What Is Synthetic Data in AI?

Data is the lifeblood of AI. But what if you have no data? And what if you could create it out of thin air—perfectly tailored, fully compliant, and ready to supercharge your models?

AI models thrive on data, learning from it to make predictions, recognize patterns, and drive decisions. But what happens when the real-world data required to train these models is scarce, sensitive, or restricted by stringent privacy regulations? Enter synthetic data—a solution gaining momentum in the AI community for its ability to fill these gaps and push the boundaries of innovation.

Synthetic data is more than just a buzzword; it's a lifeline for organizations that need to harness the power of AI while navigating the complexities of data privacy, compliance, and ethics. As AI continues to permeate industries, the role of synthetic data becomes increasingly critical, especially in sectors where data sensitivity is a top concern. In fact, Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models.

Understanding synthetic data is essential for any organization looking to leverage AI without compromising on privacy or compliance. But the real game-changer comes when synthetic data is managed through a data catalog, a tool that brings order, governance, and accessibility to the data landscape. This blog post will explore what synthetic data is, why it matters, and how a data catalog can be the key to unlocking its full potential in AI projects.

What is synthetic data?

Definition: Synthetic data is artificially generated data that mimics the characteristics and structure of real-world data. Unlike anonymized or masked data, which starts as real data that has been altered to protect sensitive information, synthetic data is created from scratch using algorithms and models designed to replicate the statistical properties of the original data. The goal is to produce data that is realistic enough to be used for training AI models, testing systems, or conducting simulations, but without the privacy risks associated with real-world data.

How does synthetic data compare to “real” data?

While real data is invaluable for training AI models, it comes with its own set of challenges. Real data can be messy, incomplete, and biased, requiring extensive cleaning and preprocessing before it can be used effectively. Additionally, real data often contains sensitive information that must be protected, especially in regulated industries like healthcare, finance, and government. Synthetic data, on the other hand, is generated to be clean, complete, and free from the privacy concerns that plague real data. It can be tailored to specific use cases, ensuring that it is relevant and useful for the task at hand.

However, synthetic data is not a perfect substitute for real data. While it can replicate many of the patterns found in real data, it may not capture all the nuances and complexities of the real world. This is why synthetic data is often used in conjunction with real data, rather than as a total replacement. As Gartner research VP Alexander Linden says, “When combined with real data, synthetic data creates an enhanced dataset that often can mitigate the weaknesses of the real data.”

What are some examples of synthetic data?

Synthetic data has a wide range of applications across different AI domains. For example, in image recognition, synthetic data can be used to generate thousands of labeled images that AI models can use to learn how to identify objects in real photos. In natural language processing, synthetic data can be used to create text samples that help models understand language patterns and context. In predictive modeling, synthetic data can be used to simulate different scenarios, allowing AI models to learn how to make predictions in a variety of situations. These examples highlight the versatility of synthetic data and its potential to drive innovation in AI.

Why is synthetic data vital for AI?

Overcoming data scarcity

One of the biggest challenges facing AI development is the lack of sufficient training data. For startups and young businesses, this problem is especially acute. Without a large repository of historical data, these organizations struggle to train their AI models to perform accurately and effectively. Synthetic data offers a solution by providing a way to generate the needed data without having to rely on existing datasets. By using synthetic data, these organizations can overcome the hurdle of data scarcity and accelerate their AI initiatives.

Enhancing model training

Beyond addressing data scarcity, synthetic data plays a crucial role in enhancing the training of AI models. Because synthetic data can be generated to meet specific requirements, it allows AI teams to create controlled datasets that focus on particular aspects of a problem. This can lead to more robust and accurate models, as the synthetic data can help the AI system learn from a wider variety of examples than might be available in real-world data alone. It can also lead to models that reduce the impact of bias in the original data, creating balanced synthetic datasets with equal populations of training data rather than forcing data science teams to make trade-offs. What’s more, synthetic data can be used to augment real data, providing additional training examples that help the AI model generalize better to new, unseen data.

