How Synthetic Data Enhances Enterprise Data Strategy

According to Gartner®, “through 2030, for data used to train artificial intelligence (AI) models, synthetic tabular data will grow at least three times as fast as real structured data.” 1
Synthetic data has been expanding rapidly in the last few years. To use data to benefit your business, products or research, you need to consider leveraging synthetic data for its many advantages while also avoiding potential risks.
How Synthetic Data Enhances Enterprise Data Strategy
In this blog post, we examine the adoption of synthetic data across industries, its benefits, and provide an overview of the major use cases and recommendations for data, privacy and business leaders.

Here's what you'll learn:

  • What synthetic data is, a high-level overview;
  • Synthetic data's privacy-enhancing benefits;
  • The major use cases for synthetic data according to Gartner analysts;
  • What to consider when starting a synthetic data project.
Get complimentary access to Gartner research “Emerging Tech: Top Use Cases for Tabular Synthetic Data.”

What is synthetic data?

The term synthetic data refers to data that has been generated artificially. In synthetic tabular data generation, the goal is to use real world data to train and create synthetic data with similar statistical properties. This newly created synthetic dataset is comparable to the original dataset in terms of quality and statistical distribution. Thus, synthetic data appears and behaves exactly like the source data, but it does not have any direct relation to actual individuals, places or objects. The more realistic synthetic data is, the greater its analytical value.

In most cases, it is created by using machine learning or artificial intelligence algorithms that learn the distributions and relationships within the original dataset.
Synthetic data overcomes many pitfalls, allowing for faster, less expensive, and more scalable access to data that is representative of the underlying source and privacy-preserving.” 2
Gartner
A synthetic dataset can be generated from real world events, financial transactions, customer, medical, insurance data. Synthetic data is used for a variety of academic and business purposes.

As Gartner points out, “In some cases, real data is too expensive or time-consuming to collect at scale, especially for the types of analysis that customers hope to achieve. Additionally, the real data that clients collect may have imbalances (e.g., lacking “edge cases” or demographic diversity) or face regulatory or company-imposed privacy restrictions that prevent its use outside specific contexts.” 3

Does synthetic data enhance privacy?

Synthetic data is gaining popularity as a privacy-enhancing technology (PET). The synthetic data does not have a 1:1 relationship with the original real world data. It is possible to ensure that synthetic data is privacy-preserving and does not leak personal information by providing additional privacy measures, such as training the model using Differential Privacy during the synthesis, or testing the final output against various privacy attacks. If you would like to know more about the technical details of synthetic data generation and how generative adversarial networks are used to create synthetic datasets, read this article.

Of course, privacy preservation is critical for such industries as healthcare, insurance, finance and banking because of the volume of sensitive and personal data involved.

Strict regulations govern the processing of health data in both Europe and the United States, including the EU General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA) and the Health Information Technology for Economic and Clinical Health Act (HITECH).

Switzerland recently updated its Federal Act on Data Protection (FADP), reinforcing privacy obligations and increasing the requirements for documenting processing activities and implementing governance procedures.

GDPR requirements apply to all financial organizations in Europe as of 2018. Financial data is also subject to strict regulations in the United States. Financial institutions are required to comply with federal laws such as the California Consumer Privacy Act (CCPA). Furthermore, these companies are subject to industry-specific standards, for example, the Payment Card Industry Data Security Standard (PCI DSS) or the Gramm-Leach-Bliley Act (GLBA).

In addition, we cannot overlook the Trans-Atlantic Data Privacy Framework, which is set to regulate the flow of data between the EU and the U.S. This framework carries strong obligations for companies handling data transferred from the EU, including the requirement that they self-certify that they adhere to the Principles through the Department of Commerce.
Sharing data across jurisdictions and legal entities, according to vendor Statice, is one use case that it comes across in its work with clients.” 4
Gartner
The use of synthetic data as a PET is promising because privacy-preserving synthetic data does not contain personal information. As long as there is no real world personal information present, personal data regulations do not apply, allowing the synthetic data to be used freely for secondary analysis, shared internally and externally and monetized while remaining compliant and protecting your sensitive data.

The major use cases for synthetic data

For many industries, initiatives focus primarily on internal data. But in others, such as healthcare, the focus is on external collaboration and external data. Both can be enhanced by using synthetic data, and its use cases are numerous, including product development, gaining access to testing data, validating external vendors and partners, and many others.
Use-Case Insights for Tabular Synthetic Data
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Machine learning training and testing

A typical machine learning project faces three major challenges: the lack of real-world data, the quality of the data, and the inability to unlock the data to train machine learning algorithms.

