Synthetic data is useful for a variety of use cases. Whether you need to address privacy concerns, provide faster turnaround for testing, or train a machine learning algorithm, it can help.

When generating synthetic data, you must separate static data describing the subject and dynamic data describing events. The latter is called time series and needs to be handled in linked tables.

Realistic

Synthetic data is media artificially rendered to mimic the properties of real world data. It is used to train machine learning models for a variety of applications. This includes text, images and sound. It is often more practical than collecting a large number of real world samples and can be adapted to a specific use case.

When creating synthetic data, you need to consider whose privacy is protected. MOSTLY AI’s synthetic data generator learns statistical patterns distributed in rows, so if you have multiple events that belong to the same subject ID, this may create phantom data subjects.

While synthetic data is not a new concept, its popularity has grown rapidly in the wake of privacy concerns and regulatory pressures. It is increasingly used in healthcare, manufacturing and banking. It is also useful in training autonomous vehicles, as it can be much cheaper and safer than relying on real-world data. It can even eliminate the need for risky or dangerous field tests.

Compliant

For businesses looking to avoid the privacy, bias, and expense of real data, synthetic data is a solution. It can be used to train and test machine learning algorithms, without compromising privacy or violating compliance rules. Synthetic data can also be used for a wide variety of business purposes.

To generate compliant synthetic data, start by determining your business needs and compliance requirements. You should identify sensitive data points in the original data, and take steps to ensure that they are not included in the generated datasets. You should also ensure that the data you create is unbiased, and that it preserves statistical properties such as the distribution of values.

A popular way to use synthetic data is to create images for computer vision projects. For example, Alphabet’s subsidiary company Waymo uses a synthetic data generator to create datasets for training self-driving cars. Another common use is to generate time-series data for machine learning models.

Scalable

Using synthetic data for machine learning training allows data scientists to expand their models quickly without having to wait for new real-world observations. In addition, it can reduce the need to invest time and money in collecting and transferring real-world data, which is critical for compliance with privacy regulations.

One example of this is the way in which Alphabet’s subsidiary, Waymo, uses synthetic data to train its self-driving cars. Waymo claims that using synthetic data allows them to produce the same results as they would if they were training the cars on real-world observations.

Using synthetic data can also help companies minimize privacy concerns and get to market faster. It can also level the playing field between established tech giants and smaller upstarts by democratizing access to high-quality data at scale. However, it is important to note that generating a dataset does require some complexity and requires careful configuration and documentation. Therefore, it is best to use a dedicated artificial data generation solution that has the capabilities needed for scalability and quality control.

Affordable

Synthetic data generation tools provide a simple way to create meaningful copies of sensitive and valuable real-world data assets, like customer transaction data in banking or patient journeys in healthcare. They allow for easy sharing and collaboration without the burden of bureaucracy, danger to privacy or loss of data utility.

One of the big selling points for synthetic data is that it’s significantly cheaper and quicker to produce than collecting, labeling and constructing real-world data sets. This is particularly important in the case of data that’s either rare or dangerous to collect, such as fraud cases or road accidents that self-driving cars must react to.

In addition, it’s a great solution for companies that can’t share their real-world data with competitors due to strict privacy regulations. It allows them to gain insights while still adhering to PII compliance standards, a boon for companies in industries such as banking and finance or health care. And it can help them mitigate human bias embedded in the original data by introducing fairness contraints into the generation process.