How data subsetting enhances test efficiency: best practices

Data subsetting holds the key to many optimizations in test efficiency: from minimizing storage costs to complying with data regulations. 

The accumulation of excessive volumes of data in non-production environments represents a common practice that is, however, costly in resources and difficult to manage.

In this context, the main premise behind test data subsetting practices is to extract only the most relevant portions of a dataset. An approach that puts the focus on data significance rather than data volume, shifting away from conventional strategies while accessing key benefits. 

Part of broader test data management practices, this approach results in more precise testing procedures and ensures that resources are not misused but rather directed toward crucial data. Moreover, when data is extracted from production environments, this technique aligns with one of the core principles of the GDPR: the principle of data minimization. In this article, we analyze why.

What is data subsetting?

Definition and purpose of data subsetting

Data subsetting refers to the process of selecting a portion of a larger dataset based on specific criteria. 

Subsetting is used across various industries, including healthcare, finance and marketing. In the context of test data management specifically, this splitting of extensive datasets into smaller, more manageable ones represents an alternative to using copies of production data in development, testing and acceptance stages. Instead, relevant portions of data are selected to work with, all while maintaining the quality and representation of data.

The purpose of database subsetting is to optimize testing by improving efficiency, reducing storage costs and ensuring testing focuses on relevant data, while filtering unnecessary data out. We explore these benefits further below in this article.

As such, it emerges as a key measure to overcome some of the challenges within test data management. More specifically, it avoids the need to manage and store large volumes of data while also complying with data privacy laws, with the ultimate goal of ensuring test data provisioning remains an efficient and secure process.

Data subsetting vs. data masking: key differences

Data subsetting and data masking are complementary techniques that serve different but equally important purposes in test data management.

While data subsetting enables the extraction of a representative subset of real data—reducing volume and improving test efficiency—data masking ensures that any sensitive information within that subset is protected. Masking modifies sensitive data so that it remains unidentifiable while retaining its format and usability.

These techniques are often used together, especially when subsets are extracted from production environments. In such cases, applying masking techniques is essential to protect privacy and ensure compliance with regulations like the GDPR.

Therefore, subsetting improves manageability and performance, while masking ensures privacy and legal compliance—together forming a robust approach to secure and efficient test data provisioning.

Benefits of data subsetting for test environments

• Reduces computational loads, speeds up data analysis so that associated costs also decrease.
• Improves data retrieval capacities and test data provisioning.
• Optimizes data storage, bringing down associated costs.
• Promotes more targeted data testing, leading to more precise results and, ultimately, optimized applications. This is because unnecessary data is filtered out, so that data subsets can be created that focus on data that is representative.
• Fosters compliance with data privacy regulations by supporting the data minimization principle outlined in the GDPR—ensuring that only the data strictly necessary for testing purposes is included.
  

How data subsetting improves test efficiency

Reduces database size for faster testing

A smaller database means reduced data processing times and the use of fewer computational resources, both of which promote a quicker pace in testing. 

Improves resource utilisation and cost savings

Reducing non-production datasets is an effective strategy to minimize costs related to storage and computer power during testing. 

Because smaller datasets are quicker to process, test data subsetting also means teams can access quicker and continuous iterations and faster developments, which ultimately lead to faster releases while maximizing resources.

Enhances test coverage with relevant data subsets

The conventional approach to data management understands full copies of production data can provide more comprehensive testing, covering all possible test cases. 

However, data subsetting offers a unique advantage: it allows teams to identify and apply only the specific data relevant to certain tests with precision, while maintaining relationships within the data so it remains representative. This approach enables more efficient and focused end-to-end testing, ensuring that critical business processes are validated without the overhead of unnecessary data.

Best practices for implementing data subsetting

Identifying the right subset criteria

Subsetting practices must be guided by the specific application they will be used for, and subset criteria is at the heart of this. As such, some of the common characteristics that a subset of data should meet include being:

• Representative, as in including the most important patterns or cases necessary to make valid conclusions.
• Relevant, as in ensuring the selected data is aligned with its intended use case.
• Has a balance between enough data for it to be representative while keeping data volume to its minimum for performance and cost-related reasons.
• Complies with data privacy rules such as the GDPR or HIPAA.
• Maintains security, especially when referring to sensitive data.

Maintaining data integrity and referential consistency

This step is key to avoid data subsets leading to wrong conclusions or errors when used. It involves relationships within the dataset and all potential dependencies between different datasets remain when subsetting is executed. 

