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What is synthetic data?

Synthetic data is created by Generative AI models trained on real world data samples. The algorithms first learn the patterns, correlations and statistical properties of the sample data. Once trained, the Generator can create statistically identical, synthetic data. The synthetic data looks and feels the same as the original data the algorithms were trained on. However the big advantage is that the synthetic data does not contain any personal information.
The definition of synthetic data describes how synthetic data is generated. Synthetic data is generated by AI trained on real world data samples. The algorithm first learns the patterns, correlations and statistical properties of the sample data. Once trained, the generator can create statistically identical, synthetic data. The synthetic data looks, feels and means the same as the original sample the algorithm was trained on. The synthetic dataset is a perfect proxy for the original, since it contains the same insights and correlations.
Experiment with synthetic data

Different types of synthetic data and how they compare

Before we dive deeper into the world of synthetic data we need to clarify one misconception. It's important to distinguish between AI-generated synthetic data and mock data. Before the AI revolution, the term synthetic data was used to describe either randomly created or rule-based mock data. Even today, a lot of people mistake AI-generated synthetic data for simple mock data, even though the two data types couldn't be more different. 

AI-generated synthetic data is sample based. In order to generate it, you need a large enough sample dataset for the Generative AI models to learn from. The models use the original data as an input to learn the properties of that data to a very high degree. The resulting synthetic data looks and feels like the original data and contains all the relevant statistical information.

Mock data generators do not require data samples. Instead the user needs to define the rules (or randomness) how the data is created. But since these rules can become very complex very quickly, as a consequence the resulting synthetic data contains no relevant statistical information.

Another important difference is between structured and unstructured synthetic data. An example for unstructured synthetic data are synthetic images and video. Structured synthetic data is tabular data, where data points and their relationships are both important properties. Examples of tabular data include financial transaction records, patient journeys and CRM databases. Most of these types of data describe human behavior in a chronological way and is commonly referred to as behavioral or time-series data.

MOSTLY AI is the pioneer in the space of AI-generated structured synthetic data and we will focus on that for the remainder of this page.

What is synthetic data used for?

Synthetic data can be safely used instead of the original data, for example, as training data for machine learning models and as privacy safe versions of datasets for data sharing.

What is AI powered synthetic data generation and how does it work?

Synthetic data generation is powered by deep generative algorithms. These algorithms use data samples as training data, in order to learn the correlations, statistical properties and data structures. Once trained, the algorithms can generate data, that is statistically and structurally identical to the original training data, however, all of the data points are synthetic.

Synthetic data subjects look real, but they are AI-generated and are completely artificial. When generating synthetic data, it's extremely important to prevent the algorithm from overfitting to the original data. Overfitting means the AI could potentially learn "too well", memorize original data and then accidentally leak original data points during the inference phase.

Not all synthetic data generators perform well and that's why you want to make sure to work with synthetic data generators of highest quality.

Synthetic data is safe and fully anonymous

One of the main benefits of synthetic data generation is that it provides a better way to anonymize data. Instead of manipulating (e.g. masking, randomizing, etc.) an existing dataset, synthetic data is generated from scratch, while keeping the patterns of correlations of the sample data used to train the synthetic data generator. Synthetic data not contain any one-to-one relationships to the original data subjects, eliminating the risk of re-identification.
AI-generated synthetic data is set to revolutionize how we share, use and build datasets. The first synthetic data sets generated by AI were images. Today, synthetic images are an important part of training computer vision algorithms. The next frontier of the synthetic data revolution is taking place in the field of tabular or structured synthetic data. Companies, governments and researchers using traditional data anonymization techniques, have to live with the so-called privacy-utility trade off. The privacy-utility trade off means that the more you anonymize, the less useful the data becomes.

AI-generated synthetic data offers a great alternative where privacy is preserved without data utility loss!

Why are data scientists and data managers interested in synthetic data generation tools?

Synthetic data generation tools can offer simple and effective ways for creating meaningful copies of sensitive and valuable data assets, like patient journeys in healthcare or transaction data in banking. These synthetic customer datasets can be shared and collaborated on safely without the burden of bureaucracy and dangers to privacy.

