Advantages of synthetic data over real data
The main advantages of synthetic datasets over original datasets are
- With synthetic data, it is possible to generate an unlimited amount of data depending on the model requirements.
- With synthetic data, it is possible to create a quality dataset that can be risky and expensive to collect.
- With synthetic data, it is possible to acquire high-quality data that is automatically labeled and annotated.
- Data generation and annotation are not as takes time as is the case with real data.
Why use synthetic data (synthetic data vs real data)
Real data can be dangerous to obtain
More importantly, it can sometimes be dangerous to obtain real data. If you take autonomous vehicles, for example, AI cannot be expected to rely solely on real-world data to test the model. The AI driving the autonomous vehicle must test the model to avoid accidents, but getting its hands on accidents can be risky, expensive and unreliable, making simulations the only testing option.
Actual data could be based on rare events
If real data is difficult to obtain due to the rarity of the event, then synthetic data is the only solution. Synthetic data can be used to generate data based on rare events to train the models.
Synthetic data can be customized
Synthetic data can be customized and controlled by the user. To ensure that synthetic data does not miss edge cases, it can be supplemented with real data. Additionally, the frequency, distribution and diversity of events can be controlled by the user.
Synthetic data comes with automatic annotation
One of the reasons why synthetic data is preferred over real data is that it comes with perfect annotations. Instead of manually annotating data, synthetic data is accompanied by automated annotations for each object. You don't have to pay extra for data labeling, making synthetic data a more cost-effective choice.
Synthetic data allows non-visible data annotation
There are certain elements of visual data that humans are inherently incapable of interpreting, and therefore annotating. This is one of the main reasons for the industry's push towards synthetic data. For example, applications developed based on infrared imaging or radar vision can only work on synthetic data annotation because the human eye cannot understand the imagery.
Where can you apply synthetic data?
With the release of new tools and products, synthetic data could play a major role in the development of Artificial intelligence and machine learning models.
Currently, synthetic data is widely leveraged by – computer vision and tabular data.
With computer vision, AI models detect patterns in images. The cameras, equipped with computer vision applications, are used in many industries such as drones, automotive and medicine. Tabular data attracts a lot of interest from researchers. Synthetic data opens the door to the development of health applications, previously restricted due to privacy concerns.
Synthetic Data Challenges
Using synthetic data presents three major challenges. They are:
Should reflect reality
Synthetic data must reflect reality as closely as possible. However, it is sometimes impossible to generate synthetic data which does not contain any personal data elements. On the other hand, if synthetic data does not reflect reality, it will not be able to present the patterns needed for model training and testing. Training your models on unrealistic data does not produce credible insights.
Must be free from bias
Like real data, synthetic data can also be subject to historical bias. Synthetic data can reproduce biases if generated too accurately from real data. Data scientists Bias must be taken into account when developing ML models to ensure that newly generated synthetic data is more representative of reality.
Must be free from privacy concerns
If synthetic data generated from real-world data is too similar to each other, they can also create the same privacy issues. When real-world data contains personal identifiers, the synthetic data it generates may also be subject to privacy regulations.
Final Thoughts: Synthetic Data Opens Up New Possibilities
When you compare synthetic data and real-world data, synthetic data isn't far behind on three counts: faster data collection, flexibility, and scalability. By changing the settings, it is possible to generate a new data set that may be unsafe to collect or may not be available in reality.
Synthetic data helps in forecasting, anticipating market trends and making solid plans for the future. Moreover, Synthetic data can be used to test the veracity of models, their premises, and various outcomes.
Finally, synthetic data can do much more innovative things than real data. With synthetic data, it is possible to feed models with scenarios that will give us insight into our future.