What are the challenges of using synthetic data?

What are the challenges of using synthetic data?

There are many ways to use synthetic data, including for training self-driving cars and other applications where obtaining real-world data is difficult or dangerous. For example, robotics companies can test and engineer their systems using simulations rather than waiting months to collect a large dataset.

Using synthesized data can help to improve model performance and save time. But there are challenges with this method that need to be considered.


When real-world data is unavailable or inappropriate for machine learning models, synthetic data can provide a valuable alternative. It allows companies to simulate real-world conditions without risking live production. It can also help them develop new applications that may not be feasible with live production data.

Another benefit is that synthetic data is scalable and can be tailored to specific needs. For example, a company can generate images and video data to test computer vision systems and train machine learning algorithms for autonomous vehicles. They can also use synthetic text data to create chatbots and machine translation algorithms.

Furthermore, synthetic data can be generated in a privacy-friendly way. This can be especially important when developing AI/ML models for sensitive scenarios, such as banking fraud or medical diagnosis. It can prevent the revelation of personal details that would violate privacy laws. It can also reduce the risk of data leaks and other security threats. It can also save time and money by reducing the need for manual review of real-world data.


The growing use of artificial intelligence (AI) and machine learning across a variety of industries brings with it concerns about privacy. These technologies must learn from vast amounts of information, which may reveal private details and be used to discriminate against people in hiring, lending or housing decisions. This has prompted many companies to turn to synthetic data, which offers the same benefits of real-world data without the risks.

Synthetic data is computer generated and can be of any type, from text for natural language processing applications to tabular synthetic data for machine learning and analytics applications. It can also be a form of media, such as a video, image, or sound for computer vision applications.

One of the challenges of using synthetic data is that it can be difficult to validate its accuracy. This is because it can be hard to tell if the model will behave in a similar way in real-world situations. Another challenge is that the data being used to generate synthetic data may change over time.


Creating real data sets can be expensive and time-consuming. For example, an automaker may spend millions collecting data from real vehicle crashes to train their self-driving cars. Synthetic data is cheaper and quicker to produce. It also eliminates the need to transfer real data from one team to another, allowing developers to work at their normal pace.

Moreover, synthetic data is privacy-preserving, which makes it easier for teams to share and collaborate across departments and geographical boundaries. This allows businesses to bypass challenges such as privacy concerns and cost, enabling them to gain valuable insights for business decision-making.

Real raw data is still preferred for data modeling, but when such data is difficult to collect, synthetic test data is a viable alternative. Synthetic data can be generated in the form of text in natural language processing, tabular data for regression tasks, or media such as video and sound in computer vision applications. It can also be used to evaluate a model’s performance by running it through various evaluation metrics.


For data scientists, working with real-world data can be a time-consuming process. To avoid spending time collecting, cleaning, and organizing data, organizations can generate synthetic data for training their models. This can save time and resources while ensuring the accuracy of their models.

Synthetic data also allows organizations to collaborate with others on a project without worrying about privacy issues. This benefit can be especially valuable for sectors where the availability of authentic data is limited or poses privacy risks, such as health care and finance.

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Creating synthetic data requires the use of advanced algorithms and machine learning techniques. For example, neural networks can be used to transform 2D image data into 3D simulations. Other techniques such as variational autoencoders and generative adversarial networks improve data utility by feeding the model more examples. These advanced technologies are critical to the success of artificial intelligence (AI) models. However, their application can be challenging to implement and scale.