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What is synthetic data?

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
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

What is synthetic data used for?

Synthetic data can be safely used instead of the original data, for example, as training data for building machine learning models and as privacy safe versions of datasets for data sharing. Data scientists and data managers are increasingly expected to work with synthetic versions of customer data and to use synthetic data generators for creatings synthetic data assets.
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, like data masking, where sensitive parts of the data are simply masked or encrypted, 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. 

What is 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, learn the correlations, statistical properties and data structures. Once trained, the algorithm 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. In other words, the AI could potentially learn too well and accidentally generate original data points. That's why quality check on synthetic data are necessary. There are open source synthetic data generators like SDV, which are typically high maintenance and difficult to control for quality. Commercial solutions offer more robust, quality controlled options. 

Different types of synthetic data and how they compare

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 - at least 100 subjects are required by MOSTLY AI's synthetic data generator. Mock data generators don't require data samples and the resulting data is completely fake with no statistical intelligence contained within. While the AI engine of the synthetic data generator can learn and recreate business rules, mock data generators can't. 

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. 
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Synthetic image data is used for training computer vision models in self-driving cars

Why do data scientists and data managers need 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, dangers to privacy and loss of data utility. Building AI and machine learning models, providing explainability and governance to AI/ML models calls for shareable, moldable, discardable, flexibly sized and augmented, synthetic data sets.

Synthetic data for AI and machine learning is better 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 AI and machine learning development projects need. Due to how the synthesization process takes place, the data can be augmented to fit certain characteristics. Synthetic data is created by generative AI algorithms, which can be instructed to create bigger, smaller, fairer or richer versions of the original data. 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. A huge production data set can be subsetted into more manageable sizes for software testing or supersetted for stress testing. Human bias embedded in the original data can be removed by introducing fairness contraints to the generation processThe possibilities of synthetic data are truly endless. 

What is an example of synthetic data?

“Gartner predicts that 20% of all test data for consumer-facing use cases will be synthetically generated by 2025.” – Gartner
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 and development. 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. Price and risk prediction models, customer analytics, explainable AI, developer sandboxes, testing, demoing and building personalized products are the most popular synthetic data use cases with many more coming. 

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. Here are the best synthetic data case studies with real life challenges, solutions and quantifiable results:
Telefónica, a telecommunications company uses synthetic customer data for analytics,
Erste Bank used synthetic test data to develop a successful mobile banking app,
A leading financial institution leverages a synthetic data sandbox to speed up data-intensive POCs with third party vendors,
A large telco reduces employee churn by analyzing global synthetic employee data,
A large bank improves the performance of their fraud and anomaly detection model with upsampled synthetic fraud data

How does synthetic data compare to other data anonymization techniques?

Legacy data anonymization techniques not only endanger privacy, but often also destroy the utility of the data. Synthetic data is the best technology to use when individual data points never need to be linked back to the original.

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. Other anonymization approaches, like generalization, perform better on the privacy front, but fail to preserve data utility.

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

Ready to try synthetic data generation?

The best way to learn about synthetic data is to experiment with synthetic data generation. Try it for free or get in touch with our sales team for a demo.
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