Some data are difficult to gather or are infrequent. Collecting data on the wide range of real-world road events for an autonomous car, for example, maybe prohibitively costly. Bank fraud, on the other hand, is a rare occurrence.
Because fraudulent transactions are uncommon, gathering enough data to build ML models that can forecast fraudulent transactions is rare.
It’s also complicated in the healthcare sector where your sensitive data is under several regulations all around the world with the General Data Protection Regulation in Europe (GDPR) or the Personal Information Protection Law (PIPL) in China.
So, instead of struggling to acquire expensive real information, generating a large amount of synthetic data solve the issue for a penny on the dollar. By 2024, it is expected that 60% of the data utilized in AI and analytics projects will be generated synthetically.
What is synthetic data?
Synthetic data is data that is artificially made, instead of data that comes from actual events. People use synthetic data for different reasons, including testing new products and tools, validating models, and training AI models.
Why is synthetic data important now?
Synthetic data is important because it can be made to look like real data. This can be useful in many cases, for example when you need data that doesn’t exist or when you need to test something out.
Data may be required for testing a product that is scheduled to go public, but such data does not exist or is not available to the testers. On the other hand for machine learning algorithms, training data is required.
When real data is not available or difficult to collect, we just have to go for synthetic ones. Synthetic data first started to be used in the ’90s, with the arrival of the cloud in the late 2010s making it more accessible.
Several industries for several applications can benefit from it:
- Social Media
- Automotive and Robotics
- Financial services
Applications are almost endless, your Marketing, HR, IT (Software and Cybersecurity), and R&D can leverage that for their operations but also anything business cases that use machine learning.
Synthetic data allows us to continue developing new and unique products and services when real-world data is lacking or inaccessible. Worth mentioning: Regulators are miles away on the matter.
Synthetic Data vs Real Data?
Data is utilized in applications and the most straightforward indicator of data quality is data’s usefulness when in use. Today, one of the most frequent applications for data is machine learning.
The research of artificial intelligence (AI) has advanced to the point where it’s now being used by NASA. Researchers from NASA and MIT wanted to see if AI models generated from synthetic data could compare with real-world ones.
They divided data scientists into two categories in a 2017 research: one that used artificial data and another that used real data. The group using synthetic data was able to match the results of the group utilizing genuine information 70 percent of the time.
This would make synthetic data more advantageous than other privacy-enhancing technologies (PETs) such as data masking and anonymization.
Benefits of synthetic data
It may appear like an unlimited supply of scenarios for testing and development when you can make data that resembles the real thing. Although this is true, it is important to remember that any synthetic models built that way will only be able to simulate general trends.
Bypassing restrictions of collecting and processing real data:
Real data may be limited by privacy restrictions or other legislation. Synthetic data can replicate all essential statistical qualities of real data without exposing it, thus eliminating the problem.
Synthetic data are also immune to some issues that can include people not answering questions, people skipping questions, and other logical constraints.
Synthetic data tries to keep the relationships between different variables the same instead of just looking at specific statistics. The benefits of synthetic data show that its use will only continue to grow as our data becomes more complex and harder to access.
Some rules of thumb for synthetic data:
There are three main types of synthetic data: generated, simulated, and emulated. Each type has different benefits and drawbacks.
Pure Synthetic: This data does not contain any original data, as it is completely synthetic. This implies that re-identification of any single unit is practically impossible, and all variables are still fully accessible.
Partially Synthetic: Some data is replaced with synthetic data. This helps the model work better, but it means that some people could find out true information about the dataset.
Hybrid Synthetic: Hybrid synthetic data is data that is made up of both real and synthetic data. This ensures the relationship and integrity between other variables in the dataset. The original distribution of the data is investigated, and the nearest neighbor of each data point is found. For each record of real data, a near-record in the synthetic data is chosen.
Two known basic methods for generating synthetic data are::
Agent-based modeling: To do this, a model is created to explain an observed behavior and then reproduce random data using the same model. It emphasizes the study of systemwide interactions among actors.
Deep learning models: Generative adversarial networks (GAN) and variant autoencoders are synthetic data generation technologies that improve data usefulness by feeding models with more information.
Synthetic Data Limitations:
Some data points may be missing from synthetic data. This is because synthetic data can only mimic real-world data it is not an exact replica.
The quality of the synthetic data is based on the quality of the input made and how well the data was generated. If there are biases in the input data, they will be reflected in the synthetic data.
How to accept the results is tricky for someone that doesn’t saw the benefits. For example, if I gave you a medicine AntiCovid that never cured anyone before? Would you take it?
Synthetic data is not easy to master, for its production and controlling the outcome. I would recommend running trials with synthetic data and authentic ones to avoid inconsistencies especially when we manipulate complex datasets.
I described last year’s actual possibilities with AI and I believe that the next generation of algorithms will revolution those like for self-autonomous vehicules and image recognition.