My most recent blog article was about “The ROI of well-organized test data provisioning.” In that post—and the upcoming ones—I’ll dive deeper into all the important aspects of the return on investment (ROI) of test data management. From experience, I know there are significant positive returns—economically, operationally, and in terms of job satisfaction for testers and everyone involved in test data management. 

This time, I want to share my insights on “The Big Five of Test Data.” These are the most important and impactful challenges in test data management. Read on to discover how you can start addressing one of these challenges today and gain better control: 

• Access and availability 
• Data quality and coverage 
• Compliance and privacy regulations 
• Managing data volumes 
• Data dependencies and integration 

If any of these are relevant to you, keep reading. Because solving even one of these major test data challenges can lead to significant savings and help test teams work better, faster, and with less frustration. 

Access and availability

Accessing test data or having it available can often be a considerable challenge. Test data can exist in various sources, such as: files and databases. 

One of the biggest steps forward an organization can take is to gradually democratize access to test data across all sources. By this, I don’t mean voting on critical test data management decisions—although that wouldn’t be entirely outlandish 🤔. What I mean is making test data easily and securely accessible to testers in your test team. 

Fortunately, I’m seeing this happen more often, particularly in organizations that are deeply embedding the DevOps methodology. 

In a DevOps environment, making test data more accessible is crucial. This is rooted in the principle of “bring the pain forward”—the idea that the sooner and easier testers can conduct tests in the development process, the better. This is a complete departure from the traditional waterfall method, where testing often happens “later.” 

In early development stages, testers may not need direct access to data sources—unit tests can often be run with manually generated data. But as software matures toward production, industries like insurance and finance—where software impacts people’s lives significantly—demand greater assurance before going live. System, chain, and integration testing must be done with representative test data, not manually created or generated data. 

How you structure access to data sources directly affects how quickly, efficiently, and effectively test teams can work. 

Data quality and coverage 

Data quality and coverage is a natural challenge to tackle after access and availability. Data quality and coverage is about whether you have the right data to test the right test cases. And to achieve this, testers need access to data sources. 

Many organizations struggle with low-quality data or limited coverage, which significantly impacts the ROI of test data management

Many organizations struggle with low-quality data or limited coverage, which significantly impacts the ROI of test data management. As software teams, we have one clear goal: to develop systems that provide value and function acceptably. This requires high-quality software that customers want to use and that accelerates and simplifies processes. To achieve this, test teams need to test effectively using high-quality, well-covered test data. Without this, bugs arise, users get frustrated, and the software fails to meet its purpose. 

For example, insurers often deal with complex historical data, which can be further complicated by migrations and additional datasets. A critical first step for test teams in such environments is to generate a representative view of test data to ensure software meets quality standards. As development progresses and system, regression, or performance tests are conducted, representative test data becomes increasingly critical. Understanding your data sources plays a key role. With insights into your data, you can better identify the unique test cases essential for your tests. 

Compliance and privacy regulations 

Knowledge is power. For test teams, this means understanding everything about the data stored in your databases, applications and files. 

This knowledge forms the foundation for compliance. Once you know where information is stored and what kind of information it is, you can determine how to handle it to comply with various privacy laws and regulations. So, knowledge is the pathway to compliance.  

Many organizations mention compliance as a top motivation for adopting test data management. Regulations like GDPR in Europa and CCPA in California or HIPAA and ISO certifications emphasize two key compliance factors that affect ROI: 

• Minimizing penalties or the impact of data breaches.

   

• Simplifying test environments for faster development (i.e., improving access and availability). 

A critical first step is gaining insight into: 

• What data do we store? 

• Where is it stored? 

• What are we trying to achieve with it? 

With these insights, you can implement the technical and organizational measures needed to comply with regulations while meeting your goals. Techniques like data anonymization or synthetic test data generation allow organizations to remain compliant while delivering high-quality software to customers. 

Managing data volumes 

One of the biggest challenges for organizations today is managing the ever-growing data volumes. 

Under the motto, “If we can store it, why wouldn’t we?” more and more data is being collected. New technologies enable data-driven decisions, but they also create increasingly complex IT environments that must be managed. 

The last decade has also introduced two additional complexities: SaaS applications and cloud environments. 

For SaaS, organizations may not have direct access to their own data—a surprising reality given how valuable data is. Luckily, many SaaS platforms support test data, though they often introduce additional complexities. Similarly, while cloud environments like Azure or AWS present challenges, they’re manageable as long as the data and databases are accessible. 

In many organizations, full copies of production databases are still used in testing, acceptance, and development environments. While many have adopted agile or DevOps methodologies, their infrastructures often remain stuck in a traditional waterfall approach. Providing full production copies to every team is costly, especially in the cloud, where storage needs are increasing. 

Fortunately, smarter solutions like data subsetting or virtualization exist to alleviate these problems, making test data management more efficient. 

Data dependencies and integration 

The final challenge is one of the most technical: data dependencies and integration. 

In organizations with complex systems, there are often numerous dependencies. For example, consider testing a life insurance policy scenario where a policyholder dies. Multiple processes are triggered: Who receives the payout? Are there children? Was there a prior divorce? Such dependencies create challenges, especially when data must be reverted after destructive testing. 

To tackle data dependencies and integration, organizations must deepen their understanding of data, databases, and sources.

Data subsetting helps maintain relationships within data, ensuring it’s still usable for testing. Ideally, test data should also integrate with CI/CD pipelines or test automation processes, making it more accessible to teams. 

To tackle data dependencies and integration, organizations must deepen their understanding of data, databases, and sources. Collaboration between testers, developers, and database administrators is essential to streamline processes.