Different Types of Synthetic Data Generation
Synthetic data enables organizations to share datasets that look and feel real, but don’t reveal any personal information. This has tremendous benefits when it comes to securing customer privacy.
Businesses use synthetic data when real data isn’t available due to expense, sensitivity, or processing time. The process starts with a machine learning fitted distribution from which to sample new data.
Generating a single subject table
A Single subject table is a collection of data about a single person, for example a bank statement or medical records. A Single subject table is the simplest type of synthetic dataset to generate. It can be used to test models or train algorithms.
A single subject table is not a complete dataset, and it is important to keep in mind that the order of rows will not be preserved. For instance, the data subjects’ names will not be sorted in alphabetical order. However, you can reintroduce this sorting in post-processing.
Synthetic data generation is a powerful tool for solving difficult problems that real data can’t. For instance, it can help reduce AI bias by ensuring that minority classes are well represented in the training set. It also makes it possible to analyze sensitive data without compromising privacy. The process of creating a synthetic data set varies depending on the tool you use, but it usually begins with connecting a generator to an existing dataset and identifying personally-identifying fields for exclusion or obfuscation.
Some common types of synthetic data include tabular datasets, image data, video and audio data, and time series data. Synthetic data can be generated using traditional methods such as tools or software, but these may not provide enough data for a comprehensive test. Alternatively, it can be generated by partnering with a third party that provides this service. These companies often specialize in one specific type of synthetic data and can provide more detailed and accurate results.
Generating multiple subject tables
While the process of synthesizing multiple subject tables varies between tools, most of them work by generating data that is statistically similar to the original dataset and then sampling it in order to produce synthetic data. The result is a set of data that has the same structure as the original dataset but is not personally identifiable, which allows for safe sharing and use.
The key to a successful machine learning model is having an adequate volume of diverse examples on which to train. This can be difficult with real data because each company creates its own transactional documentation that has idiosyncratic design flair through color, font, format, stamps, signatures, seals, and other variables. Synthetic data can supplement the training set by providing an adequate volume of different examples that are more representative of real-life data.
Adding synthetic data to the training set can also help correct for bias. For example, a mortgage lender might have a disproportionate number of male loan applicants and would benefit from synthetic applications from strong female candidates.
A number of businesses are using synthetic data for their machine learning needs. For example, American Express used GANs to generate fraudulent data and augmented its real-world dataset with synthetic fraud cases to improve the accuracy of its AI fraud prevention models. Another use of synthetic data is for medical research, where it can be used to mimic a patient’s real-world condition and generate more accurate results.
Generating time series data
Time series data is a set of measurements that are collected over time. It can be used for a variety of business applications, including market predictions, transaction recording, nature forecasts, component monitoring in machines, and more. This type of data is a great fit for machine learning algorithms, such as autoregressive models and GANs. However, it can be difficult to train these models on small datasets because they tend to overfit and require large amounts of training data.
Synthetic data generation can be used to overcome these problems. It is possible to generate time-series data that looks similar to real data, but it is less expensive and easier to manage. This is particularly important for businesses that do not have the resources to collect and store scalable data.
To use synthetic data, you must first determine the requirements for your project. This will include the compliance standards and privacy concerns. Once you know these factors, you can select the appropriate data generator for your application. Some tools can identify personally-identifying fields in your data and label them for exclusion or obfuscation. Once these fields are removed, the generator can begin to generate artificial data based on statistical patterns.
When using a synthetic data generator, it is important to keep in mind that the order of rows will not be preserved. This is because the synthetic data is generated by a generative model, which learns the statistical distribution of real data. To avoid errors, you should always enter a smaller number of subjects under Output settings than the number of subjects in your original subject table.
Generating image and video data
Synthetic data is a useful tool for overcoming bias in machine learning algorithms. It allows data scientists to test and train models without risking personal information or violating privacy laws. It also makes it easy to meet regulatory requirements and protect customer data. There are several different ways to generate synthetic data.
Some businesses choose to use tools or software, while others partner with third parties that offer a particular type of synthetic data generation. These tools or third-party providers have experience in generating high-quality data and offer a more robust, quality-controlled solution than conventional methods.
Creating synthetic data for training and testing is important, as real-world data can be expensive and difficult to collect. This is especially true for sensitive data sets, such as health and privacy information. Using synthetic data can help companies avoid privacy issues, allowing them to train and test their machines with a wider range of attributes.
A common use case for generating synthetic data is to generate image and video data. This is used in fields such as computer vision and autonomous vehicle technology. Other types of synthetic data include tabular data and text data. The latter is commonly used by chatbots and machine translation systems.
The best enterprise-grade synthetic data generators use business entity modeling to produce realistic, balanced and quality-controlled datasets for your testing or training needs. They also have automated privacy checks to ensure your data is free of personal information.
