With the advancing field of artificial intelligence (AI) comes greater interest in gathering useful synthetic data. But what does this entail, and what should be done to acquire good-quality synthetic data?
In this introduction guide, we'll look at all the basics you need to know about synthetic data.
What is synthetic data?
Synthetic data is computationally generated information that mimics real-world data properties without duplicating it exactly. It holds immense potential for machine learning, data analysis, and various AI applications, enabling unique innovations.
Synthetic data can be thought of as a substitute for real-world data, providing the means to test systems without compromising sensitivity or security.
(Explore common data types.)
Applications of Synthetic Data
Synthetic data finds utility across several domains and use cases, and here are some of its applications.
Machine Learning
Machine learning relies heavily on data. Synthetic data is an invaluable resource for researchers, developers, and industry professionals. Through the augmentation of existing datasets, synthetic data can help boost machine learning algorithm (MLA) performance.
Highly accurate, synthetic data will help to overcome data scarcity, which often hampers the development of robust and generalizable MLAs.
Synthetic data revolutionizes machine learning by enabling the exploration of scenarios that real-world data may not cover comprehensively. This transformative approach also extends to more use cases in:
- Natural language processing (NLP)
- Image recognition
- Predictive analytics
Healthcare
In the healthcare and hospital setting, the use of synthetic data can also be used for anonymity and data privacy purposes.
For instance, synthetic data can be used for medical research and drug trials, reducing the risk of exposing sensitive patient information.
Synthetic data also enables medical professionals to train on diverse datasets that include a variety of diseases and conditions not readily available in real-world datasets. This improves their skills and knowledge, leading to better healthcare outcomes for patients.
(Related reading: IoMT, the internet of medical things.)
With an understanding of how synthetic data can be useful, let’s look at how it really works.
Synthetic data types
Various kinds of synthetic data types exist, each possessing distinct characteristics that fit specific applications and fulfill unique requirements.
In general, synthetic data is broken down into two main types:
- Structured
- Unstructured
(Related reading: .)
Structured synthetic data
Structured synthetic data closely follows a predetermined format, with precisely defined fields, values, and relationships. It is often used in scenarios where there is a need for large amounts of consistent and predictable data.
Some examples of structured synthetic data include:
- Numerical data (e.g., financial transaction records)
- Categorical data (e.g., customer demographics)
- Temporal data (e.g., time series data)
It often includes synthetic census data, financial records, or transaction histories, making it invaluable for testing software and models under controlled, reproducible conditions.
Unstructured synthetic data
Unstructured synthetic data does not follow any specific format or structure. Instead, it replicates the randomness and unpredictability of real-world data. This type of synthetic data is typically used in applications like natural language processing and image recognition.
Some examples of unstructured synthetic data include:
- Textual data (e.g., social media posts)
- Geospatial data (e.g., maps)
- Audio data (e.g., speech recordings)
- Visual data (e.g., images and videos)
Unstructured synthetic data encompasses text, images, audio, or video that retain essential qualities of real information. This type of data is critical for training advanced models in artificial intelligence and machine learning, as it provides consistent lifelike data while addressing privacy concerns.
Benefits of Synthetic Data
Synthetic data offers some added benefits for organizations seeking to innovate without compromising security. Here are some of them:
Enhanced privacy
With synthetic data, real user information stays secure. By using data that mimics real datasets, organizations minimize the risks associated with exposing sensitive information.
Essentially, synthetic data acts as a shield, safeguarding personal details while still permitting valuable insights and advancements in various fields.
In industries where data privacy is key — be it healthcare, finance, or public policy — synthetic data offers a much-needed pathway to innovation without breaching confidentiality.
Furthermore, it aligns with stringent privacy regulations like GDPR and CCPA, ensuring that organizations remain compliant. This forward-thinking approach to data privacy demonstrates a commitment to both technological advancement and ethical responsibility.
(Related reading: ethical AI.)
Cost efficiency
One of the most compelling advantages of synthetic data is its cost efficiency. Through the use of synthetic data as a substitute for buying real-world data, organizations can significantly reduce the expenses associated with collecting or acquiring data.
Creating real-world datasets often involves:
- Extensive labor
- High operational costs (OpEx)
- Time-consuming processes that can strain resources
In contrast, synthetic data provides a scalable and economic solution, allowing entities to bypass these financial burdens while still acquiring useful data for analysis.
Moreover, synthetic data usage mitigates the need for expensive anonymization techniques and compliance auditing costs.
How to generate synthetic data
Let's now look at how we can generate synthetic data.
- Firstly, look carefully at your real original datasets, and identify key patterns and statistical properties. This is key in helping to retain the features of your data in your synthetic dataset.
- Then, through advanced techniques such as generative adversarial networks (GANs), variational autoencoders (VAEs), or even simpler probabilistic models, craft synthetic datasets to mirror the characteristics of the original datasets.
- Further refine and validate against original datasets to ensure that the synthetic data retains utility while remaining free from real-world constraints. This balance of authenticity and innovation propels synthetic data into a pivotal role in modern data science.
When creating synthetic data, you'll also have to consider if you require a fully synthetic dataset or a partially synthetic one that augments real data.
Techniques & tools
To help us achieve the level of likeness to real data, we can employ some available tools and technologies out there in the market.
Synthetic text generators using LLMs
Large language models like GPT-4o and Gemini have shown a lot of promise in generating high-quality, coherent text. Companies can choose to fine-tune these models on a specific dataset; to generate synthetic text that resembles real data while being completely artificial.
With OpenAI API, you can modify these models to suit your needs and create a dataset that’s perfect for your use case.
Generative Adversarial Networks (GANs)
Generative Adversarial Networks are deep learning architectures used for generating synthetic data by pitting two neural networks against each other — a generative network versus a discriminative network.
The generative network creates samples that mimic the patterns of real data from the input dataset, while the discriminative network attempts to distinguish between real and synthetic data. This competition between the two networks results in the generative network becoming better at creating realistic synthetic data. Such an approach yields remarkably lifelike synthetic datasets, often indistinguishable from real data to uninformed observers.
GANs have been used for a variety of applications, including:
- Image generation
- Text-to-speech synthesis
- Video game development
They are particularly useful in situations where there is limited real-world data available but a need for large amounts of diverse training data.
(Related reading: gen AI & democratized generative AI.)
Variational Autoenoders (VAEs)
Another approach utilizes Variational Autoenoders. VAEs are artificial neural network architectures that play a significant role in creating representations that maintain statistical integrity while offering flexibility. They encode real data into a compressed form and then decode it back, producing synthetic datasets that closely align with the original.
Additionally, there are specialized tools like Synthpop and DoppelGANger. These open-source platforms empower organizations to generate high-quality synthetic data customized to their specific needs.
As technology evolves, more innovative tools and techniques are expected to enhance the efficiency and accuracy of synthetic data generation.
Best practices for handling synthetic data
Handling synthetic data requires some best practices to ensure its integrity and usability.The following are some guidelines that organizations can follow when working with synthetic data.
Understand the limitations
While synthetic data is becoming increasingly popular, it is important to understand its limitations.
- Synthetic data may not capture all of the complexities and nuances present in real-time data.
- Synthetic data cannot replace real data completely, but it can serve as a valuable supplement for certain use cases.
You’ll have to carefully evaluate the suitability of synthetic data for your specific needs before fully relying on it.
Ensure diversity
When working with artificially generated data, it is crucial to maintain diversity in the generation of synthetic data to accurately reflect different demographics, regions, and behaviors. This can be achieved by incorporating multiple sources of real data into the synthesis process.
Validate against real data
To ensure statistical accuracy, it is recommended that synthetic datasets be validated against real datasets before using them in any applications.
You’ll also have to maintain a robust validation process to certify the accuracy of the synthetic data. This validation must involve comparing the synthetic data against real-world datasets to verify consistency and reliability.
When generating synthetic data, always do a thorough analysis of the original data to identify key features and patterns that must be retained. After the synthetic dataset is generated, do a data validation between the truth dataset and the synthetic one to measure its viability.
Real thoughts on synthetic data
Wrapping up, synthetic data is a powerful tool that can help organizations overcome data challenges and drive innovation in various industries.With the right techniques, tools, and best practices, synthetic data can serve as a game-changing solution for businesses looking to enhance their processes and decision-making capabilities.
As technology continues to advance in the direction of AI models, we can expect synthetic data to play an even more significant role in shaping our future.
