Synthetic data is like a virtual twin of real-world information, generated artificially instead of from actual events. It’s created using clever algorithms and serves two primary purposes: validating mathematical models and training machine learning models. You can think of data generated by computer simulations, such as music synthesisers or flight simulators, as synthetic data examples.
Now, synthetic data plays a crucial role in maximising the responsible and fair use of sensitive information. We’re swimming in an ocean of data about individuals, with details about their unique characteristics, preferences, and behaviours becoming increasingly abundant. As society becomes more data-driven, we’re empowered to gain valuable insights and tackle pressing issues like the climate crisis and the COVID-19 pandemic. Data availability and utilisation are driving research and innovation to new frontiers.
Of course, with essential data comes great responsibility. When dealing with personal or sensitive data, there are inevitable hurdles to overcome, whether legal, technical, ethical, or practical. That’s where synthetic data comes in handy. It acts as a filter, protecting the confidentiality of certain aspects of the data that should remain private. In many cases, some datasets exist but cannot be released to the general public due to privacy concerns. Synthetic data allows us to sidestep those issues by generating data that mimics the real thing without using actual consumer information without permission or compensation.
One of the cool things about synthetic data is its ability to meet specific needs or simulate certain conditions that may not be present in the original data. This is particularly useful when designing systems, as synthetic data provides a simulated or theoretical value, situation, etc. It allows us to account for unexpected results and have a preliminary solution or remedy if things don’t go as planned. Synthetic data often represents authentic data, giving us a baseline.
Moreover, synthetic data can help us overcome the challenges that real-world data sometimes present. Actual data may be scarce, biased, imbalanced, noisy, or incomplete. Synthetic data can address these issues by augmenting existing datasets and increasing their diversity and representativeness. It can also generate novel scenarios and edge cases that real-world data may miss. Additionally, synthetic data can be instrumental in testing and validating hypotheses and assumptions before applying them to real-world data.
However, it’s important to note that synthetic data is only a magical solution for some data-related problems. There are limitations and risks to consider. For instance, synthetic data may not accurately capture the complexity and variability of real-world data, potentially leading to overfitting or underfitting models. It might introduce biases or errors that don’t exist in real-world data, resulting in inaccurate or misleading outcomes. Moreover, ethical and legal standards that apply to real-world data may not always apply to synthetic data, raising concerns about potential harm or liability. Lastly, not everyone may trust or accept synthetic data, as some stakeholders or users may prefer using real-world data, which could hinder adoption or impact.
Therefore, using synthetic data with caution and rigour is crucial, following best practices and guidelines for its generation, evaluation, and application. Synthetic data should be clearly labelled and documented to avoid confusion or deception.
In conclusion, synthetic data is a powerful tool that allows us to utilise data without compromising privacy. It unlocks new possibilities and opportunities for research and innovation across various domains and sectors. However, we must approach it with care and transparency, acknowledging the challenges and responsibilities that come with its use.
Here are some example of Synthetic Data
Synthetic text is artificially-generated text that can be used for various purposes, such as natural language processing, chatbots, text summarization, sentiment analysis, etc. Synthetic text can be generated using different methods, such as rule-based systems, statistical models or neural networks1.
For example, Amazon is using synthetic data to train Alexa’s language system. By generating synthetic utterances based on real user queries, Amazon can improve Alexa’s understanding and response capabilities1.
Synthetic media refers to video, image or sound that is artificially created or manipulated using algorithms. Synthetic media can be used for computer vision, face recognition, speech synthesis, animation, gaming, etc. Synthetic media can be generated using different methods, such as computer graphics, generative adversarial networks or deepfakes1.
For example, Google’s Waymo uses synthetic data to train its self-driving cars. By generating synthetic scenarios and environments based on real-world data, Waymo can improve its perception and decision-making systems1.
Synthetic tabular data
Synthetic tabular data refers to structured data that is artificially generated to mimic real-world data. Synthetic tabular data can be used for data analysis, machine learning, testing, privacy preservation, etc. Synthetic tabular data can be generated using different methods, such as sampling, perturbation or simulation.
For example, health insurance company Anthem works with Google Cloud to generate synthetic data for healthcare research. By generating synthetic patient records based on real-world data, Anthem can enable researchers to access and analyse data without compromising privacy.