Synthetic Data

Extracted Attributes

• Types

  Multimedia, tabular, text

• Nature

  Artificially generated data

• Purpose

  Validate mathematical models, train machine learning models, train AI models, software testing, data analysis, research, demoing, building personalized products

• Applications

  Computer simulations (flight simulators, music synthesizers, medical imaging, economic models), healthcare, finance, insurance, telecommunications, natural language processing (NLP), computer vision tasks

• Creation Method

  Algorithms, statistical methods, AI techniques (deep learning, generative AI)

• Primary Benefit

  Protects confidentiality and privacy (e.g., PII, patient privacy)

• Key Characteristic

  Mimics statistical properties of real-world data without containing original values

• Regulatory Compliance

  Helps comply with HIPAA, GDPR

Synthetic data

Synthetic data are artificially-generated data not produced by real-world events. Typically created using algorithms, synthetic data can be deployed to validate mathematical models and to train machine learning models. Data generated by a computer simulation can be seen as synthetic data. This encompasses most applications of physical modeling, such as music synthesizers or flight simulators. The output of such systems approximates the real thing, but is fully algorithmically generated. Synthetic data is used in a variety of fields as a filter for information that would otherwise compromise the confidentiality of particular aspects of the data. In many sensitive applications, datasets theoretically exist but cannot be released to the general public; synthetic data sidesteps the privacy issues that arise from using real consumer information without permission or compensation.

Web Search Results

• Synthetic data generation methods in healthcare: A review on open ...

  ( ( ( Synthetic data are artificially generated data that can mimic real-world data without compromising the identity of the individuals. Thus, synthetic data offer a unique way to leverage the wealth of health information while preserving patient privacy with respect to regulations like the Health Insurance Portability and Accountability Act (HIPAA) in the U.S. or the General Data Protection Regulation (GDPR) in Europe. The value of synthetic data in healthcare is of great importance. [...] Synthetic data can serve as a substitute for real data when training AI models. But how can we generate synthetic data? Synthetic data can be generated by capturing the statistical properties of the real data to create new data points with similar properties. According to the literature, a variety of methods has been proposed for the generation of high-quality synthetic tabular, imaging, radiomics, time-series, and omics data, which are categorized into: (i) statistical-based methods, like the [...] Synthetic data can be used to improve the performance of AI models, to accelerate drug discovery through simulated clinical trials, to improve data accessibility by completing existing data and increasing data volume and to protect privacy by reproducing the original data avoiding any personally identifiable information (PPI) ( ( ( ( ( Thus, synthetic data do not only secure patient anonymity but also allow researchers to overcome barriers in data availability which empowers them to conduct a

• Synthetic Data Generation Benchmark & Best Practices ['25]

  Synthetic data is artificial data created by using different algorithms that mirror the statistical properties of the original data but do not reveal any information regarding real-world events or people. For example, data produced by computer simulations would qualify as synthetic data. This includes applications like music synthesizers, medical imaging, economic models, and flight simulators, where the outputs mimic real-world phenomena but are entirely generated through algorithms.

• What Is Synthetic Data? - IBM

  My IBM Log in Subscribe # What is synthetic data? ## What is synthetic data? Synthetic data is artificial data designed to mimic real-world data. It’s generated through statistical methods or by using artificial intelligence (AI) techniques like deep learning and generative AI. Despite being artificially generated, synthetic data retains the underlying statistical properties of the original data that it is based on. As such, synthetic datasets can supplement or even replace real datasets. [...] ## Types of synthetic data Synthetic data can come in multimedia, tabular or text form. Synthetic text data can be used for natural language processing (NLP), while synthetic tabular data can be used to create relational database tables. Synthetic multimedia, such as video, images or other unstructured data, can be applied for computer vision tasks like image classification, image recognition and object detection. Synthetic data can also be classified according to its level of synthesis: [...] Synthetic data can act as a placeholder for test data and is primarily used to train machine learning models, serving as a potential solution to the ever-growing need for—yet short supply of—high-quality real-world training data for AI models. However, synthetic data is also gaining traction in sectors like finance and healthcare where data is in limited supply, time-consuming to obtain or difficult to access due to data privacy concerns and security requirements. In fact, research firm Gartner

• What is synthetic data? - MOSTLY AI

  Synthetic data is the perfect fuel that AI and machine learning development projects need. Synthetic data is created by generative AI algorithms, which can be instructed to create bigger, smaller, fairer or richer versions of the original data. Due to how the synthesization process takes place, the data can be augmented to fit certain characteristics. In a way, synthetic data is like modelling clay for data scientists and data managers. For example, upsampling minority groups in a dataset can [...] ### What are use cases for synthetic data? Good quality synthetic data is an accurate representation of the original data. As a result, it can be used as a drop-in placement for sensitive production data in non-production environments. Typical use cases include: AI training, analytics, software testing, demoing, and building personalized products. [...] For example, synthetic data versions of customer databases, patient journeys, medical records or transaction data are used by companies to make data driven decisions while respecting the privacy of their customers. Synthetic data is an industry-agnostic solution, used across various fields from finance and healthcare to insurance and telecommunications. Real life examples of synthetic data projects ---------------------------------------------

• What is Synthetic Data Generation? A Practical Guide - K2view

  Synthetic data generation is the process of creating artificial data that mimics the statistical patterns and properties of real-life data using algorithms, models, and other techniques. Even though it’s usually based on real data, fake data often contains no actual values from the original dataset. Unlike real data, which may contain sensitive or Personally Identifiable Information (PII), fake data ensures data privacy, while enabling data analysis, research, and software testing. [...] ## Synthetic data generation accelerates innovation Synthetic data can be described as fake data, generated by computer systems but based on real data. Enterprises create artificial data to test software under development and at scale, and to train Machine Learning (ML) models. There are 2 types of data: This paper focuses on structured, tabular data and the 4 methods used to synthesize it: