What Is Test Data Management? An In-Depth Guide for DevOps

Test data is one of the most important components of software development. That’s because without accurate test data, it’s not possible to build applications that align with today’s customers’ exact needs and expectations. Test data ensures greater software security, design, and performance.  

Since test data plays an important role in the software development process, it’s critical to have an adequate framework to handle it. After all, mismanaging test data can lead to various issues—like compliance risks and underperforming digital services.

This post will cover test data management, best practices, and the top challenges that all organizations should know about.

What Is Test Data Management?

The short version is that test data management is the process of generating, optimizing, and shipping data for specific tests.

Before we dive into test data management, it’s important to understand how test data works.  

Test data is data that companies use primarily for software testing—or non-production—purposes. Developers use test data to assess how software performs in different settings and environments. Broadly speaking, there are three types of test data: valid data, invalid data, and borderline data.  

In one example, developers may use test data for performance testing. Test data can help determine how fast a system responds to certain workloads and conditions, such as traffic spikes and connectivity lapses.  

As another example, developers might use test data to determine whether a system is secure from malicious intruders. Test data can help ensure confidentiality, authentication, authorization, and integrity. 

What Does Test Data Management Entail?

Before you can use test data, you have to produce it.

There are two components to managing test data: preparation and usage. 

1. Test Data Preparation

Test data preparation involves moving data from production and preparing it for testing environments or creating it from scratch.  

When migrating data into test environments, data must first undergo a comprehensive transformation process to ensure referential integrity, relationships, and quality.  

There are generally three approaches to test data preparation. Developers may choose to clone production databases, create synthetic test data, or subset production databases. 

2. Test Data Usage 

Once data is ready for use, it goes to the developer, who takes the information and deploys it for software testing.  

At this stage, it’s critical to ensure that data is clean, accurate, and secure. Developers shouldn’t have to question whether the data they are using to run tests complies with industry or government regulations or whether it’s subpar.

A Typical Test Data Management Workflow

1. Discover and profile: find where sensitive data such as names, emails, card numbers and government IDs lives, including in free-text fields.
2. Protect: mask sensitive fields with consistent, non-reversible rules, so the same customer looks the same in every connected system.
3. Right-size: subset large databases into smaller, referentially intact data sets, or generate synthetic data where real data is not needed.
4. Provision: deliver the right data to the right environment, team or pipeline, ideally self-service.
5. Refresh and reset: keep data current and return it to a known state after destructive tests.
6. Validate: prove that non-production data is compliant before and after it is shared.

Test Data Management Techniques Compared

Test Data Management in CI/CD

In a DevOps pipeline, test data needs to move as fast as code. Mature teams automate test data the same way they automate builds: a pipeline requests a fresh, compliant data set or database clone, runs its tests, and then resets or removes the data. This removes waiting on manual data requests and makes test results more reliable, because every run starts from a known state.

Compliance needs to be built into that flow. Regulations such as GDPR, HIPAA and PCI DSS apply to personal and payment data wherever it is stored, including test environments, which is why masking or synthetic data should happen before data reaches a pipeline. See HIPAA vs GDPR vs PCI for more detail.

Best Practices for Test Data Management

While companies tend to have different strategies and systems for managing test data, the following best practices apply to any organization. 

1. Prioritize Data Discovery

In most organizations, data tends to live on multiple devices and systems. It also tends to have many different forms.  

As such, it’s critical to have a complete overview of your data. That way, you know where information is coming from before it goes into the preparation or usage stage. What’s more, data discovery can also help ensure adequate data for software testing.

2. Automate Compliance 

Companies today face an ever-expanding list of industry and government regulations. Some of the most common examples include the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), and the California Consumer Privacy Act (CCPA).  

Suffice it to say that it can be very difficult to stay on top of changing rules and regulations. At the same time, it is possible to avoid complications by using automated test data management platforms that streamline regulatory compliance and offer the latest updates and insights. 

3. Use Strong Data Governance 

Testing environments can pose significant security risks due to the vast amount of sensitive data that passes through them. Therefore, it is critical to deploy strong data governance and access control technologies to limit exposure during software testing and prevent unauthorized human and non-human identities from accessing sensitive information. 

For example, companies may use security information and event management (SIEM) tools to monitor and restrict access to data in test environments.

4. Remember to Mask Data

When using sensitive data, it’s critical to mask—or de-identify—the information to protect the owner. Masking data helps ensure accurate and reliable test data while avoiding complaints, fines, and penalties. 

Top Challenges of Test Data Management

Companies often experience a variety of challenges when managing test data. Unfortunately, this can slow down development and lead to various negative outcomes. Therefore, it is necessary to be mindful of the following pitfalls when managing test data.

1. Test Data Shortage 

To be successful at running tests, you need large volumes of accurate data. Frequently, developers start compiling test data only to find they have a shortage of viable information. A common workaround for this is to generate synthetic data. While synthetic data isn’t as accurate as real data, it can still be helpful in certain use cases and allow teams to run basic tests.

2. Managing Data at Scale

In some cases, companies may have too much data on hand. Too much data drives up storage and processing costs and makes it harder to cull databases. You should consider deleting unnecessary test data, including duplications or outdated tests that are no longer useful.

3. Poor Performance Quality 

Just because software passes through testing and goes into production doesn’t mean it will automatically perform up to expected standards. Apps may suffer from various performance issues related to factors like connectivity and device failure. 

For this reason, it’s important to run predictive testing and get a sense of how an application will fare under a variety of different scenarios. Through comprehensive stress testing, it’s possible to plan and mitigate the damage from potential failures before they occur—resulting in stronger and more resilient software.

4. Inefficient Manual Data Creation

Many developers create test data manually and produce data to support specific tests. Manual test data creation can include valid, invalid, and null data. Creating data takes a lot of time and pulls developers away from other projects. It can also result in errors, potentially leading to inaccurate or insecure tests. 

The better approach is usually to automate data creation using powerful data generation tools to produce large volumes of accurate data at scale. This can save time and lower the cost of data generation.

5. Lack of Expertise

There’s a massive developer shortage for companies across all verticals, which is making it harder to bring software to market. Testing tools often require advanced training and specialized skills—especially for complex and sensitive data. Without the right people in place, this is an arduous task that’s hard to pull off.

Test Data Management FAQs

How Enov8 Simplifies Test Data Management

Test data management can go one of two ways. It can empower developers and help create great software or turn into a massive, expensive headache.  

Enov8 delivers a platform that offers advanced visualization and automation across all development life cycle stages, including test data management and delivery. With the help of Enov8, your company can reduce project times, lower expenditures, speed up DevOps workflows, and guarantee security and compliance.

The platform is user-friendly and doesn’t require any advanced training or deployment considerations.