Synthetic Data

Artificial data, often generated with the support of deep neural networks, allow the creation of highly realistic data sets based on existing data. This solution makes it possible to rely on Big Data while protecting the identity of people in a database and avoiding their reidentification.
Technology Life Cycle

Technology Life Cycle


Marked by a rapid increase in technology adoption and market expansion. Innovations are refined, production costs decrease, and the technology gains widespread acceptance and use.

Technology Readiness Level (TRL)

Technology Readiness Level (TRL)

Ready for Implementation

Technology is developed and qualified. It is readily available for implementation but the market is not entirely familiar with the technology.

Technology Diffusion

Technology Diffusion

Early Adopters

Embrace new technologies soon after Innovators. They often have significant influence within their social circles and help validate the practicality of innovations.

Synthetic Data

Synthetic Data refers to artificially generated data sets, enabling privacy-friendly Big Data innovation. These artificial data sets are based on original data that often include personal details collected from sources like CRM databases, financial transactions, medical records, or smart city data.

Existing real-world data is used to train a synthetic data engine in a secure IT environment such as a private cloud, SaaS contexts, or within premises. In the engine, deep neural networks then automatically identify and understand patterns, structures, and correlations, even in vast and complex data sets. When training is complete, the software can generate unlimited synthetic data sets, retaining the statistical properties of the original data source. Some alternative techniques include semantic approaches, generative adversarial networks, and statistically rigorous sampling from real data.

Synthetic Data can be used for training AI models, product demos, hackathons, scenario simulations, internal prototyping, advanced analytics, development and testing, data monetization, and open innovation, as sharing data with third parties no longer poses privacy concerns. It is also compliant with GDPR and other data protection regulations, as customer identification becomes impossible. It also supports smaller companies, startups, and academia to innovate in a world where Big Data is concentrated in the hands of Big Tech. Applications can be seen across different sectors, such as finance, insurance, healthcare, government, mobility, and telecommunications.

This solution allows for more privacy-compliant, scalable, faster, and less expensive access to enhanced data, as opposed to real data, which is often expensive, biased, imbalanced, unavailable, or unusable due to privacy regulations. It also overcomes a flaw of classic data anonymization techniques, such as data destruction, where the reidentification of individual customers is still possible, even with the few remaining data points.

Future Perspectives

One of the main hurdles to applying Big Data strategies and AI training models lies in privacy concerns. Synthetic Data has the potential to democratize Big Data and AI systems while protecting individual privacy and flourishing innovation across sectors. In the future, Synthetic Data could overshadow real data in training models and could become the new norm. Access to artificial datasets could also allow academia and small and medium businesses to create powerful innovations and compete with Big Tech, creating more diversity of solutions and perspectives.

Image generated by Envisioning using Midjourney

Synthetic data is a rapidly evolving field with growing interest from multiple industry stakeholders and European bodies. In particular, the pharmaceutical
Synthetic data is one of those ideas that seems almost too good to be true.
Synthetic Data allows organizations to innovate with their valuable big data assets without putting their customers' privacy at risk. In the fourth part of our mini video series on Synthetic Data we will cover how exactly Synthetic Data enables privacy-friendly innovation.
Learn about the importance of synthetic data, its main use cases, how it can benefit computer vision projects, and methods for generating synthetic datasets.
Achieving projects placing a heavy demand on data is difficult with real-world data. Learn from Steve Harris, CEO of Mindtech, how synthetic data helps.
Synthetic data is annotated information that provides an inexpensive alternative to real-world data and is increasingly used to create accurate AI models.
Synthetic data is often treated as a lower-quality substitute and used when real data is inconvenient to get, expensive or constrained by regulation. However, this reaction misses the true potential of synthetic data. Gartner estimates that by 2030, synthetic data will completely overshadow real data in AI models.
In a report published today, 14 December, the Behavioural Insights Team (BIT) summarised their investigation into the uses of synthetic data in government.
Synthetic data is attracting increasing attention from technicians and legal scholars in recent years. This is especially noticeable among entities and people working on data-driven technologies, particularly in the artificial intelligence application development and testing sector, where sheer volumes of data are needed. In these circles, synthetic data has become a growing trend under the “fake it till you make it” concept by promising to alleviate existing data access and analytics challenges while respecting data protection rules. Given the rising prospects and acceptance of data synthesis, there is a need to assess the legal implications of its generation and use, the starting point being the legal qualification of synthetic data.

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