Externally or even within the same organization, sharing sensitive datasets can take a significant amount of time. Unlike sensitive datasets, properly anonymized synthetic data does not require lengthy access requests, thus improving time to value. Furthermore, using synthetic data can enhance the quality of the data. It is the ability to have control over the output that gives synthetic data its potential to produce a more balanced, clean, and useful dataset for training machine learning models.

If you lack adequate data, it is too expensive, or there is no consent to use it in a machine learning project, generating synthetic data can fill this gap.
Synthetic data can alleviate this bottleneck — with a relatively small portion of “real” data, many vendors can generate synthetic data that is statistically similar to the underlying data at scale and use automation to save the company’s resources." 5
Gartner

Privacy-compliant internal and external data sharing

To open up real world data or obtain secondary consent to use it for Machine Learning purposes is not an easy task. Compliance verification processes can take months. Creating synthetic data with the appropriate privacy protection levels can simplify this process. For instance, using anonymized synthetic data for a new machine learning project does not require secondary consent.

Synthetic data generation also allows you to safeguard the privacy of your customers, thereby reducing their exposure to risk. Thus, you are able to conduct experiments using simulated datasets, test different machine learning models, and process the data without privacy concerns.

Additionally, the use of synthetic data opens up new opportunities for cooperation. You may work with a third party to test a Proof of Concept (PoC) before implementing it on a large scale.

Software product testing and development

Furthermore, synthetic data is increasingly used for the testing of software applications due to its ability to generate large amounts of "fake" data that is realistic and can be used in the same manner as real data without posing compliance risks.

Synthetic test data should not be confused with mock or dummy data, which is also widely used in testing. The major difference is that synthetic data preserves the statistical properties of the real data that it is derived from, thus making it more useful for analysis.

Product professionals can generate synthetic data to gain the ability to utilize data freely to create better products, collaborate with external partners, run MVPs, scale or pivot quickly, and use the cloud more effectively.
The ability to access accurate and inexpensive data for model training gives product leaders meaningful return on investment when choosing synthetic data over real-data collection and cleaning while also reducing their costs.” 6
Gartner

What to consider when starting a synthetic data project

As a general rule, business, data, and privacy leaders should become involved in synthetic data projects from the very beginning in order to fully comprehend the scope of the business case, the type of data being used, and how it will be shared.

More often than not, you need more than one data protection method. Depending on the use case, you can choose to combine several techniques. For instance, you can combine synthetic data with Anonos’ Variant Twins, generated by its Data Embassy platform, when there are privacy variables and risks, to ensure full security.

Consider starting small and determining what the key metrics are for evaluating synthetic data privacy. Work with your team to determine all relevant use cases that may arise in the future. Consider, for example, plans for scaling the project, involving third parties, or transferring data internationally.

Synthetic data is an emerging technology with adoption still in its infancy. Therefore, when choosing to work with a synthetic data vendor, invest time in training and developing the capabilities.

Read Gartner latest analysis of synthetic data

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TL;DR

  • According to Gartner, “through 2030, for data used to train artificial intelligence (AI) models, synthetic tabular data will grow at least three times as fast as real structured data.” 7
  • The use of synthetic data as a PET is promising because privacy-preserving synthetic data does not contain personal information and is thus suitable for use in any project requiring large amounts of high quality data. Properly generated synthetic data provides an opportunity to collaborate with third parties and/or international stakeholders in a secure way, or to expedite the time to data.
  • The major use cases for synthetic data are machine learning training and testing, privacy-compliant internal and external data sharing, and software product testing and development.
  • In the machine learning realm, synthetic datasets improve time to data, enhance the quality of the data and provide the ability to have control over the output that gives synthetic data its potential to produce a more balanced, clean, and useful datasets.
  • Creating synthetic data with the appropriate privacy guarantees can simplify compliance processes and open up data sharing, exploration and collaboration opportunities.
  • Synthetic data is increasingly used for the testing of software applications due to its ability to generate large amounts of "fake" data that is realistic and can be used in the same manner as real data without posing compliance risks.
  • However, it would be wrong to assume that synthetic data is exempt from risks such as re-identification attacks or data breaches. More often than not, you need more than one data protection method. Depending on the use case, you can choose to combine several techniques.
  • As a general rule, business, data, and privacy leaders should become involved in data projects from the very beginning in order to fully comprehend the scope of the business case, the type of data being used, and how it will be shared.
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Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

1-7. Source: Gartner, Emerging Tech: Top Use Cases for Tabular Synthetic Data, Benjamin Jury, Alys Woodward, Vibha Chitkara, 22 September 2022