Synthetic data refers to any production data not coming from direct measurement that applies to a given situation. Production data in this definition is information professionals persistently store and use for business processes.
Simply put, synthetic data is any kind of artificially generated data rather than raw information gathered from an event.
Examples of synthetic data include computer-generated data. Anonymized data is its subset.
Other interesting terms…
Read More about “Synthetic Data”
As has been said, the data computer simulations (e.g., synthesized music) generate is synthetic. It approximates the real thing but is fully algorithmically generated.
Anonymized data (e.g., home addresses, phone numbers, Social Security numbers, etc.) in privacy protection is a type of synthetic data. That said, using it means protecting the confidentiality of particular aspects of the data.
The video below shows how synthetic data is generated.
Synthetic Data Applications
Synthetic data has several uses, five of which are discussed in more detail below.
Synthetic data lets marketing teams run detailed individual simulations for better budget allocation. Users should, however, note that such simulations require the data owners’ consent to maintain compliance with privacy laws like the General Data Protection Regulation (GDPR).
Without synthetic data, self-driving car simulations won’t be possible. Autonomous vehicles need replicated street scenes to navigate, and that’s where using synthetic data comes in.
Clinical and Scientific Trials
Synthetic data can be used to determine a baseline for future studies and testing when real data is not yet available.
Understanding the format of unrecorded data is challenging. Researchers use synthetic data to analyze statistical properties, create parameters for related algorithms, or build preliminary models.
Organizations use synthetic data to secure their online and offline properties. They use it as training data for video surveillance. More advanced companies use deep fakes to test face recognition systems.
Why Use Synthetic Data?
Synthetic data is helpful since it can be generated to meet specific needs or conditions for which no real information is available. Real data, in this case, refers to raw data that comes directly from event observations and the like.
Synthetic data is easier to obtain, especially in the following circumstances:
- Privacy requirements limit data availability or how it can be used
- Data required to test a product slated for release does not exist or is not available to the testers
- ML algorithms require training data that may be too expensive to generate, as in the case of self-driving cars
While organizations already used synthetic data in the 1990s, it didn’t gain much traction until the 2010s, when computing power and storage space improved.
Synthetic Data Usage Pros and Cons
Like any other kind of information, synthetic data use presents both benefits and challenges.
Benefits of Using Synthetic Data
- Using real data is governed by rules and regulations that may be too strict or hard to comply with. Synthetic data can replicate all the necessary statistical properties of real data without exposing their owners, eliminating the issue.
- If real data does not exist, synthetic data can be used to simulate conditions that have not been encountered.
- Using synthetic data can eliminate problems brought on by nonresponse, skip patterns, and other logical constraints.
- Finally, synthetic data makes it easier for users to preserve relationships between variables instead of focusing on specific statistics alone.
Challenges in Using Synthetic Data
- While synthetic data can mimic real data, it isn’t a replica. It may be missing outliers that may affect analyses and research findings.
- Since synthetic data is artificially generated, it may not be very objective. That would affect the quality and integrity of a study.
- Also, synthetic data may not be universally accepted yet. As such, it may not be taken as valid by users who have not used it before.
- While synthetic data generation costs less than obtaining real data, it does not come free and requires time and effort. It needs to be compared with real data as well to ensure accuracy.
Synthetic data is gaining mainstream adoption, but its use still requires some improvement. Over time, organizations are bound to rely on it more than real data. They should note that, like any kind of data, though, it’s not perfect.