Huge amounts of data are often needed to train AI/ML models. A synthetic dataset is used not only to augment actual data, but also to protect data privacy.
Table of Contents
What is a Synthetic Dataset?
Who Needs a Synthetic Dataset?
How are Synthetic Datasets Generated?
Synthetic Dataset Use Cases
Synthetic Dataset Challenges
Synthetic Datasets Based on Business Entities
What is a Synthetic Dataset?
Though created by computer, synthetic datasets are designed to mimic the format, structure, and statistical and mathematical characteristics of actual datasets. Synthetic data is used to train AI (Artificial Intelligence) or ML (Machine Learning) models, or test mathematical models – which require large amounts of operational or production data, that often contains sensitive information. Constraints on the use of sensitive data like PII can be avoided with a synthetic dataset that emulates sensitive data – but contains no actual sensitive data.
Who Needs a Synthetic Dataset?
To train AI and ML models, data analysts, scientists and developers need ever-larger, more diverse, and more accurately labelled datasets. Yet collecting, handling, and manually labelling such datasets at scale is challenging, if not impossible. Organizations often cannot obtain the quantity of data they need within the required timeframe – which delays the training of AI/ML models, raises associated costs, and negatively impacts project ROI. And even if such large datasets can be obtained, their use may be curtailed by privacy regulations if they contain sensitive or protected data like PII, credit cards, or medical records.
By generating and using synthetic datasets, data teams circumvent many of these issues. A high-volume synthetic dataset can be created more quickly than real-life data can be gathered. Artificial should comply with privacy regulations, by definition, although it may not accurately reflect real-world data/events.
A synthetic dataset drastically reduces the bias often found in actual datasets – growing the diversity of data by encompassing realistic, yet rare, cases that may not be found in real life. For example, a dataset containing satellite photos to train an image-classifying algorithm could include elements that don’t appear fully.
Finally, synthetic datasets slash the time required to label data, since the labeling is done automatically. And synthetic datasets reduce the overall time for dataset creation – since there’s no need to collect, unify, and de-dupe production data, or filter it for errors.
How are Synthetic Datasets Generated?
Synthetic data generation is carried out either by computer algorithms or simulations using 1 of 3 primary methods – by:
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Known Distribution
A simple tabular dataset can be synthetically generated without real data, so long as the generator understands the specific data characteristics required, and the statistical distribution of the real dataset being emulated. The more deeply the data structure is understood, the more realistic the synthetic dataset will turn out. -
Best-Fit Distribution
If a simple tabular dataset is needed, it can be synthetically generated by deriving a best-fit distribution from an actual dataset. Using the parameters defined in this distribution, the synthetic data points can be generated. -
Neural Network
The most sophisticated method for generating synthetic datasets is using a neural network. Neural networks can handle far richer data distributions than traditional algorithms (like decision trees) can – and are typically used by technology companies (like Amazon, Facebook, and Google) for image and speech recognition, natural language processing, and recommendation systems. What’s more, neural networks can synthesize unstructured data, including video.Creating synthetic datasets from a neural network generally involves:
– Diffusion models, that corrupt training images or audio with the addition of Gaussian noise, then teach the neural network to undo (de-noise) the corruption.
– Generative Adversarial Networks (GANs), where 2 neural networks work together to generate realistic, yet fake, data points – one neural network creates, the other attempts to differentiate.
– Variational Auto-Encoders (VAEs), where an unsupervised algorithm learns the original dataset’s distribution, then generates synthetic data leveraging an data architecture that uses a double “encode, then decode” transformation.
The method used to generate a synthetic dataset is usually determined based on a cost-benefit analysis – on consideration of the use case, generator capability, and organizational data governance best practices.
Synthetic Dataset Use Cases
Since a synthetic dataset is representative of a real dataset, it can be used for almost any purpose that a real dataset can be used for. For example, a synthetic dataset could:
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Protect sensitive data
With a synthetic dataset, PII and other sensitive information can be protected at scale. By using mimicking personal information, applications can be tested at scale, without the risk of using real sensitive data. -
Test applications prior to release
By using synthetic test data to fill gaps in production data, testers can test new software applications or updates when relevant data is not available. Synthetic datasets can also be used effectively for load or performance testing. -
Train AI/ML models
Training a machine learning algorithm is more efficient and faster when the algorithm can sample a combination of both real-life datasets, representing the norm, and synthetic datasets of outlier cases. -
Govern data
To reduce the bias often found under actual circumstances, a synthetic dataset can be used to stress-test models with data not normally found in the real world. The insights generated by AI/ML models can be used to create or modify the rules defined in your data governance tools. -
Augment data
A synthetic dataset is excellent “filler” when training models lack a sufficient volume of production data.
Synthetic Dataset Challenges
There are many challenges in the adoption, creation, and use of synthetic datasets. To start, creating high quality datasets is not simple and the industry is still immature. Second, the production of a synthetic dataset can consume significant time and resources. Third, it’s difficult to validate statistically that the synthetic dataset created is actually true to the format and structure of the original dataset it emulates. Fourth, incorrect creation of synthetic datasets could lead to the exposure of sensitive data. For all these reasons, it’s important to choose a synthetic dataset creation solution carefully.
Synthetic Datasets Based on Business Entities
The most balanced approach to creating synthetic datasets uses a business entity approach to data masking challenges. A business entity is any entity relevant to the business – a customer, an account, a product, an asset, an invoice. By leveraging this approach, the synthetic dataset generated is complete, consistent, realistic, and balanced – despite being based on real data from disparate systems.
The business entity model, when used in conjunction with intelligent business rules, is a highly pragmatic system that produces ready-to-use synthetic datasets, often in combination with built-in data masking tools.
