Artificial intelligence and machine learning are used to generate realistic and diverse synthetic data by learning patterns from real-world data. These technologies help ensure high-quality, statistically representative datasets while protecting privacy and improving scalability.
What is synthetic data?
Synthetic data is artificially generated data to imitate real-world data. While it doesn't represent actual people, objects, or events, it mirrors real-world data's structure and statistical properties. Synthetic data is produced using algorithms, statistical models, or machine learning techniques, often through data platforms that streamline the generation process.
Synthetic data's main purpose is to support AI and machine learning projects. It can also be used for testing software or quality assurance when actual data may be limited, unavailable, or too sensitive to use.
In essence, generative AI plays a key role in generating synthetic data. Using advanced algorithms generates complex and realistic data that can help train models and test systems and simulate real-world scenarios without needing to rely on real data.
Synthetic data addresses a variety of challenges in data science, machine learning, and software development. In data science, it simulates rare events to refine analytical models, while in machine learning, it enriches training sets with critical edge cases. The use of synthetic data in software development creates controlled environments for thorough system testing.
Synthetic data addresses privacy concerns—since it doesn't rely on real personal or sensitive data, the risk of privacy breaches is minimised, making it ideal for industries that handle sensitive information.
Synthetic data allows organisations to generate unlimited, high-quality data quickly and at a fraction of the cost of real-world data collection. This especially benefits businesses needing large datasets without extensive resources or time.
Using synthetic data reduces the likelihood of privacy breaches and helps companies avoid legal risks related to data protection regulations like GDPR or CCPA. Since no real personal data is involved, it significantly lowers the chance of costly lawsuits or compliance issues.
The role of generative AI in generating synthetic data
Generative adversarial networks
Generative Adversarial Networks (GANs) are among the most powerful tools for generating synthetic data. GANs consist of two neural networks—a generator and a discriminator—that work in opposition. The generator creates synthetic data, while the discriminator evaluates how close the data is to real data. This adversarial process allows GANs to generate highly realistic datasets, making them particularly useful for applications requiring high-quality, realistic data, such as image or video generation.
Variational autoencoders
Variational Autoencoders (VAEs) are another popular generative model for synthetic data generation. VAEs learn to represent complex data in a lower-dimensional latent space, which they can then sample to generate new data. VAEs are effective for generating synthetic datasets that maintain real-world data's statistical properties and diversity. They also provide smoother transitions between data points, making them useful for generating data in healthcare or finance.
Transformer models
Transformer models, like GPT, excel at producing coherent sentences by learning from vast amounts of existing text data. GPT and other transformer-based models have significantly advanced natural language processing, enabling the creation of realistic, context-aware synthetic text. This makes them ideal for tasks such as generating synthetic customer interactions, chatbot dialogues, or synthetic data for text analysis and machine learning models.
Evaluating the quality of synthetic data
The three main factors of data evaluation are utility, privacy, and fidelity.
- Utility: How well the synthetic data preserves the statistical properties and patterns of the actual data for its intended use.
- Privacy: Ensuring that the synthetic data does not reveal sensitive information from the original dataset, preventing re-identification.
- Fidelity: The extent to which the synthetic data accurately reflects the original data regarding distributions, correlations, and structure.
The evaluation process should start by comparing statistical distributions between the original and synthetic data. Important metrics like means, variances, and correlations should closely match. Additionally, checking for outliers and anomalies is necessary, as these may indicate errors or differences between the datasets. Lastly,
The synthetic data must meet a minimum quality standard, maintaining accuracy and ensuring privacy-compliant data sharing. Furthermore, the synthetic data must align well with existing datasets.
Getting started with synthetic data generation tools
It is essential to choose a tool that meets your specific needs, especially when generating synthetic test data and training data for machine learning models. Some synthetic data generation tools prioritise ease of use, while others focus on handling complex computational tasks. Consider key factors such as accuracy, implementation effort, and processing power to ensure the tool meets your project's requirements, especially in computer simulations and software testing.
A good approach is to start small by generating limited datasets. This allows you to test the tool's capabilities, evaluate data quality, and refine your approach before working with larger datasets. It also helps identify any limitations early on, reducing the risk of errors when scaling up. Once you are comfortable with the process, you can gradually increase the dataset size to match your needs while maintaining efficiency and realism.
Many open-source and commercial synthetic data generation tools offer different techniques, such as rule-based generation, statistical methods, and AI-driven approaches. Some popular synthetic data generator tools for high-quality synthetic data include:
- SDV (Synthetic Data Vault) – an open-source library that enables users to generate synthetic data using statistical models and deep learning techniques.
- Gretel.ai – the synthetic data platform is purpose-built for AI and provides data generator capabilities and privacy-preserving data transformations.
- MOSTLY AI – a tool specialising in generating high-fidelity synthetic datasets for finance, healthcare, and telecommunications industries.
- YData Synth – a Python-based library that offers generative models to create synthetic tabular data while preserving statistical properties.
- Synthea – a tool designed to generate realistic synthetic healthcare data for research and testing.
The future of synthetic data generation
Gartner projects that synthetic data will overshadow real data in AI models by 2030. Synthetic data offers vast potential for innovation in data science as it increases the accuracy of machine learning projects. It is a scalable solution that enables organisations to generate artificial data that mimics raw data.
One of the most significant advantages of synthetic data is its ability to help data scientists tackle problems that are difficult to solve using real-world data. Synthetic data enables the creation of diverse datasets, reducing the limitations posed by limited or biased real-world data.
Final thoughts
Synthetic data has numerous benefits. It can ensure compliance and security while safeguarding sensitive information by adhering to data privacy regulations. As organisations gain a deeper understanding of the data generation process, data teams can leverage these insights to produce high-quality synthetic datasets. As these teams refine their approach, they will be better positioned to unlock new avenues for growth.
Generating large, diverse, and realistic synthetic datasets helps businesses innovate faster, speed up model development, and reduce dependence on raw or production data. Advancements in generative models and AI-driven data generation techniques will also make synthetic data more scalable and adaptable. This will allow the creation of highly tailored test data that can simulate complex and dynamic scenarios. By using these technologies, organisations can improve training data, boost model accuracy, and gain new insights for better decision-making.
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