Synthetic Data | A Comprehensive Guide

Synthetic data is a type of data that is generated by artificial intelligence to closely imitate the design and capabilities of real or original data. It can be used in a variety of business data analytics, cybersecurity, and product development scenarios, but in in any case, synthetic data offers a range of data privacy, security, and accessibility benefits.
In this guide, we’ll dive deeper into the definition and common use cases for synthetic data while also considering the top benefits and possible drawbacks of using synthetic data. We’ll also cover some of the early pioneers and leaders in the synthetic data space to develop a better understanding of the direction in which this enterprise AI use case is heading.
Understanding Synthetic Data
Synthetic data is not real-world data, and in many cases, it is not directly modeled after a specific real-world dataset or observation. Instead, it is AI-generated data that relies on data synthesis, AI data modeling and sampling for simulation, and complex training data to look, behave, and respond like traditional data.
Synthetic data is frequently created through generative AI models like generative adversarial networks (GANs) and variational autoencoders (VAEs), but it can also be created through other data modeling and sampling strategies. These include more conventional statistical models, sampling and interpolation of either spatial or time-series data, or dependency-driven strategies like copula modeling.
The goal is for synthetic data to look and act like real-world data. In many cases, especially with advanced modeling techniques and extensive quality testing, this goal is achieved and it’s difficult to differentiate between synthetic and real data.
However, with more complicated, dynamic, and variegated data pools and environments as well as unexpected data outliers, it becomes more difficult for synthetic data to accurately copy every single variable and shift that develops in real-world data.
Synthesis AI’s Job Builder tool, a subset of its Synthesis Humans product, not only helps users generate the synthetic data to create human avatars but also helps them to reduce bias in their data outcomes. Source: Synthesis AI.
For an authoritative list of synthetic data solutions, read our guide: 9 Best Synthetic Data Software
Fully vs. Partially Synthetic Data
As the names suggest, fully synthetic data is a dataset that consists solely of artificially generated data, while partially synthetic data is a dataset that includes real data with a few synthetic data additions. Partially synthetic data is primarily generated through multiple imputation methods, including mean and regression imputation, as well as a handful of specialized modeling techniques. Partially synthetic data is most similar to hybrid synthetic data, which is a close balance of real-world and synthetic data in a dataset.
Depending on the data that is available to you and what you want to do with it, either fully or partially synthetic data could be the best solution for your organization. Fully synthetic data is best for privacy and regulatory situations that prohibit the use of any real data. It is also good for research and development projects that are innovating in new areas where real data may not yet be available or readily accessible.
In contrast, partially synthetic data works best for datasets that have a few key points that need to be kept private or datasets that are missing essential information and need to be supplemented.
Synthetic Data Use Cases
Synthetic data can be used in healthcare, finance and banking, product and software development, and multiple other areas that require large amounts of high-quality, highly secure data. These are some of the ways synthetic data is being used today:

Healthcare research and analytics: Healthcare research requires analysts to find creative ways to access patient and case data without breaking patient trust or regulatory compliance laws like HIPAA. With fully or partially synthetic data, researchers can mirror actual case data without ever touching or illegally exposing patients’ protected health information (PHI) to generative AI data analytics projects.
Other scenarios that involve private or regulated data: Synthetic data protects consumer privacy data in a variety of other industries, including retail and e-commerce, finance, insurance, and banking. Businesses can more accurately measure current performance and predict future outcomes without looking at the specific demographic data of their customers, which helps to maintain consumer trust.
Synthetic computer vision: Whether it’s generating humanoid AI avatars, realistic road or factory blueprints, or some other kind of computerized environment, synthetic data provides the quantity and quality of data developers need to create complex computer vision products that closely replicate actual environments and their data points.
R&D for innovative products and solutions: Research and development teams often rely on synthetic data to train, test, and improve the performance of their latest innovations. This is particularly effective for technologies like autonomous vehicles, drones, disaster response systems, smart cities, and digital twins, all of which benefit from synthetic data because actual performance data may either be invisible or difficult to collect and access.
ML model development, testing, and validation: Machine learning and other AI models require massive amounts of diverse data for initial training and ongoing testing and validation. In cases where enough real-world data is not available or easily accessible, teams can synthesize artificial data to fill in the gaps and spin up models quickly.
NLP and AI-generated audio: Data synthesis is an important part of voice or audio synthesis. Based on training data — and perhaps a library of actual human voices or relevant sound effects — synthetic data generation tools can generate believable audio for videos, podcasts, and other media.
Cybersecurity: Synthetic data can be used to simulate AI cybersecurity attacks, network environments, and other components of a business’s cybersecurity landscape for cybersecurity training and improvements. Additionally, synthetic data may be generated to stand in for a business’s most private data so it’s less likely to be breached during data analysis and other data-driven tasks.

