The Ultimate Solution to Testing Woes:

Why Synthetic Data Generation is Critical for Software Application Testing


Jan Kellner, Consultant at Automators

11 min readMay 01 2023


The Ultimate Solution to Testing Woes: Synthetic Data Generation

As technology advances, the use of software systems in organisations is becoming more common. However, as the use of these systems grows, so does the need to test them. Companies have traditionally used real production data for testing purposes, but this strategy can have serious consequences, such as privacy leaks or legal liabilities.

Recently, the use of synthetic data for software testing has appeared as a possible solution to these problems. Synthetic data is artificially generated data that is created to look like real production data but does not contain any sensitive or personal information. It offers a controlled and structured environment for testing software systems without endangering customer privacy.

In this article, we will explore the concept of using synthetic data for testing software systems, emphasising the rising importance of data privacy and the potential consequences of using real data for testing. We will also demonstrate several examples of companies that have faced serious consequences after using real data for testing, and how synthetic data generators can help create anonymized synthetic data from existing databases.

As we have highlighted in our Save Your Business blog posts series, there are several examples of companies that have used real production data for testing purposes and suffered serious consequences as a result. One company tested their support system using actual production data, which led to a data privacy breach that exposed sensitive customer information. Another company tested their inventory management system using actual inventory data, which was incomplete and contained errors, resulting in shipment delays and customer complaints.

Similarly, a bank used actual customer information from their production database to test a new loan application process, which resulted in a data leak that exposed confidential financial information of their clients. Due to a lack of real-world data, an insurance company struggled to test a new policy, which led to delays and increased development costs. Finally, during an internal restructuring process, an organisation responsible for collecting TV and radio licensing fees used real production data that was not properly protected, resulting in a hacker gaining unauthorised access to sensitive information belonging to millions of citizens.


All these examples illustrate the serious dangers associated with using real production data for testing purposes and emphasise how important it is using synthetic data instead to protect confidential information and avoid costly data leaks.

First , production data often contains sensitive and confidential information about real users, their personal details, and transactions, making it risky to use in testing environments. This can result in data leaks, jeopardising individuals’ and organisations’ privacy and security and exposing the company to legal and financial repercussions.

Second , production data is not always representative of all possible scenarios and edge cases that a system will face. This can result in insufficient testing since the system may not be tested under all possible conditions. Furthermore, using production data can be time-consuming because it needs to be cleaned and anonymized before it can be used for testing.

Third , production data may contain outliers, anomalies, or errors that can impair the performance of the application being tested, or it may be messy and inconsistent, making accurate testing results difficult to achieve.

Finally , production data may be inaccessible or unavailable, particularly when dealing with new technologies, industries with strict data privacy regulations, or industries where production data does not exist. This can lead to delays in testing and the release of the application or product, which in turn can result in higher development costs and even missed revenue opportunities.

Now, let us look at the challenges of using production data, the advantages of using synthetic data, and how a synthetic data generator can create anonymized data from existing databases.


Challenges and Benefits of Working with Production Data and Synthetic Data in Testing

Working with data is a crucial part of making sure that a system works correctly and reliably. However, as we have already shown you, the type of data used in testing has a great influence on the testing process’s accuracy, efficiency, and security.

Let us now take a closer look at the main differences between working with production data and working with synthetic data. Understanding these distinctions allows testers and developers to make informed decisions about the type of data to use in their testing process.


Working with production data

Working with production data to create subsets that meet specific testing requirements can be difficult. It requires a deep understanding of the data structure, as well as knowledge of the specific data fields required for testing.

One of the major challenges is making sure that the subset of data chosen for testing accurately represents the overall population. This can be a difficult and time and resource consuming task, especially if the data is skewed or unbalanced. Besides that, selecting too little or too much data can result in misleading results or performance issues during testing.

Moreover, it is critical to make sure that sensitive, confidential data is not unintentionally included in the subset. This requires thorough analysis and masking of sensitive data fields, which is a time-consuming and error-prone process.

One common way for dealing with sensitive data is to use data anonymization techniques such as masking or obfuscation. Even with these techniques, however, it can be difficult to fully protect the sensitive information. Anonymized data can still be reverse-engineered or combined with other data sources to identify individuals or expose confidential information.

Therefore, it is critical to take into account the limitations of data anonymization and consider available alternatives, such as synthetic data. Understanding the key differences between working with production data and working with synthetic data can help testers and developers in making informed decisions about which type of data to use in their testing.

Working with production data to create subsets that match testing requirements is a complex process that demands deep understanding of the data, attention to detail, and expertise in data manipulation. Mistakes can have serious consequences, so this high-risk task should be handled with caution. Therefore, using synthetic or dummy data is a safer and more efficient option.


Working with synthetic data

The use of real production data for software testing can have serious consequences; however, synthetic data is an alternative solution that can help minimise or completely avoid these risks.

Synthetic data is data that is created artificially and replicates the characteristics of real data. It can be customised to meet the specific requirements of a software testing scenario.

