Test Data Management Strategy Design: Guide & Best Practices

JUN, 2021

by Justin Reynolds.


Author Justin Reynolds

This post was written by Justin Reynolds. Justin is a freelance writer who enjoys telling stories about how technology, science, and creativity can help workers be more productive. In his spare time, he likes seeing or playing live music, hiking, and traveling.


Before software goes into production, it needs to undergo thorough testing to ensure quality and performance. To accomplish this, DevOps teams typically rely on test data. 

Read on for an overview of test data, including why test data management is imperative for success and some best practices to consider when forming a test data management strategy. 


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*aka ‘Data Compliance Suite’

The Data Securitization and Test Data Management platform. DevSecOps your Test Data & Privacy Risks.

What Is Test Data?

Test data refers to any data that developers use for testing software. DevOps teams use test data to see how applications will perform in live production environments.  

There are different types of tests that DevOps engineers run. For example, DevOps may use test data to conduct security, performance, or regression testing.  

How Is Test Data Different From Other Types of Data?

It’s important to realize that test data is no different than any other type of data. At its core, test data is simply information that helps you create better software.  

With that in mind, test data can vary in different use cases. To illustrate, you may use synthetic data for focused tests. And in cases where you want to closely replicate live environments, it’s typically better to use production data.  

Types of Test Data

Now that you have a better idea of what test data is, here’s a breakdown of some of the common types of test data that software developers use.  

Valid test data

Valid test data refers to clean, acceptable data that a program can process. In other words, when an application supports data, the data is valid. 

Invalid Test Data

Invalid data is another word for an unsupported data format. In other words, this refers to data that a computer cannot accept or process. 

Absent Data

Absent data is a term for blank files that do not contain any data. Developers use absent data to test how an application would perform without data. 

Common Test Data Challenges

From the outset, test data may sound like a simple enough concept. But teams may face a variety of challenges when creating and deploying it. In this section, we’ll examine some common obstacles that can arise using test data.  

Lack of Relevant Data

While companies are collecting more data than ever before, they often lack access to relevant data they can safely use during testing. As such, teams often create synthetic data as a workaround. 

Manually Creating Test Data

While manually creating test data can help when development teams lack access to meaningful data, it can be extremely labor-intensive and expensive. In order to streamline data creation, many teams choose to automate the process entirely. 

Data Validity and Integrity

Data requires ongoing maintenance in order to remain effective. In other words, data can become obsolete and lose context over time. In addition, developers need deep visibility and traceability with data throughout its lifecycle to ensure integrity.  

What Is Automated Test Data Management? 

Test data management is the process of generating, optimizing, and shipping data for software testing. With this in mind, the quality of the data you use for testing is very important.  

Unfortunately, you can’t ensure test data quality when you manage test data by hand. As a result, it’s important to automate test data management whenever you can. 

Let’s take a look at some key reasons why automated test data management is critical for success. 

Boost IT Delivery

In order to compete in the ever-changing software industry, software teams need to move quickly.  

A robust test automation strategy enables you to produce high-quality software faster than manual testing does. As we explain in a recent blog post, manual testing is still a good approach in certain settings. Even so, it’s much more efficient to use automation most of the time. 

Ensure Compliance

The key to effective software testing is to use production-like data, which is highly accurate and impactful. However, this can be challenging from a regulatory compliance perspective. That being the case, using a robust data compliance tool enables you to test safely and accurately. For example, you can mask data and quickly generate compliance reports. 

Improve Quality 

At the end of the day, software quality and production speed are top initiatives for DevOps teams. Automated test data management ensures the highest level of test data quality and availability. And this leads to higher quality software, with fewer bugs and vulnerabilities. 

How to Form an Effective Test Data Management Strategy 

Test data management strategies tend to vary across different companies and development environments. Here are some things to consider when forming a test data management strategy for your organization. 

Form a Preparation Plan

Before you can test data, you need to either create data from scratch or migrate it from production. For the best results, spend some time forming a preparation plan. While you’re at it, outline some best practices that your team should follow when preparing data. 

Check Your Data 

Software engineers need to move quickly. As such, it’s critical to avoid slowdowns during the development process. For this reason, teams need to ensure that data is clean, secure, and accurate before it goes to developers for testing. 

To illustrate, a developer shouldn’t have to worry about any HIPAA violations before testing software. Once a developer receives data, they should be clear to use it in any testing capacity. By checking data ahead of time, you can ensure a fast, safe, and reliable testing environment.  

Track and Maintain Your Test Environments 

It’s easy to lose track of your test environments, especially when creating and managing software at scale. To avoid this, it helps to centralize your test environments in a single repository. The repository should contain a list of your latest test environment versions. 

Deploy Strong Governance 

Working with test data can lead to potential security and privacy risks. For this reason, you should deploy strong data governance across your entire cloud environment. Data governance tools can limit what developers have access to and track data movement and changes.  

Explore Open Source Tools for Test Data Management 

There are a variety of open source tools you can use to enhance your test data management strategy. It’s a good idea to browse the market and integrate open-source tools where it makes sense.  

For example, Databucket is an open-source test data management tool for analyzing test and metadata across different projects and environments. Another great tool to explore is Databene Benerator, which you can use to generate high-volume test data. Additionally, DataFactory also enables you to create test data by providing values for names, email addresses, phone numbers, texts, and dates. 

Automate Across the Entire Development Lifecycle With Enov8

Test data can be a major liability if you aren’t careful. As such, it’s important to operate with deep visualization and automation across all of your development lifecycles. Without full visibility and automation, your team is bound to encounter slowdowns, security issues, and quality challenges, among other things. 

For these reasons, you should consider using a tool like Enov8 to expedite DevOps workflows and reduce development times and costs. Enov8 is a one-stop shop that identifies where data security vulnerabilities exist. It can provide you with the insights that you need to create secure, production-like data at scale.  

Other Reading

Enjoy what you read? Here are a few more articles that you might find interesting.

Enov8 Blog: Types of Test Data you should use for your Software Tests?

Enov8 Blog: Why TDM is so Important!

Enov8 Blog: What is Data Fabrication in TDM?

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