To learn more about how generative AI is used in the enterprise, read our guide: 15 Generative AI Enterprise Use Cases
Benefits of Using Synthetic Data
Businesses of all kinds are increasingly using synthetic data to protect consumer and organizational privacy, comply with various regulations, and achieve more sophisticated research and analytics results at a quicker pace and larger scale. These are just a handful of the benefits that may come from using synthetic data in your organizational workflows and projects.
Enhanced Data Privacy and Compliance
In many industries, strict regulations are in place for how customer demographic data like health conditions, dates, and names can be used. If companies choose to use this data, they run the risk of noncompliance fines or even jail time, but if they avoid this data completely, they may not be able to achieve the in-depth analytics they need for future growth.
Synthetic data helps in this area, allowing regulated industries to use anonymized data that is similar to actual personally identifiable information (PII) for their data-driven projects. This is also useful for organizations that want to keep their most sensitive business data from full-company access but still want to derive useful insights from that information.
Supplements for Existing Datasets
Data scarcity is a huge issue for many projects. Relevant data may be difficult to find or collect, it may be prohibitively expensive, or it may be covered in so much regulatory red tape that it’s not worth using.
In many cases, datasets are incomplete, and users don’t have the resources necessary to find the missing pieces. Synthetic data generation tools solve this problem effectively, using their algorithmic and statistical training to fill in the gaps quickly and affordably.
Accessible Test Data 
Whether it’s for an existing product or a new development, synthetic data is often used by organizations that need secure, compliant, and easy-to-use test data at their fingertips. Synthetic data is particularly effective for R&D use cases, especially for the development of new technologies. Researchers can generate synthetic data that meets their exact requirements, even when they are trying to research or develop products based on complex or near-invisible data.
Possible Cost Savings
Because you’re not paying for third-party access to real data sources and are instead generating the exact data you need through self-service, synthetic data often saves organizations both time and money in the data collection process. However, if you’re not intentional with your processes and the tools and partners you choose, synthetic data generation can still become expensive over time.
Highly Scalable Data Creation
Synthetic data generation tools are equipped to synthesize data on a massive scale. Not only can these tools generate data quickly and with minimal human intervention, but they also frequently provide the data labels, annotations, and other organizational elements that make data most useful for tasks like data modeling and model training.
Synthetically generated data, then, is great for the scale and diversity of data required for machine learning model development and fine-tuning.
To learn about the larger landscape of leading AI software, read our guide: Best Artificial Intelligence Software 2024
Drawbacks of Using Synthetic Data
While synthetic data can make many projects easier, faster, and more manageable, it can also lead to inaccuracies, biases, and other issues if you’re not careful and aware of synthetic data’s shortcomings.
Here are some of the most important drawbacks to keep in mind when using synthetic data:
Limited Transparency
The algorithms and training data that go into building data synthesis tools are often not all that transparent, especially because there is currently little regulation that enforces standards of transparency for AI. This can make it difficult to evaluate or validate data outcomes. And if your synthetically generated data ends up being inaccurate without your knowledge, you may unknowingly draw inaccurate or even dangerous conclusions about your products and services.
Difficulty in Capturing Real-World Data Complexities
Real-world data is difficult to mimic exactly, especially because its environment, the data itself, and any other number of factors can change at a moment’s notice, leaving your synthetic data outdated and inaccurate. The AI and statistical models that generate synthetic data do not necessarily have a contextual understanding of how the real data fits into the world, meaning the conclusions drawn when creating synthetic data may not work for all business use cases, especially as data changes over time.
Potential for Bias in Training Data and Algorithms
As is the case with any other AI-based innovation, synthetic data is only as good as the training data and algorithms that go into its creation. If the training methodologies include any sort of inherent biases or wrongful assumptions, you may end up with inaccurate or even offensive synthetic data. This could result in a damaged reputation, lost customers, or possible legal issues, depending on the severity of biased outcomes, like deepfakes.
Possible Overfitting
Depending on how a synthetic data generation model is trained, it can begin overfitting synthetic data to the training data it utilizes. In other words, the model may be so good at reading and following its training data that it also starts to account for any noise in the training data while failing to consider any new variables or data scenarios that may arise when it’s time to generate new data.
Overfitting makes it so synthetic data looks but does not act as effectively as real-world data, especially in complex and more unusual scenarios that aren’t “by the book.”
Top Synthetic Data Companies
Various startups and established companies are making their way into synthetic data products and services. The following are some of the top synthetic data companies across both generic and industry-specific synthetic data requirements:

MOSTLY AI: This company offers a synthetic data generation platform that supports data anonymization and other privacy and security efforts. It primarily partners with organizations in banking, insurance, telecommunications, and healthcare.

Syntho: Syntho’s synthetic data generation platform is called Syntho Engine. It is designed to work with a variety of data types and integrates with several third-party cloud platforms and other tools. It is most commonly used in healthcare, finance, and public organizations.

GenRocket: This company focuses on synthetic data generation for test data scenarios, including test data automation and CI/CD workflows. It is not only used in the healthcare, insurance, and financial service industries but also for any kind of company that wants useful test data for AI/ML training, ETL, and/or digital transformation projects.

Hazy: Hazy is an enterprise-focused data synthesis company that generates new data and optimizes existing data for digital infrastructure, business intelligence, and AI advancements and improvements. The company primarily works with organizations in financial services, telecommunications, government, and research capacities.

Synthesis AI: This data synthesis company focuses on generating data for computer vision tasks and initiatives. Its products focus on generating realistic human avatars, workplace scenarios, data for driver and pedestrian safety, and more.

Bottom Line: Using Synthetic Data
Synthetic data works well for a variety of business projects and use cases, particularly in sectors where data privacy and regulatory compliance are a must. It is anonymized, easy to generate and access, and most importantly, it is designed in such a way that it is affordable, scalable, and performs effectively in most data-driven workflows.
But while this type of data can be incredibly useful, it’s only beneficial if your organization goes in knowing the potential risks, biases, and shortcomings that come with using artificially generated data. In addition to the traditional work your team does to clean, prepare, and model data for machine learning training and similar projects, it’s important to closely assess any training data or processes that go into synthetic data generation. This is because it’s essential to know how accurately synthetically generated data mimics the real-world data you would traditionally use. For the best possible results, work with a leading synthetic data company that you trust to be transparent and aware of your particular data requirements.
For a complete understanding of today’s providers of synthetic data solutions, read our guide: 9 Best Synthetic Data Software
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