Synthetic data can be defined as artificially annotated information. They are generated by algorithms or computer simulations and are widely used in the healthcare, industrial, and financial sectors. A look back at a key trend in the world of data!
The key differences between real and synthetic data
Synthetic data, also known as artificial data, is computer-generated rather than collected from real sources. While they are intended to represent patterns and characteristics similar to those of real data, they are not derived directly from real observations or events. There are therefore three main differences between conventional data and artificial data.
The first distinction between real data and synthetic data concerns the notion of representativeness. Real data comes from sources, measurements, or observations made in the real world. In fact, they reflect the characteristics and variations of a tangible, observed reality. They are therefore as representative as possible. Synthetic data, on the other hand, is generated in a programmed way. Although they are designed to reproduce patterns and characteristics similar to real data, they do not always capture all the complexity and variability of real data.
Real data is likely to contain sensitive information about individuals. They are governed by strong confidentiality principles, due to personally identifiable information (PII) or compliance risks. Synthetic data, on the other hand, is generated in such a way as not to contain any real or identifiable information. As such, they provide a workaround for data confidentiality issues, offering a safer alternative for sharing, analysis, and application development.
Synthetic data can be generated in unlimited quantities and tailored to the specific needs of an application. This frees you from the limitations of real data in terms of quantity and availability, giving you greater flexibility when testing, experimenting, or developing data-intensive applications.
How are synthetic data generated?
Synthetic data can be created using statistical models that reproduce the distributions, correlations, and characteristics of real data. They can also be generated via simulation. This involves creating simulated scenarios and processes that mimic real-life behavior. Machine learning can be used to generate synthetic data by learning from existing real data.
Finally, real data can sometimes be used as the basis for generating synthetic data. In this case, a number of elements are modified to preserve the confidentiality or sensitivity of the information. In all cases, synthetic data generation is always based on a thorough understanding of the characteristics and structures of your real data, in order to maximize its realism and representativeness.
What are the main advantages of synthetic data?
More flexible, more available, and often richer, there are many reasons to be interested in the generation of synthetic data, as they offer four major advantages:
Advantage #1: Limiting data confidentiality issues
Generating dummy data that contains no personally identifiable information means that data can be shared, analyzed, and processed without ever risking individual privacy or data protection regulations.
Advantage #2: Improve data accuracy
In many cases, real data can have information gaps. Synthetic data helps to fill these gaps by generating additional data for areas where real data is incomplete. This provides a more complete and accurate representation of the entire dataset. They can also be used to correct imbalances in data classes or to detect and compensate for outliers.
Advantage N°3: Guarantee data availability
Real data can often be scarce and difficult to access. With synthetic data, there are no quantitative constraints or dependence on limited real-world resources. They can be produced at will, allowing greater flexibility in project realization and scenario exploration.
Advantage N°4: Control costs linked to data collection and storage
Collecting real data can be costly in terms of financial, human, and material resources. By using synthetic data, it is possible to generate data at a lower cost. What’s more, synthetic data can be generated on demand, reducing storage capacity requirements and optimizing costs.
Some examples of uses for synthetic data
Synthetic data already meets a number of uses. When it comes to synthetic location data, for example, routes, and movements of people, or vehicles can be easily simulated, saving considerable time in urban planning or logistics.
Synthetic image and video data are used to simulate scenes, objects, and movements, and are commonplace in the world of virtual reality, video analysis, and object recognition model training. Synthetic text data is used to simulate documents, conversations, and even sentiment analysis.
Finally, synthetic financial data can be created to simulate transactions, investment portfolios, price variations, trading volumes, and so on. They are therefore very common in the analysis of financial markets or the development of trading algorithms.