As per Gartner’s report, it is predicted that by 2024, 60% of the data used for AI and analytics projects will be artificially generated. This statistic highlights the fact that synthetic data is becoming an increasingly popular trend. This concept has been gaining traction in recent years due to the increasing demand for data-driven decision making in various industries. But what is synthetic data? Don\’t worry, we see that in due time, the piece will also cover its advantages and disadvantages. As well as what techniques are used to generate synthetic data. Keep reading.
What Is Synthetic Data?
Synthetic data refers to artificially generated data that mimics real-world data in terms of statistical properties and patterns. It can be used as a substitute for real data in situations where the latter is scarce, sensitive, or expensive to obtain. One of the main advantages of synthetic data is that it can be generated quickly and at a lower cost compared to collecting and processing real-world data.
A flight simulator game is a perfect example of synthetic data used to replicate real-life situations in a controlled environment.
Synthetic data can be broadly classified into two types: structured and unstructured. Structured synthetic data refers to data that has a predefined schema or format, such as tabular data, while unstructured synthetic data refers to data that does not have a fixed schema, such as text, images, and audio.
Read: Why is Statistics Important?
Applications of Synthetic Data
Synthetic data has a wide range of applications in various industries. In this section, we will discuss the most common applications of synthetic data.
Machine Learning and AI
One of the most significant applications of synthetic data is in the field of machine learning and AI. Synthetic data can be used to train machine learning models, especially when real data is scarce or expensive to obtain. By generating synthetic data, machine learning models can be trained on a larger and more diverse dataset, leading to better performance.
Software Testing and Development
Synthetic data is also widely used in software testing and development. Synthetic data can be used to test software applications, especially when the real data is sensitive or protected. By using synthetic data, software developers can ensure that their applications are working correctly without compromising the privacy of their users.
Data Analysis and Business Intelligence
Synthetic data can also be used in data analysis and business intelligence. Synthetic data can be used to create realistic datasets that can be used for data analysis and modeling. By using synthetic data, businesses can gain an understanding of their customers\’ behavior, preferences, and needs without compromising their privacy.
What Are the Benefits of Synthetic Data?
In the present times, the development of advanced, successful models in Artificial Intelligence (AI) and Machine Learning (ML) requires a massive amount of high-quality data, which is becoming increasingly difficult to obtain. Synthetic data, which is artificially generated, has emerged as a viable solution to this problem and is being utilized in various industries. Synthetic data can be particularly beneficial in the following scenarios:
- It can be used to preserve privacy by generating a synthetic dataset that does not contain any sensitive information. This is especially useful in the healthcare and financial sectors, where data privacy laws are stringent.
- Synthetic data can be used to build complex models that require large amounts of data, which can be either too expensive or too time-consuming to collect. This is particularly relevant in the case of self-driving cars and other computer vision applications.
- Researchers can explore and test new algorithms under controlled conditions with synthetic data. This enables them to conduct experiments in a risk-free environment and save time and resources that would have gone into collecting real-world data.
READ: What is Alpha Significance Level
Challenges of Synthetic Data
The use of synthetic data has been gaining traction in recent years, especially in domains where obtaining real-world data is difficult or expensive. However, generating and using synthetic data comes with its own set of challenges.
One of the primary challenges with synthetic data is ensuring that the underlying model used to generate it is accurate and free of biases. As the saying goes, Garbage in, garbage out”. Any biases present in the original data will carry over into the synthetic data, which can have serious ramifications for any models or analyses built on top of it.
Generating synthetic data is a complex and time-consuming process, requiring highly skilled individuals to build the algorithms that generate the data. This can be a significant barrier to adoption, particularly for organizations that lack the necessary expertise.
Another challenge is that the use of synthetic data is still a relatively new area, and some business users or researchers may be hesitant to trust it. This is especially true in industries where the stakes are high, and errors can have serious consequences.
Finally, if synthetic data is generated using machine learning models, there is a risk of overfitting. Overfitting occurs when a model is trained to fit a particular dataset too closely, resulting in a model that does not generalize well to real-world scenarios. This can undermine the usefulness of synthetic data, making it less effective for certain applications.
Ethical Implications
Synthetic data has the potential to raise ethical concerns, particularly when it comes to privacy and consent. Generating synthetic data that accurately reflects real-world data requires access to large amounts of personal information. This can raise concerns about data privacy and the potential for data breaches. Also, there may be concerns about consent, as individuals whose data is used to generate synthetic data may not be aware that their information is being used in this way.
Legal and Regulatory Compliance
Synthetic data may cause legal and regulatory issues. In some cases, the use of synthetic data may be subject to the same regulations as the use of real-world data. For example, if synthetic data is used to train a machine learning model that is used in a regulated industry, such as healthcare, the use of that model may be subject to the same regulations as the use of a model trained on real-world data.
Techniques for generating synthetic data
- Scikit-learn is a popular machine learning library that provides a range of tools for generating datasets of various sizes and complexities.
- One of the commonly used techniques for generating synthetic data is fitting to a known distribution using the Monte Carlo method.
- Decision tree machine learning models are also widely used for classification and regression tasks.
- Another powerful technique for generating synthetic data is through the use of generative adversarial networks (GANs). Recently, American Express used GANs to generate synthetic data that helped improve their fraud detection models\’ accuracy.
- Another technique that is gaining popularity is domain randomization, where images are altered in various ways to improve neural network models.
Synthetic data generation is becoming increasingly common, and many companies are now offering solutions for generating synthetic data. One notable example is MIT’s Synthetic Data Vault, an open-source software ecosystem that provides a range of tools for generating synthetic data.
Additional Resources
If you’re looking for a thorough understanding of synthetic data and its usage, you might want to check out a few resources.
- One such resource is a paper that provides a comprehensive survey of synthetic data and its applications.
- Additionally, you can refer to The Ultimate Guide to Synthetic Data\” by AI Multiple and
- “An Overview of Synthetic Data” by NVIDIA for a deeper understanding of the topic.
These resources can be highly informative for anyone who wants to learn more about the subject.
