In this developing world, artificial intelligence and machine learning are paving the way to developments that seemed like fiction 10 years ago. AI and ML models use large amounts of good quality data to solve or predict problems at hand. However, collecting that kind of data is more problematic than you think. Collecting data is very costly and very difficult. The sensitive data that these models use raises privacy concerns and hence, the data has legal consequences if you violate any boundary.
In this case, generating synthetic data that is able to represent the scenarios in the world comes handy. Statistically speaking 60% of the data used in AI and analytic projects will be synthetic in the near future. If you are looking to find information on synthetic data, then this is the guide for you. Here’s everything that you need to know about synthetic data.
As the name might suggest, synthetic data is data that is created artificially rather than being generated from events that occurred. The data is created from complex algorithms and is used in a wide range of activities. Whether it is test data for new products or tools or model validation or AI model training, synthetic data can cover everything for you.
Synthetic data is a type of data augmentation which is the technique used to increase the sheer amount of data by modifying copies of existing data. The biggest advantage of synthetic data is that it can be generated to meet the exact conditions that are not likely possible to occur in real life.
Although synthetic data has been used in the industry since the ’90s, it takes a lot of storage space which previously made it not so popular. However, with the arrival of computing power and storage space through cloud technology, synthetic data is used abundantly now.
The quality of data is measured by how effective it was when using it. When you think about it, synthetic means artificial, and artificial things will differ from real-world data. So , if you compare the same scenario with 2 different sets of data, one synthetic and one real, what will be the difference?
In 2017, a study was conducted keeping the same scenario as above in mind. Scientists needed to know if machine learning models could perform the same as real data with synthetic data. Data scientists were divided into 2 separate groups. 1 group worked with synthetic data while the other one with real data. 70% of the time results from synthetic data and real data were on par with each other. Due to this reason, the application of synthetic data has increased in the robotic industry, financial industry, healthcare, manufacturing, etc.
While overcoming real data usage restrictions due to privacy and laws, synthetic data seems like a saving grace for data scientists. If you can deal with some of the problems like missing outliers, user acceptance, and the tedious step of actually generating synthetic data, you will be good to go.