But there is another area where synthetic data plays an increasingly important role: Explainable AI. Explainability and governance of AI/ML models can greatly benefit from synthetic data, for example by providing data to stress-test models with outliers and diverse datasets.

Synthetic data for AI and machine learning is more flexible than real data

“Synthetic data generation accelerates the analytics development cycle, lessens regulatory concerns and lowers the cost of data acquisition.” – Gartner
Synthetic data is the perfect fuel that AI and machine learning development projects need. Synthetic data is created by generative AI algorithms, which can be instructed to create bigger, smaller, fairer or richer versions of the original data. Due to how the synthesization process takes place, the data can be augmented to fit certain characteristics. In a way, synthetic data is like modelling clay for data scientists and data managers. For example, upsampling minority groups in a dataset can improve the performance of machine learning models. Or human bias embedded in the original data can be removed by introducing fairness contraints to the generation process.

What are use cases for synthetic data?

Good quality synthetic data is an accurate representation of the original data. As a result, it can be used as a drop-in placement for sensitive production data in non-production environments.

Typical use cases include: AI training, analytics, software testing, demoing, and building personalized products.

For example, synthetic data versions of customer databases, patient journeys, medical records or transaction data are used by companies to make data driven decisions while respecting the privacy of their customers. Synthetic data is an industry-agnostic solution, used across various fields from finance and healthcare to insurance and telecommunications.

Real life examples of synthetic data projects

AI-generated synthetic data is used by companies accross the world to accelerate their data innovation projects in a privacy-compliant and agile way.
TelefĂłnica uses synthetic customer data for analytics
Erste Bank used synthetic test data to develop a successful mobile banking app
JPMorgan leverages a synthetic data sandbox to speed up data-intensive POCs with third party vendors
Anthem uses synthetic data to detect fraud and for delivering personalized service to members

How does synthetic data compare to other data anonymization techniques?

Research has shown many times that legacy data anonymization techniques endanger privacy. They often also degrade the utility of the data.

Even worse - a lot of companies are equating pseudonymization with anonymization. But from a legal perspective, pseudonymized data is still personal data. And it needs to be treated and protected as just that.

How good is the quality of synthetic data?

A key question for any synthetic data generator is how accurate its output is. The data synthesis is therefore usually accompanied by an automated quality assurance (QA) process. The QA process checks if the synthetic data can be trusted to faithfully represent the original data. Each created Generator by MOSTLY AI comes with an automated Model Insight Report.
Learn more about how accurate the MOSTLY AI Synthetic data is and how it compares to other tools

The benefits of synthetic data continue to evolve

Data being the lifeblood of modern businesses poses enormous challenges to decision makers. Especially in regulated environments. On the one hand, data use is restricted by privacy, safety or other regulations. On the other hand, data access is critical in driving innovation. Synthetic data helps overcome this dilemma. The benefits of synthetic data are cost reduction, greater speed, agility, more intelligence and cutting-edge privacy. From transforming data access to AI governance, synthetic data generation can deliver high value use cases across organizations.
Download the HBR Report on Synthetic Data

The potential of cross-industry collaboration

Synthetic data enables not only privacy-compliant data sharing within organizations. It enables a new level of cross-company and cross-industry collaboration - with huge economic benefits for everyone involved.

Synthetic data increases efficiency and profitability

The most important and immediately tangible benefit of synthetic data is that it helps speed up business processes and reduce bureaucracy. For data scientists, analysts and many more, it reduces the time to data massively and frees up their time to focus on value creation. It leads to leaner processes, higher employee loyalty and increased competitiveness.

Synthetic data is highly flexible

Felxibility is a synthetic data benefit agile teams can't work without. You can create and share synthetic data at will. It is as good as production data but much more flexible. You can modify the data, e.g. to correct for bias. You can downsize large datasets or create more data. The end goal is to increase data consumption across all of your teams in full compliance with the strictest data privacy regulations. 
“By 2030, the majority of the data used for the development of AI and analytics projects will be synthetically generated”
- Gartner
The benefits of synthetic data go way beyond privacy. Synthetic data will have a far-reaching impact not only on a data management and governance level, but also on C-level decision making.

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