Introduction
We use data every day. Basically, we are working and living data-driven outcomes. We are using apps that have been tested for their functionality using data. We are working on GPTs and operating AI tools that are developed using data. Every major industry including healthcare, insurance, tech, banking, and e-commerce is basically operating on the virtue of data-driven insights. We need data more than ever due to its consistent demand in testing apps, software, and models using multiple scenarios.
In the world of data science, data is actually the bread and butter for testers and developers. Whether it’s software testing, updating, or development, testers need flawless data for it. They also require data for the development and training of AI/ML models. Due to the major demand of data, scientists are generating synthetic (artificial data) to feed apps, models, and software. In this article, we will uncover the following topics: What is synthetic data, what are the major benefits of synthetic data, and why synthetic data generation is not always the best choice to use.
Synthetic data
Synthetic data is artificially generated data that data scientists use as a substitute for real data to test software and apps.
Due to the consistent and continuous demand for data, scientists feel the need to develop something that has all the statistical properties of real data but doesn’t have the concerns of use like original data, such as privacy and security concerns regarding the sensitive information of people and businesses.
As we can’t use and find large amounts of real data to create customized scenarios like error conditions and edge cases, we need its replacement, i.e., synthetic test data. To protect the sensitive information associated with real data usage, two types of solutions are available in the market. These are synthetic data and masked data. Among both, synthetic data is the most accurate and has a major success rate when it comes to applicability and functionality.
Let’s discuss what the major benefits of synthetic data are that make it different from other possible solutions.
The Pros and Cons of Synthetics Test Data
Like every technology, synthetic data has its pros and cons. But it’s important for organizations and businesses to carefully understand its nature and efficacy before using it for purposes like deep learning with synthetic data. Plus, its deep knowledge will also help them make informed decisions. Let’s start with its advantages.
Advantages of Using Synthetic Data
Synthetic data has many advantages. As the database grows, synthetic data becomes more important. It helps us to overcome the complexity of data and privacy concerns.
First, it can replicate the trends in the original data. This allows us to use it without violating privacy rules.
Second, it can be used to simulate new situations. For example, to simulate rare weather conditions or equipment malfunctions. It can also be used for prototype testing purposes.
Third, it can help us combat overfitting. This is when an algorithm performs well during training but does not perform well during testing. Synthetic data can be used to train the algorithm.
Fourth, it can help to balance the data. Synthetic data has many solutions to real-world problems.
Let’s explore a few others in detail.
Enhanced Privacy and Security
Real-world data has some privacy considerations. Like one must protect the sensitive and private information of individuals under data protection laws (GDPR AND HIPAA). Their data should not be used in any experiment without their consent.
Synthetic data removes this concern. Techniques such as anonymization substitute real names, addresses and other identifiers with fictional values, preserving utility while preserving privacy. Think about testing a healthcare app. Synthetic data allows you to create realistic patient profiles without exposing real individuals’ information.
Cost Efficiency
Real data collection is costly and time-consuming. Surveys require people to take part, medical data needs access to patients’ records, and datasets can be expensive to buy.
By contrast, synthetic data generation is relatively cheap. Take this example: You have just launched a new e-commerce platform. To test it functionally for different scenarios and to manage the platform, you are seeking multiple users. But you have a tight budget. What will you do? In this situation, you can generate synthetic customer profiles that have diverse buying habits.
Improved Test Coverage
In real-world data, you’re dealing with what’s already out there, edge cases that may be missing, and unusual scenarios. With synthetic data, you can tailor it to your testing needs. For example, you can create data sets that include rare events, such as system crashes and extreme weather conditions (for automotive testing), or simulate unusual user behaviors (for software testing). All of this comprehensive testing helps you identify vulnerabilities that might otherwise go undetected with real data alone.
Let’s take an example from e-commerce:
In e-commerce synthetic test data allows QA teams to test the system’s tolerance for errors, which can be hard to do with real-world customer data. This is why it’s important to develop simulations for various scenarios. Like credit card transaction failure or address validation error, to make sure the checkout process works properly.
Scalability and Accessibility:
Another advantage of synthetic data generation is its scalability. If you need to test a system that has millions of users, you can easily create synthetic data to meet those requirements. Real data is costly and has limited availability. On the other hand, synthetic data is available in large datasets without the limitation of size, time, and location. And it’s also cost-effective.
For instance, if you are working on testing a social media platform for a worldwide user base. With synthetic data, you can create diverse profiles and run a comprehensive test with multiple scenarios of languages and demographics.
Disadvantages of Synthetic Test Data Generation
While synthetic data has multiple advantages and exciting possibilities, it also comes with a few considerations.
At times, synthetic test data can be complex and time-consuming, especially when dealing with high volume, high variety, or high-velocity data.
It can also introduce a number of biases and errors into your testing, such as omitting certain aspects of real data (e.g. outliers, anomalies or correlations).
It can also limit your testing scope and effectiveness by not covering all possible scenarios or variations in real data. For example, it may not detect certain issues or defects that are only present in real data.
Lack of Realism:
Synthetic data doesn’t always accurately reflect real-world conditions.
Let’s take an example: You train a car model based on synthetic images. In the real world, it may not respond to certain conditions like glare, rain, and obstacles on the road. And this ‘fidelity gap’ can significantly affect a model’s performance.
Complexity in Creation:
Synthetic data generation is a complex process. It needs data scientists fully equipped with technical expertise because, to generate functional artificial data, he/she must understand the statistical nature of real data. If these distributions are not captured properly, the synthetic data can be misleading, resulting in models that are not reliable.
Potential for Bias:
Bias in underlying algorithms can also affect synthetic data. For example, if a model is trained to approve loans using a synthetic dataset, it may be biased towards certain demographics. This can result in unjustified loan rejections.
Regulatory and Ethical Issues:
Synthetic data raises new regulatory issues. Like wrong and unethical use of this data. There are several synthetic data generation tools available in the market free of any cost. It may raise concerns regarding the ethical and responsible use of synthetic data. Someone can use the data in notorious ways, like creating deep fakes. Therefore, it’s our responsibility to create a check and balance in this emerging technology to ensure its ethical usage
Conclusion:
To sum up, synthetic data is doing a fantastic job. It is fulfilling much greater purposes and is also following the security and privacy regulations. But we shouldn’t be blinded by the benefits of an easy way. We should also keep in mind the possible drawbacks of synthetic data generation and its usage. Before inserting this data into your workflows, it’s crucial to check every major impact it will create.