One of the major advantages of using synthetic data for testing is higher data privacy. Synthetic data, unlike real production data, does not carry any personally identifiable information or sensitive data. This means that businesses can test their software systems without exposing their clients’ private data. Synthetic data can also be generated in large quantities, making it easier for businesses to properly test their systems under various conditions and scenarios, potentially identifying issues before they become problematic, without requiring real data.

Another significant advantage of synthetic data is that it eliminates the possibility of sensitive data leaks. Businesses must take extra precautions when using real production data for testing purposes to make sure that the data is properly anonymized and secured. Even with these precautions, there is always the chance of data leak. Because synthetic data contains no sensitive data, it removes this risk completely.

Furthermore, synthetic data provides greater flexibility and cost-effectiveness. It is generated quickly and efficiently, eliminating the need for time-consuming data cleaning and anonymization, and it is easily customised to fit the specific requirements of a software testing scenario, which allows businesses to test their systems more thoroughly. Synthetic data is also much less expensive to create than real-world production data, which is expensive and time-consuming to obtain. This makes it an appealing option to businesses of all sizes.

Control over data quality is yet another upside of synthetic data. Synthetic data is created according to predefined rules and can be controlled to make sure that it meets the desired standards. This data quality control ensures that the data is accurate, complete, and consistent, which is critical for testing the system’s reliability.

Besides all that, synthetic data can be generated to also include specific edge cases, which are scenarios that may not occur regularly, but it is still important to test them. Testers can make sure that the system can handle even the most unlikely scenarios by generating synthetic data for edge cases. In some cases, production data may be unavailable or difficult to obtain. Synthetic data can be generated on demand, allowing testing to proceed without delays.

Overall, using synthetic data for testing has several advantages over using real production data. It improves data privacy, eliminates the consequences of data leaks, and boosts flexibility and cost-effectiveness. Synthetic data allows testers to simulate a wide range of scenarios that would be difficult or impossible to simulate with real data. Synthetic data can be created to cover all possible edge cases and conditions, ensuring thorough testing, and lowering the risk of bugs and errors. With the ever-growing importance of data privacy and security, businesses wanting to test their software safely and efficiently are more and more often turning to synthetic data.


How Synthetic Data Generators Create Anonymized Synthetic Data

Synthetic data generators create data that is similar to real data but does not contain any identifiable information. These generators scan existing databases for data patterns, which they then use to generate new data with the same patterns. If no database exists, the generator can be given data types to generate, such as names, emails, credit card numbers, or other information. This data can then be used to test software systems without risking sensitive data.

Synthetic data is created by analysing the structure and patterns of existing data in order to generate new data with the same statistical distribution. This can be done in two ways:

Model-based generators: These generators employ mathematical models and algorithms to generate synthetic data with statistical properties similar to the original data. The models are trained on real-world data to learn the patterns and relationships between the variables, and then they are used to create new, synthetic data.

Rule-based generators: To generate synthetic data, these generators use pre-defined rules and logic. The rules are based on the original data’s characteristics, and the tool generates new data that follows these rules.

These generators’ synthetic data is already anonymized so all personal information or sensitive data that might compromise data privacy is removed. Companies can now test their software systems without fear of violating data privacy laws.

Our Datamaker belongs to the rule-based generators category. You can use this tool to generate synthetic data that looks and behaves like the real thing by selecting from a variety of data types, such as names, surnames, nationalities, emails, addresses, countries, credit cards and many more. Datamaker is best suited for software development and testing because it allows anyone to generate massive amounts of data sets with the click of a button without any knowledge of coding or anonymization techniques.

For example, a travel company needs to test its booking system but does not wish to use real customer data to protect their privacy. The company creates anonymized customer profiles with personal details such as names, email addresses, phone numbers, and payment information using a synthetic data generator. Like this, the company can test the functionality of its booking system and make sure that it can handle large volumes of data without risking the privacy of real customers. The generator can also be used to test the system’s reliability and ensure a smooth customer experience while avoiding regulatory violations.

Overall, synthetic data generators can provide significant benefits to companies that need to test their software systems while keeping sensitive data safe. Companies can ensure data privacy and compliance with data privacy regulations by creating anonymized synthetic data, while also ensuring that their software functions properly and delivers high-quality results.


Final Thoughts

Protecting sensitive data should be a top priority for all businesses, especially when testing software systems. As we have seen in the examples mentioned in this and our previous Save Your Business blog posts, the use of real production data for testing can lead to serious violations of privacy and financial losses.

Synthetic data generators, on the other hand, provide a solution to this problem. Companies can test their software systems without endangering sensitive information by creating anonymized synthetic data. Furthermore, synthetic data generators are simple to use and can be used to generate synthetic data for a variety of data types, including names, dates, emails, cities, countries, or credit card numbers.

Synthetic data generators are an easy, safe, and efficient way to test software systems. As technology advances, it is critical for companies to focus on data privacy and security. By using synthetic data generators, companies can make sure that their testing processes are secure and that their customers’ sensitive information is protected.

And what better way to start working with synthetic data generators than trying out Datamaker here or booking a live demo?

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