Automating subsetting for scalable TDM solutions

Manual data subsetting would largely contradict the practice’s aim for efficiency, particularly in large datasets. As such, tools are available to automate the process and ensure the best results. 

Choosing the right data subsetting solution

Key features to look for in a subsetting tool

• Advanced capacities for data identification, even for complex technological environments
• Ability for applying custom filters to data
• Allows for quick adjustments in criteria
• Guarantees data integrity
• Provides tools for complying with data regulations, such as the sensitive data map and data masking techniques
• Is scalable
• Is designed for managing large datasets efficiently
• Can automate processes
• Integrates with the organization’s broader digital ecosystem
• Supports handling multiple technologies simultaneously to accommodate diverse data environments
• Provides a larger perspective on test data management beyond data subsetting. For instance, it can generate synthetic data as an alternative to data subsetting, so that the delivery of test data is enriched

Market comparison of leading data subsetting solutions

Why icaria TDM offers the best approach to data subsetting

icaria TDM is a tool designed to facilitate access to data on demand for testers. As part of this aim, it provides advanced data subsetting capabilities for testing procedures. 

At the heart of icaria TDM’s subsetting process is the provision of coherent data, with referential integrity and functional richness. In order to do so, the platform has been designed to streamline the process through the following key advantages:

• An internal search engine that provides an efficient way to locate relevant data, even in scenarios with complex criteria must be met.
• Operation through comprehensive subsetting plans, in charge of determining what data structures must be transferred, what conditions should be met, the complete transfer entities and the valid trajectories from source to destination. 
• Capacities for creating sensitive data maps and applying data masking for guaranteeing regulatory compliance.
• Able to automatically identify the relationships between entities, even if they aren’t documented or do not exist physically in the database. These relationships can then be applied to the subsetting process via icaria Studio.
• Capable of defining different data delivery strategies, a crucial benefit for projects where constant deliveries are the norm and conflicts can occur within destination environments.
• Facilitates delivery retry processes by offering different policies, which is useful for the delivery of data into databases that present active referential integrity and models with a tendency to circular references.
• Configuration can be specific for data domains, allowing for an incremental management of complexity and reusing of the knowledge.
• Guarantees full integration within existing digital ecosystems thanks to its technology-agnostic execution.
• Supports the simultaneous handling of multiple, diverse technologies within the same environment, including complex enterprise systems like SAP and Salesforce — a challenge that many competitors have yet to fully resolve. This capability enables seamless data subsetting across heterogeneous platforms, ensuring comprehensive test data coverage.

The future of data subsetting in test data management

The immediate effects of data subsetting on reducing operational costs and enabling faster testing cycles have already gathered attention on these practices in the present. 

However, current movements in technological and regulatory fields mean data subsetting is also set to become an increasingly adopted strategy for enhancing test data management in the near future.

Several drivers can be mentioned as propellers for this movement. On the one hand, the rise of AI and Machine Learning will largely depend on accessing high-quality data, but also carefully curated datasets. Both of these conditions point towards data setting as a critical step.

On the other hand, this movement will likely be accompanied by stricter data privacy regulation and requirements, with data subsetting one of the crucial measures to address data privacy.

Additionally, a more intense focus on DevOps and agile development will also likely bring in renewed attention for data subsetting.

As test data management continues gaining traction, the process itself is also expected to continue evolving. Increased efficiency and capacities for quick and precise data subsetting, all while guaranteeing compliance with evolving regulation can be named as the major end goals of any movements within data subsetting tools.
Ultimately, data subsetting is likely to be included as part of larger preoccupations around data in organizations. As data ethics emerges as a crucial element for company and product reputation (as recognized by analysis by  McKinsey), the importance of practices such as test data subsetting goes beyond the technical: it ensures organizations put the right tools into action to preserve privacy and build resilient applications.

In this context, tools like icaria TDM emerge as key allies for organizations seeking to elevate their data strategies. Built with testers’ needs in mind, the platform provides the data that testers and automated tests need, when they need it, all while guaranteeing sensitive information is protected. 

With compliance-focused features and highly automated processes, icaria TDM reduces operational costs without compromising data security or quality. But icaria TDM is not designed solely to meet minimum requirements: it relies on advanced data subsetting and other practices such as synthetic data or anticipated fault detection to ensure better test coverage and, ultimately, improve end result software.

Want to learn more about subsetting processes via icaria TDM and how this tool facilitates testing by providing reliable, on-demand and compliant datasets? 

Learn more about us and get in touch with us to speak to our team about data subsetting and best practices in test data management.