Use cases: synthetic data for AI

Synthetic data has proven to be a valuable asset in several AI projects across industries. For instance, in the automotive industry, synthetic data is used to simulate driving conditions for training self-driving car systems. These simulations provide the necessary data for AI models to learn how to navigate roads, recognize obstacles, and make driving decisions, all without the need for extensive real-world testing. In healthcare, synthetic data is used to train AI models for diagnostic tools, enabling them to identify diseases and conditions based on medical images and patient data. In financial services, there’s a growing interest in tabular synthetic data that simulates the properties and behaviors of large-scale structured datasets found in common data platforms and analytical tools. While in software development, synthetic data can dramatically shorten the time needed to test new modules or user functionality by creating accurate quality-assurance records that are closely aligned with real-world application or user data. These examples demonstrate how synthetic data can be leveraged to advance AI technology in various fields.

Why is synthetic data valuable to highly regulated industries?

Data privacy and compliance are paramount in highly regulated industries. Organizations operating in sectors like the public sector face strict regulations governing the use, storage, and sharing of personal and sensitive data. The challenge is to harness the power of AI without violating these regulations or compromising individuals' privacy. Synthetic data offers a compelling solution. Because it is generated from scratch, synthetic data does not contain any real personal information, making it exempt from many of the privacy concerns and compliance requirements associated with real data.

For example, in the healthcare industry, synthetic data can be used to train AI models for diagnosing diseases or predicting patient outcomes without exposing sensitive patient information. In finance, synthetic data can be used to simulate market conditions or test trading algorithms without revealing confidential client data. In the public sector, synthetic data can be used to model public policy outcomes or assess the impact of regulations without compromising citizen privacy. And in the insurance market, IDC has estimated that, due to regulations impacting AI, by 2027, “40% of AI algorithms utilized by insurers throughout the policyholder value chain will utilize synthetic data to guarantee fairness within the system and comply with regulations.”

As AI models become increasingly complex and opaque, synthetic data plays an increasingly important role in the emerging field of Explainable AI. Synthetic data can greatly benefit the explainability and governance of AI/ML models, for example in financial services, by providing data to stress-test models with outliers and diverse datasets in partnership with regulators.

By leveraging synthetic data, organizations in these industries can pursue AI initiatives with greater confidence and compliance.

Data masking and anonymization

While these are common techniques for protecting sensitive data, they have limitations. Masked data is still derived from real data, meaning that it may retain some elements of the original information, posing a risk of re-identification. Anonymized data can also be susceptible to re-identification if it is not properly handled. Synthetic data, on the other hand, is inherently free from these risks because it has no direct connection back to the training data used in its creation. This makes synthetic data a more robust and secure option for organizations that must protect privacy while still using data for AI and machine learning purposes.

Regulatory concerns

In addition to addressing privacy concerns, synthetic data can help organizations meet regulatory requirements related to data use and processing. Many regulations, such as the General Data Protection Regulation (GDPR) in Europe, impose strict controls on the use of personal data, including requirements for obtaining consent, minimizing data collection, and ensuring data accuracy. Synthetic data allows organizations to comply with these regulations by providing an alternative to real data that does not contain personal information. This enables organizations to continue their AI and machine learning projects without running afoul of regulatory requirements.

How does a data catalog support synthetic data for AI?

A data catalog supports synthetic data use cases in a few key ways:

Centralizing synthetic data management

As organizations increasingly turn to synthetic data for their AI projects, the need for effective data management becomes critical. This is where a data catalog comes into play. A data catalog is a centralized repository that organizes and manages data assets, including synthetic data, across the enterprise. By cataloging synthetic data alongside real data, a data catalog ensures that all data is easily accessible, searchable, and usable by AI teams. This centralization streamlines data management and reduces the complexity of handling large volumes of synthetic data.

Ensuring data governance

Data governance is a key concern for any organization dealing with data, and synthetic data is no exception. A data catalog helps enforce governance policies by providing a single source of truth for all data assets, including synthetic data. This ensures that data is used in compliance with organizational policies and regulatory requirements. Furthermore, a data catalog can track the lineage of synthetic data, providing visibility into how it was generated, how it has been used, and who has access to it. This level of transparency is essential for maintaining trust and accountability in AI projects that involve synthetic data.

