What is Synthetic Data?
Synthetic data is artificially generated information that has no connection with real-world events. It’s created algorithmically to mimic real datasets. Synthetic data is generated in a way to have similarities in mathematical and statistical properties with genuine data. This allows researchers and developers to use it for various purposes: training machine learning (ML) models, testing software, or using it in situations where actual data may be scarce or sensitive.
Synthetic data safeguards privacy and confidentiality, which is especially important in fields like healthcare or finance. Moreover, it allows to create large datasets with minimal costs and in less time than would be spent on collecting real-world data.
Types of Synthetic Data
There are several types of synthetic data, each serving different purposes:
Fully Synthetic Data
This type of synthetic data has no direct link to actual data points. It’s entirely generated from random processes or models. Fully synthetic data is used in situations where the real-world scenario is too complex or sensitive to replicate with actual data.
Partially Synthetic Data
To create partially synthetic data, developers modify or replace some aspects of real data with generated content. This type of data is used to protect sensitive information while retaining some level of real-world accuracy.
Hybrid Synthetic Data
This is a combination of real and synthetic data, where the latter is used to fill in gaps or expand datasets in a controlled manner. Hybrid synthetic data is great for enhancing diversity in datasets and improving ML model training process while maintaining confidentiality and privacy.
Methods for Generating Synthetic Data
Statistical Distribution-Based Generation
This method relies on statistical models to generate data with similar distributions (like normal, binomial, or exponential) to the original dataset. Techniques like Monte Carlo simulations or bootstrapping are used to create large datasets that statistically resemble the actual data in terms of mean, variance, and other properties.
Industry Applications
Finance
In the financial industry, statistical methods for generating synthetic data are used for risk modeling and financial simulations. By leveraging this method, banks and investment firms can analyze market behaviors without exposing sensitive financial information.
Market research
Synthetic data is often used to simulate consumer behavior and preferences, helping companies to predict market trends and evaluate the potential success of products.
Agent-Based Modeling (ABM)
ABM is used to create complex, interactive simulations of individual agents (like cells in biology, consumers in economics, or vehicles in traffic systems) and their interactions within a defined environment. These individual agents operate based on a predetermined set of rules. Agent-based modeling can produce dynamic and unpredictable datasets and is useful for understanding complex dynamics and nuanced systems or organisms.
Industry Applications
Urban Planning and Transportation
ABM helps in simulating traffic patterns, urban development scenarios, or public transportation systems. This data is then used to optimize city planning and mobility solutions.
Epidemiology
This method is applied to model the spread of diseases and the impact of public health interventions. ABM aids in pandemic planning and response strategies.
Neural Network Techniques
Variational Autoencoders (VAEs)
VAEs use deep learning to encode data into a compressed representation. They employ the input data's distribution to generate new data points. VAEs do so while maintaining the statistical characteristics of the original dataset. Variational Autoencoders are particularly useful for capturing the underlying structure and variability within complex datasets.
Industry Applications
Biotechnology
VAEs are applied to create genomic sequences or protein structures for research. This significantly facilitates discoveries without compromising sensitive genetic information.
Content Creation
With the help of variational autoencoders creators can generate realistic textures, graphics, or other digital assets for video games and virtual reality environments.
Generative Adversarial Networks (GANs)
GANs consist of two neural networks – a generator and a discriminator. These neural networks are employed in a competitive setting to produce data: the generator creates data, while the discriminator evaluates its authenticity. This refines generator’s capability over time and produces highly realistic synthetic data.
Industry Applications
Entertainment and Media
GANs produce lifelike audio, video, or images for movies, music, and art. This method is also applied in special effects when generating realistic characters and environments.
Fashion and Retail
Generative adversarial networks can design virtual fashion items or inventory for online retail. This aids in product visualization and marketing strategies while saving time and money on real photoshoots.
Diffusion Models
This method starts with a dataset, gradually adds noise to it, and then learns to reverse the process to create new, high-quality synthetic instances. Diffusion models are known for generating high-quality, incredibly detailed images or audio.
Industry Applications
Audio and Speech Processing
Creates realistic speech or environmental sounds that can be employed in telecommunications, media, and AI-driven customer service applications.
Climate Modeling
Diffusion models are capable of simulating weather patterns or climate phenomena. This can be applied to predict changes, impacts, or help in planning and response strategies.
Other techniques for synthetic data generation
Rules engine, entity cloning, and data masking can be used in the process of generating or handling synthetic data, although they are not synthetic data generation methods in the traditional sense of creating new data from scratch. Here's how they fit into the context of synthetic data:
Rules Engine
This technique created data via user-defined business policies. This means that a defined set of rules or logic dictates how data should be generated or modified. While it can be used to create synthetic data, it often serves more as a method for transforming or manipulating existing data.
Entity Cloning
In the context of synthetic data, entity cloning involves copying and modifying existing data entities to create new, synthetic versions. Entity cloning uses duplication and altering of real data to protect sensitive information rather than generating entirely new datasets from statistical models or algorithms.
Data Masking
This technique is utilized to hide or obscure sensitive information in a dataset. Data masking can be integrated in the process of preparing synthetic data, when the goal is to create a version of the original dataset with the same structural and statistical characteristics but without revealing any sensitive information.
Tools for Creating Synthetic Data
There are various tools designed for synthetic data creation that adhere to different projects’ needs and requirements. Here are some of them:
MOSTLY.AI
A synthetic data generator that excels in creating realistic and detailed datasets. MOSTLY AI generates anonymized datasets that maintain the statistical properties of the original data. It employs advanced machine learning techniques, particularly GANs, to produce data that is useful for testing, analytics, and training purposes. MOSTLY AI is set on creating data without compromising personal privacy.
Datomize
This tool uses advanced algorithms to generate synthetic data that closely mirrors the original data’s structure and statistical properties. Datomize is popular in the financial sector where it simulates banking transactions and customer data while maintaining privacy.
Mimesis
A Python library that generates high-quality synthetic data for testing and filling databases during development. Mimesis supports multiple languages and provides a wide range of data categories – from personal information to business-related data.
Hazy
Hazy allows organizations, especially in the financial sector, to perform effective data analysis, testing, and development. The platform enables companies, particularly in fintech, to scale their data operations without exposing sensitive customer information.
