Synthetic Test Data: What is it, How to Generate and Use cases

Synthetic Test Data

Picture yourself as a software developer working on a new app. You want to test your app before launching it to the public. But you don’t have enough real data to test all the features and scenarios. How can you make sure your app works well and has no bugs? This is where synthetic test data comes in. This data is artificially created to mimic real data. It has other names such as mock data, fake data, dummy data, or example data.

Synthetic data is a helpful tool for software testing and analytical applications. It can help you test your app faster, cheaper, and more reliably. According to a report by Gartner, synthetic data can reduce testing costs by up to 50% and improve testing quality by up to 60%.

In this article, we will talk about what synthetic data is, how to generate it, and what some of its use cases are.

What is the meaning of synthetic test data?

Synthetic data is artificially generated data using algorithms. Using it gives the real-world data vibes.  This data is used for testing software, systems, or applications.

Moreover, synthetic data is important for many industries, such as banking, healthcare, and education. It helps them protect sensitive information, meet legal or ethical requirements, or recreate scenarios that are hard to find in real data.

For example, synthetic data can simulate customer transactions, patient records, or student grades. In short, this data is a useful tool for developers, testers, and analysts. It helps them improve the quality, performance, and security of their products.

What are the benefits of synthetic data?

Here are some benefits of synthetic data:

1. Data Privacy and Security

Synthetic data does not contain any sensitive or personal information. This means you can use it without worrying about data breaches or privacy laws. It protects your data and your customers’ data. It could be beneficial for GDPR, HIPAA, and CCPA compliance.

Also, it reduces the legal and ethical risks of testing. You do not need to get consent from real data owners or follow data protection regulations. Also, you avoid exposing real data to potential errors, bugs, or malicious attacks. It helps you avoid legal fights and reputation damage.

3. Scalability Testing

You can create as much synthetic data as you need to test the performance and capacity of your systems, applications, and databases. You can also simulate different scenarios and conditions with this data. It helps you test your system’s scalability and reliability.

4.  Algorithm Development and Testing

Data scientists and machine learning engineers use synthetic data to develop and test new algorithms and models. Synthetic datasets help to test in a controlled way. The engineers can separate the variables and evaluate the performance and accuracy of algorithms.

5. Data Diversity

Synthetic data is the best option to include a wide range of data situations that real datasets don’t have. You can create data that matches your specifications and requirements. This way, you can test your software for different cases and find more bugs.

6. Data Quality Control

Plus, it can help you control the quality of your data. You can avoid using real or sensitive data. You can also ensure that your data is consistent, accurate, and valid for your testing purposes.

7. Versatility in Testing

This can also make your testing more versatile and flexible. You can generate data on demand, as much as you need, and whenever you need. You can also modify or delete your data easily without affecting your production environment.

8. Educational and Training Environments

Not to mention, synthetic data can be helpful to teach students and trainees how to use software tools and techniques. The best part? It keeps the real data safe from student errors.

What are the types of synthetic data?

Here are the different types of synthetic data with examples:

Valid Test Data

Valid test data is the data that matches the expected input format and values for a system or application. It is used to check if the system works as intended and meets the requirements.

Examples of valid test data can be:

  • A valid email address
  • Dates in a proper format
  • A valid phone number

Invalid or Erroneous Test Data

Invalid or erroneous test data is data that does not match the expected input format and values for a system or application. It is used to check how the system handles errors and exceptions.

Examples of invalid test data can be:

  • An email address that doesn’t have “.com”
  • Dates that do not follow a certain format, like Feb 30th, because February only has 28 or 29 days.
  • A phone number that has alphabets

Huge test data

Huge test data is a lot of data that you need to test your system or application. You use it to see how your system performs under heavy load or stress. It’s all about ensuring that your system doesn’t slow down or crash when handling large datasets.

Examples of huge test data can be:

  • A database with millions of customer records
  • An e-commerce site with thousands of product reviews
  • Hundreds of gigabytes of images or videos on your website

Boundary test data

Boundary test data is data that is at the edge or limit of what your system or application can handle. You use it to check if your system works correctly right after if the input data is more than your system’s capacity.

Examples of boundary test data can be:

  • Testing the longest or shortest possible name
  • Evaluating the highest or lowest possible price
  • Testing the earliest or latest possible date

How to generate synthetic test data?

Here are the five best approaches to generate this data. They will help you get deep learning with synthetic data.