Enhancing collaboration

AI projects often require collaboration across multiple teams, including data scientists, engineers, analysts, and business stakeholders. A data catalog facilitates this collaboration by making synthetic data easily accessible and understandable to all team members. With a data catalog, AI teams can quickly find the synthetic data they need, understand its relevance and quality, and share it with others for further analysis or model training. This collaborative platform accelerates AI development and ensures that synthetic data is used effectively to drive business outcomes.

Example of how a data catalog supports synthetic data

Consider a financial services company that wants to develop an AI model for detecting fraudulent transactions. Due to privacy regulations, the company cannot use real customer transaction data for training the model. Instead, it generates synthetic data that mimics the patterns of real transactions, including both legitimate and fraudulent ones. The company uses a data catalog to manage this synthetic data, ensuring that it is properly labeled, stored, and governed. The data catalog also allows the company's data scientists to collaborate on the model development, sharing insights and feedback as they work. As a result, the company is able to develop a highly accurate fraud detection model without compromising customer privacy or violating regulations.

What are the key considerations for using synthetic data?

For teams seeking to jumpstart their AI projects with synthetic data, there are a few key things to keep in mind:

Quality control

While synthetic data offers many benefits, it is essential to ensure that the data is of high quality. Poorly generated synthetic data can lead to inaccurate AI models and flawed decision-making. To avoid this, organizations should implement rigorous quality control measures when generating and using synthetic data. This includes validating the data against real-world examples, checking for biases or inconsistencies, and continuously monitoring the performance of AI models trained on synthetic data. Metrics such as accuracy, identical match share, and distance to the closest record are commonly deployed to evaluate how closely the synthetic data mimics the properties and distribution of the original training data. By maintaining high standards for synthetic data quality, organizations can maximize the value of their AI initiatives.

Ethical implications

The use of synthetic data raises important ethical considerations, particularly in sensitive industries. While synthetic data can protect privacy and reduce the risk of bias, it is not immune to ethical challenges. For example, if synthetic data is used to train AI models that make decisions about individuals' lives—such as in healthcare or criminal justice—there is a risk that these models could perpetuate or even exacerbate existing biases. 

Organizations must be mindful of these ethical implications and take steps to ensure that synthetic data is used responsibly. This includes conducting thorough ethical reviews of AI projects, involving diverse perspectives in the development process, and being transparent about the limitations and risks of synthetic data.

Integration with existing data

Synthetic data is most effective when used in conjunction with real data. Integrating synthetic data with existing datasets can enhance the training of AI models by providing additional examples and scenarios that the models can learn from. 

However, this integration must be done carefully to avoid introducing inconsistencies or errors. A data catalog can play a crucial role in this process by helping organizations manage the integration of synthetic and real data. The catalog can provide insights into the relationships between different datasets, track changes and updates, and ensure that data is used consistently across AI projects. By leveraging a data catalog, organizations can achieve a seamless integration of synthetic and real data, leading to more accurate and reliable AI models.

Conclusion

Synthetic data is a powerful tool for AI, offering solutions to data scarcity, privacy concerns, and regulatory challenges. It is particularly valuable in highly regulated industries where the use of real data is often restricted. By generating artificial data that mimics real-world patterns, organizations can train AI models, test systems, and conduct simulations without compromising privacy or compliance. However, the effective use of synthetic data requires careful management and governance, which can be achieved through the use of a data catalog.

For organizations looking to leverage synthetic data in their AI projects, a data catalog is an essential tool. It provides the structure, governance, and collaboration needed to ensure that synthetic data is used effectively and responsibly. By centralizing synthetic data management, enforcing governance policies, and facilitating collaboration, a data catalog can unlock the full potential of synthetic data in AI. Whether you're in healthcare, finance, government, or any other industry, consider how a data catalog can support your synthetic data initiatives and drive your AI success.

Curious to learn how Alation can support your synthetic AI goals? Book a demo with us today to learn more.