Between concept and misunderstanding
Synthetic test data has been a part of the testing community for years. The sometimes inconsistent use of the term can lead to confusion or misunderstandings. Since a definition is usually advisable, we would like to offer some assistance and summarize the most important explanations regarding synthetic (test) data below.
What is the purpose of synthetic data?
In principle, synthetic data (as the name suggests) is artificial data. While the specific purpose may vary, in most cases this data is intended to have some relation to real-world data. Synthetic data thus mimics real-world data. This refers to various data attributes such as structure, content, relationships, patterns, or statistical aspects. It is ensured that the data does not contain any information that requires protection or is sensitive.
In summary, the most important benefits of synthetic data are:
- Suitability:
Data can be made available for a specific purpose. For example, providing adequate test data increases testing efficiency. Or AI systems can be trained with targeted data for a specific purpose, while complying with regulatory requirements. - Data security:
Sensitive data is protected during processing. This protection serves to comply with various legal and regulatory requirements, as well as to protect against cybercrime. The focus is on personal data and sensitive company data.
What forms does synthetic data take?
As already explained, this refers to artificially generated data with a certain connection to reality (in IT systems, we generally speak of production data). This connection to reality can vary considerably, which is why the following characteristics have emerged in the definition:
Fully synthetic data
This data has little to no connection to reality. It is generally not derived from actual production data. For example, fictitious people are created who possess products that are not offered and do not exist in reality.
Semi-synthetic data
This data is mostly derived from reality. It is based on real data or data models. People are created synthetically according to a production pattern, but they possess real products of the company.
Hybrid approaches
The approaches are combined. For example, completely fictitious people are assigned products from a company.
How are synthetic data created?
There are countless tools and methods for creating test data. We group these methods into the following categories:
- Model-based approaches
- Automation framework
- GenAI models
- Data Intelligence Platforms
- Data as Code
- Masking and synthetic data generation
These approaches are often found in combination. The selection of a suitable solution depends heavily on the requirements of a project or company.
Advantages and potential
The ability to create secure synthetic test data not only increases data security but also opens up numerous other potentials. The most important are:
- Create suitable test data as needed and adapt it to requirements if necessary
- Generation of data that does not (yet) exist in reality
- Unlimited and scalable data generation for different systems and testing purposes
- Data can be shared with external partners
- Training data for AI systems or external SaaS solutions
We are happy to assist you
Infometis specializes in test data management, synthetic data, and software testing. We support our clients from overarching data governance to the implementation and deployment of suitable test data solutions.
In addition to our methodological expertise, we conduct ongoing market assessments of leading solution providers and match their capabilities to typical customer requirements.
