All you need to know about what smarter synthetic data is
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What is synthetic data?
Synthetic data is generated by AI trained on real-world data. The resulting synthetic data looks, feels and means the same as the original. The synthetic dataset is a perfect proxy for the orignal, since it contains the same insights and correlations.
They realized early on the potential of using AI to generate structured business data and to create what we now call synthetic data. Back then this was not much more than an idea. It was unclear how the process was going to work, since no previous research or competitors existed in the space.
The inspiration came from the unstructured data domain where the first artificially created synthetic images were produced. The three co-founders experienced the challenges companies were facing with traditional data anonymization. These challenges only increased as GDPR was introduced in Europe in 2018. MOSTLY AI released the first version of its Synthetic Data Platform at the same time and proved to the world that synthetic data has a vast potential.
Part 1
Synthetic data is better than real data
“Synthetic data generation accelerates the analytics development cycle, lessens regulatory concerns and lowers the cost of data acquisition.” – Lorem name
Thanks to the flexibility of the synthetization process, synthetic data can be tailored to suit use cases and protect data privacy simultaneously. Synthetic data is the must-have ingredient for successful data projects throughout organizations.
Part 2
How was synthetic data invented?
Before generative AI became a reality, the term synthetic data was used for all kinds of fake or mock data, such as: Random data, Rul-based data.
Data generation methods reached a new level with AI-powered deep generative models. They can create an unlimited amount of highly realistic, completely safe synthetic data. MOSTLY AI pioneered data synthesis for structured, tabular data. Today, MOSTLY AI is the expert in generating behavioral and transactional synthetic data.
Read case studyData generation methods reached a new level with AI-powered deep generative models. They can create an unlimited amount of highly realistic, completely safe synthetic data. MOSTLY AI pioneered data synthesis for structured, tabular data. Today, MOSTLY AI is the expert in generating behavioral and transactional synthetic data.
Part 3
What are the use cases for synthetic data?
“Synthetic data generation accelerates the analytics development cycle, lessens regulatory concerns and lowers the cost of data acquisition.” – Lorem name
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, such as AI training, analytics, software testing and development.
Use caseAI training
Synthetic data for AI training is better than real data. The synthetization process can also augment the data. By upsampling rare events and patterns, AI algorithms can learn more effectively.
Read nowAI governance
Synthetic data for fair and explainable AI systems should be an integral part of every machine learning development. The process of synthetization can remove biases embedded in the original data.
Read nowSynthetic test data
As opposed to rule-based test data, synthetic test data is easy to generate. It is highly realistic and flexibly sized. Synthetic test data is a crucial ingredient for data-driven software development and testing
Read nowPart 4
How does synthetic data work?
Not all synthetic data is created equal. Modern day synthetic data generators are sophisticated AI algorithms. Some are better than others. MOSTLY AI's category-leading deep neural network models extract patterns from a provided dataset. Once trained on real data, our synthetic data platform can generate completely new synthetic data. This data mimics the characteristics of the original, to the extent that it is nearly indistinguishable from it. Still, as it bears no direct relationship to the actual data, synthetic data is absolutely safe to use and collaborate on.
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How does synthetic data compare to other data anonymization tools?
Legacy data anonymization technologies not only endanger privacy, but also destroy the utility of the data. Synthetic data is the best technology to use when datapoints don’t need to be linked back to originals. We see a lot of companies using pseudonymization as anonymization. But from a legal perspective, pseudonymised data is still personal data. And it needs to be treated and protected as just that. A pseudonymized dataset still includes so-called direct identifiers. Other tools, like generalization, perform well on the privacy front, but fail to preserve data utility.
Learn moreHow does synthetic data compare to other data anonymization tools?
Legacy data anonymization technologies not only endanger privacy, but also destroy the utility of the data. Synthetic data is the best technology to use when datapoints don’t need to be linked back to originals. We see a lot of companies using pseudonymization as anonymization. But from a legal perspective, pseudonymised data is still personal data. And it needs to be treated and protected as just that. A pseudonymized dataset still includes so-called direct identifiers. Other tools, like generalization, perform well on the privacy front, but fail to preserve data utility.
Learn more
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Why synthetic data?
Synthetic data is highly flexible
You can create, share and discard synthetic data at will. It is as good as production data and capable of improving data quality. You can even modify existing data sets, e.g. to correct for present bias.
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The synthetic data guide
Synthetic data is highly flexible
Synthetic data is the AI-generated version of real data. AI algorithms learn the patterns and dimensions of data. Once they were trained, they can generate infinite amounts of synthetic data that is statistically representative of the original training data.
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Fair synthetic data for ethical Ai
Synthetic data is not just a privacy-enhancing technology. Synthetic data generation is a data augmentation tool. The synthesis process is capable of introducing different constraints, such as fairness. The result is fair synthetic data, devoid of bias. We like to think about fair synthetic data as a reflection of the world as we would like it to see it, not as it is right now.