to Master Test Data Management in Agile
Step:1 – Building a proper test data discovery pipeline
Most companies today are marred by disjointed databases, spread globally over different systems, that can barely communicate with each other. Each system stores different forms of data and is subject to different rules based on the type of data. Rules and compliances can also change from region to region. For instance, the GDPR in the EU mandates that real user data cannot be used for testing. This can make it significantly harder to identify what data companies already have and how much of it can ideally be used for testing.
Companies should spend time sorting and tagging their data along with maintaining records of ownership. Doing this before testing even begins ensures that data is available to the right people at the right time.
Step:2 – Securing sensitive data with masking
While masking is a complicated process, it is mandated under many legislations, including the GDPR. Any PIIs, employee, client, and user data need to be adequately secured before being pushed into the pipeline.
Many companies opt to hide this data and offer protected access to specific entities. Others mask it and synthesize similar data sets with the same structure and algorithms. Testers only have access to the masked data.
This is a useful practice if done right. Production-grade data remains secure, and testers still get access to quality datasets. It is a process that needs to be prioritized at the beginning of sprints and testing cycles.
Step:3 – Reusing and refreshing data
The quality of data being used is paramount to optimal test results. Poor data standards can affect how the application will function in real life, leading to poor user experience.
But bringing in new data for every test cycle is not an ideal solution. It will only force the company to store a lot of redundant data, wasting storage and exacerbating costs. Experts usually suggest following a multi-cycle strategy wherein after every few production and updating cycles, the data is refreshed. The data can be combed through, and useless data can be weeded out and deleted entirely or relegated to the cloud. New data points can be added to make testing more relevant to the current environment.
Truly agile testing requires a constant pipeline of secured, updated, and relevant data flowing through.
Step:4 – Introducing efficiency in production data derivation
One key fact to remember is that most production datasets also contain unnecessary information that may not be relevant to the ongoing tests. When mimicking those sets, it is advised to reduce and break them off into masked subsets that can then individually be used for different tests. This reduces the volume of data being pushed to the testers and makes data management that much easier.
Efficiently deriving the right data helps accelerate the testing cycle, further enabling agility.
Step:5 – Automating test data management
AI and automation have entirely transformed software testing. Manual, redundant tests can be automated, freeing up testers for cognitive tasks such as building new test cases for new features. But what many companies do not yet know is that test data management can also be automated.
TDM processes such as synthesizing production-like data, masking, tagging, refreshing, and managing the data while maintaining compliance can be automated with the right solution.
Avo’s iTDM, or Intelligent Test Data Management, is an AI/Ml-powered platform that delivers reliable, representative, relevant, and compliant synthetic test data to downstream environments without breaking privacy norms. It helps in reducing TDM costs while expediting time-to-market without compromising on the quality of test data being made available. To learn more about Avo iTDM, contact us today.
