Synthetic data lets companies build, train and test machine learning models without worrying about privacy or other barriers to real-world use. Tools such as random sample generators and generative adversarial networks make it possible to create datasets that closely resemble real data but don’t contain any information that could identify people or other entities.
The process of generating synthetic data is often much faster and cheaper than collecting real-world data. This is because generating data from scratch can be done at a fraction of the cost of manually labeling real-world data, which requires significant human resources and specialized software tools.
Companies in the banking and finance industry gather a lot of data on their customers, but this often contains personally identifiable information (PII). Using synthetic data allows them to use that data for model training without breaching customer privacy.
There are a few ways to generate synthetic data. Commercial vendors offer platforms and frameworks that plug into your data pipeline and provide synthetic dataset generation and evaluation functionality out-of-the-box. There are also open-source tools that allow you to build your solution. Commercial vendors are a good choice if you need to share your synthetic data with multiple departments or external stakeholders, as they will have built-in and tested privacy evaluations.
Data is the fuel that drives artificial intelligence, teaching computers to recognize and automatically respond to objects, actions, and commands. While some companies mine the internet or purchase data from others, others manufacture their own. It’s called synthetic data, and recent research has shown it is an efficient and relatively inexpensive alternative to real data.
It’s also faster to produce than real data, which requires time-consuming manual labeling for supervised learning tasks. For example, Waymo invested billions in its self-driving car project and spent over a decade collecting millions of miles of real-world driving data to train its autonomous vehicle technology.
But it isn’t practical for every industry to spend the time and money needed to collect a large volume of data for model training and testing. This is where synthetic data comes in, allowing businesses to accelerate the process and improve the quality of their results without sacrificing privacy or exposing confidential information. This can help them abide by the strictest regulations, including those related to health care, financial services, and data analytics.
It’s more accurate
Synthetic data mimics real-world characteristics and patterns, but without revealing personal or commercial sensitive information. This allows analytics and machine learning to take place without the need for access to confidential or proprietary data. This enables organizations to gain valuable insights and make better decisions without exposing any sensitive information and also addresses privacy concerns as AI regulation tightens around the world.
While there are several ways to collect synthetic data, it can often be more complex and time-consuming than using real data. Traditionally, synthetic data has been generated by hand, by human-assisted software or manually using tools and databases.
However, several new approaches to data synthesis have emerged that can make the process faster and more cost-effective. Many businesses now rely on these methods to supplement their existing datasets with more specialized and useful information. This makes synthetic data increasingly mainstream, with healthcare and financial services sectors using it to train AI models on a wider range of fraud variations, for example.
It’s more flexible
One of the biggest benefits of synthetic data generation is its flexibility. For example, a company can create data that has various facial hair styles, glasses, head poses and skin tones to produce more balanced datasets for machine learning models. Likewise, it can produce non-visible data such as infrared or radar images to help model performance. This allows teams to build more realistic, comprehensive datasets without the cost of real-world data collection and annotation.
This is especially helpful in situations where collecting real-world data may be too dangerous or expensive. For instance, autonomous vehicle companies often use simulations to train AI on road traffic incidents because real car crashes are too risky to collect and expensive to annotate. This type of data also helps mitigate bias issues and democratizes access to quality data while reducing costs. In addition, it is possible to create time series synthetic data that resembles the structure of actual tabular data (such as bank transactions). This provides more precise, reliable results for models.
This is Aryan, I am a professional SEO Expert & Write for us technology blog and submit a guest post on different platforms- Technoohub provides a good opportunity for content writers to submit guest posts on our website. We frequently highlight and tend to showcase guests.