Random Data Generation

Random data generation is a way of creating fake data for testing purposes. For example, you can generate random names, addresses, phone numbers, email addresses, etc.

It is a way to make data items without any rules or patterns. You can use it for simple software testing. It is easy to do and does not need much planning. But it has some drawbacks too. For example, it may not match the real data that you want to test. Also, it may miss some important features or details that you need.

Statistical Methods

Statistical methods are another way of creating synthetic data for testing purposes. You can use statistical methods to analyze your real data and create data that follows the same patterns, distributions, and relationships.

Moreover, it can help you test your models, protect your privacy, and avoid data scarcity. Synthetic data is like a realistic simulation of your data.

Data Masking and Anonymization

Data masking and anonymization is a way to use fake data instead of real data. This is useful when you have sensitive information in your datasets, such as names, addresses, or IDs. You can apply different techniques to change the real data into fake data but keep the same format and structure.

This way, you can protect the privacy of the people in your data while still testing your system or application.

For example, you can replace the actual names of your customers with random names but keep the same length and initials. This technique is very important for ethical and legal reasons, as you do not want to expose or misuse the personal data of your testing participants.

Data Transformation

Data transformation is a way of changing existing data into new synthetic data. You can do this to make more data for machine learning.

For example, you can rotate, scale, or change the color of an image. This way, you can keep the important features of the data but make it look different.

Data transformation helps you build bigger and better datasets for training and testing your machine-learning models.

Generative Models (e.g., GANs and VAEs)

Another way to create synthetic data is to use generative models. These are neural networks that can learn from real data and produce new data. There are two types of generative models: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

GANs have two parts: a generator and a discriminator. The generator tries to make fake data, and the discriminator tries to tell if it is real or not. The generator gets better and better until the discriminator cannot tell the difference.

On the other hand, VAEs are different. They use probability to model the real data distribution. They can generate data that is close to the real data but not exactly the same. VAEs are good for complex tasks like making images or text.

What are the use cases of synthetic test data?

Synthetic data can be used in different industries. Here’s a glimpse of how to apply this data in different industries:

Software Development and Testing

Synthetic data is very useful for software development and testing. It helps you check how your software works in different situations. For example, you can use it in unit testing to test each part of your software separately. This way, you can make sure that everything works well on its own.

You can also use this data in integration testing to test how different parts of your software work together. This way, you can find and fix any problems that might happen when they interact.

Another use case is in regression testing to test how your software behaves when you change something in the code. This way, you can avoid breaking anything that was working before.

Finally, you can use it in performance testing to test how your software performs when it has to deal with a lot of data. This way, you can measure and improve its speed and efficiency.

Data Analytics and Business Intelligence

It can be used in data visualization to create charts and graphs. This will help you look at patterns and trends in your data easily. Also, you can test different ways of presenting your data.

It can help you train your machine-learning models, too. This way, you can improve your model’s accuracy and performance. Plus, you can avoid using sensitive data that may have privacy issues.

The amazing thing is that you can use this data to study your customers and competitors. This will help you understand their needs and preferences. You can also test new products and services before launching them.

Healthcare and Medical Research

Clinical trials need a lot of data to test new drugs or treatments. But real data is hard to get and has privacy issues. Synthetic data can solve this problem. It can create realistic and diverse data sets that mimic real patients. This way, researchers can run more trials and get better results.

Medical imaging is another area where synthetic data can help. Imaging techniques like MRI or CT scans produce huge amounts of data. But they are also costly and time-consuming. Synthetic data can generate images faster and cheaper. It can also create images with different conditions or diseases. This can assist doctors to diagnose and treat patients better.

Not to mention, healthcare training is a use case for synthetic data. Healthcare workers need to learn how to use medical devices or software. But they can’t practice on real data or patients. Synthetic data can create realistic scenarios and data for them. They can use this data to learn and improve their skills. This can make them more confident and competent.

Final words

Synthetic test data is fake data that looks like real data. It is made by computers, not by people. The reason for it being so important? It helps you test and improve the software as well as train your machine-learning models without using real data. Real data can be private, sensitive, or hard to get. Synthetic data has many benefits. For example, it protects data privacy and security, reduces legal and ethical risks, allows scalability testing and data diversity, and more. To generate synthetic data, we can use five common approaches such as random data generation, statistical methods, data masking and anonymization, data transformation, and generative models (e.g., GANs and VAEs). Furthermore, this data can be used in different industries for different purposes. For instance, you can use it for software development and testing, data analytics and business intelligence, healthcare and medical research, and more.

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