Synthetic data refers to any dataset that is generated artificially. Most datasets are generated based on research, use cases, or other activity. In the case of synthetically generated data, datasets are instead developed in a way that replicates real datasets, so that they can act as supporting information during model training or testing processes.
Gartner predicts that by 2024, 60% of data used for analytics and development projects will be synthetically generated, partly because it is easy to generate, reduces overhead costs, and is similar to real data. In a world where data is currency, synthetic data makes data accessible and scalable, giving businesses the competitive advantage they need to stay afloat in the market. It can be generated by drawing numbers based on distribution or creating a physical model based on real-world data.
Common Types of Synthetic Data:
- Fully synthetic data: Generated based on specific parameters the developers choose for their use case. Doesn’t rely on existing datasets but is rather meant to supplement them.
- Partially synthetic data: Meant to replace existing values within the dataset when there is a risk of privacy disclosure, or when existing datasets are incomplete, and gaps need to be filled.
- Hybrid synthetic data: Generated using both real and synthetic data. Random records of actual and synthetic data that are similar to each other are combined to form the resulting hybrid set.
Other Definitions Include:
- “Generated by applying a sampling technique to real-world data or by creating simulation scenarios where models and processes interact to create completely new data not directly taken from the real world.” (Gartner)
- “Annotated information that computer simulations or algorithms generate as an alternative to real-world data … created in digital worlds rather than collected from or measured in the real world. It may be artificial, but synthetic data reflects real-world data, mathematically or statistically.” (NVIDIA)
- “Artificially generated information that can be used in place of real historic data to train AI models when actual data sets are lacking in quality, volume, or variety.” (CIO)
Use Cases Include:
- Software testing: Using real data can pose a security issue for software testing, as the application’s security is not fool-proof. In this case, developers can use synthetic data to replicate the dataset and use that to test the software.
- Replicate existing data: AI/ ML models need vast amounts of data to understand what needs to be done. But it’s not always possible to get such a volume of data, especially if the instance is not typical. Synthetic data can be used to replicate existing data and fill those gaps. It applies to any use case such as digital twins, healthcare records, fraud detection systems, etc.
- Fill gaps in existing data: When companies have incomplete datasets, synthetic data can be used to fill the gaps necessary to train programming algorithms. Sometimes, it can also balance an uneven (or biased) dataset. For example, we can use synthetic data to fill those gaps if a health record dataset doesn’t have pulse rates for a few records.
Benefits of Synthetic Data:
- Helps businesses maintain compliance with regulatory measures by limiting the usage of their existing data
- Cuts down the overhead costs of accessing, processing, and storing actual data
- Helps research departments work with higher-quality data, as they’re responsible for generating it
- Helps scientists access datasets that might not be within their reach, which allows them to scale their programs with ease
- Helps researchers and developers to work with datasets that are uniform, structured, and labeled
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