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Demystifying DeepSeek
Decoder
Synthetic data is artificially generated data that mimics real-world data. It offers several business benefits, ranging from addressing gaps in data, increasing quantity and ensuring privacy.
By leveraging synthetic data, businesses can innovate responsibly, improve decision-making and drive growth while safeguarding privacy and security.
What’s in it for you?
It can boost privacy, enable model training on scarce or sensitive data and accelerate development.
What are the trade-offs?
Synthetic data requires careful generation and validation; attention also needs to be paid to potential biases.
How is it being used?
It’s being used to aid research, simulate scenarios in testing and security contexts and can also enable data sharing.
What is synthetic data?
Synthetic data is artificially generated information that mimics the real-world data. It’s created using algorithms, but the actual techniques that can be used range from generative AI methods to statistical methods to agent-based modeling.
It’s valuable because acquiring, storing and processing data can be expensive; companies use synthetic data to either scale existing data sets, address gaps or mitigate any privacy issues (particularly pertinent in fields like healthcare and finance).
What’s in it for you?
Synthetic data offers businesses a number of significant advantages:
It can help businesses experiment with data and AI while mitigating privacy risks.
Collecting ‘real’ data can be expensive and takes time: synthetic data can help businesses scale data quickly and relatively cheaply.
Synthetic data can help teams test and learn quickly, reducing errors and identifying problems faster than they otherwise would be able to.
In short, synthetic data helps businesses harness the power of data while mitigating risks, accelerating innovation and driving growth in an increasingly privacy-conscious world.
What are the trade-offs of synthetic data?
Synthetic data can be powerful, but if used improperly or without sensitivity it can create issues around accuracy and reliability. It can also perpetuate biases and can be expensive to create.
Synthetic data's usefulness depends on its fidelity to real data. If the generation process isn't robust, the synthetic data might not accurately reflect real-world patterns. This can lead to flawed models and inaccurate insights.
Synthetic data can sometimes miss subtle but important nuances present in real-world data. If used to train an AI model for example, this can undermine the model's ability to handle edge cases or complex scenarios.
Validating the quality and usefulness of synthetic data can be challenging. Businesses need robust methods to ensure the synthetic data is fit for purpose and doesn't introduce unintended consequences.
Generating high-fidelity synthetic data can be computationally intensive, requiring significant processing power and potentially incurring costs, especially for complex datasets.
Although synthetic data can protect privacy, if used improperly it can occasionally reveal information from the original data set.
How is synthetic data being used?
Synthetic data is being used in a range of different industries and fields. They include:
Healthcare, where synthetic data is helping improve research by expanding datasets without the need for ever increasing amounts of highly sensitive personal patient information.
Banking, where synthetic data is strengthening fraud detection by helping teams simulate scenarios on a huge scale.
Synthetic data is also increasingly being used to train machine learning and AI models. It’s particularly valuable in areas where data can be scarce or challenging to acquire, but given the rise of generative AI — which is driving a need for more and more data — synthetic data has a part to play in AI development in just about every industry and sector.
Finally, synthetic data also has a part to play in data sharing — while the rules and regulations around privacy and data sharing are constantly evolving, creating new compliance risks for organizations, synthetic data opens up the possibility of sharing synthetic representations of data sets which approximate real-world data, but without private or personal information.
