Developing successful AI and ML models requires access to large amounts of high-quality data. However, collecting such data is challenging because:
- Many business problems that AI/ML models could solve require access to sensitive customer data such as Personally Identifiable Information (PII) or Personal Health Information (PHI). Collecting and using sensitive data raises privacy concerns and leaves businesses vulnerable to data breaches. For this reason, privacy regulations such as GDPR and CCPA restrict the collection and use of personal data and impose fines on companies that violate them.
- Some types of data are costly to collect, or they are rare. For instance, collecting data representing the variety of real-world road events for an autonomous vehicle may be prohibitively expensive. Bank fraud, on the other hand, is an example of a rare event. Collecting sufficient data to develop ML models to predict fraudulent transactions is challenging because fraudulent transactions are rare.
As a result, businesses are turning to data-centric approaches to AI/ML development, including synthetic data to solve these problems.
Generating synthetic data is inexpensive compared to collecting large datasets and can support AI/deep learning model development or software testing without compromising customer privacy. It’s estimated that by 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated.
What is synthetic data?
Synthetic data, as the name suggests, is data that is artificially created rather than being generated by actual events. It is often created with the help of algorithms and is used for a wide range of activities, including as test data for new products and tools, for model validation, and in AI model training. Synthetic data is a type of data augmentation.
Why is synthetic data important now?
Synthetic data is important because it can be generated to meet specific needs or conditions that are not available in existing (real) data. This can be useful in numerous cases such as
- when privacy requirements limit data availability or how it can be used
- Data is needed for testing a product to be released however such data either does not exist or is not available to the testers
- Training data is needed for machine learning algorithms. However, especially in the case of self-driving cars, such data is expensive to generate in real life.
What are its applications?
Industries that can benefit from synthetic data:
- Automotive and Robotics
- Financial services
- Social Media
Business functions that can benefit from synthetic data include:
Synthetic data allows us to continue developing new and innovative products and solutions when the data necessary to do so otherwise wouldn’t be present or available. For a more detailed account, feel free to check our article on synthetic data use cases/applications.
Comparing synthetic and real data performance
Data is used in applications and the most direct measure of data quality is data’s effectiveness when in use. Machine learning is one of the most common use cases for data today. MIT scientists wanted to measure if machine learning models from synthetic data could perform as well as models built from real data. In a 2017 study, they split data scientists into two groups: one using synthetic data and another using real data. 70% of the time group using synthetic data was able to produce results on par with the group using real data. This would make synthetic data more advantageous than other privacy-enhancing technologies (PETs) such as data masking and anonymization.
Benefits of synthetic data
Being able to generate data that mimics the real thing may seem like a limitless way to create scenarios for testing and development. While there is much truth to this, it is important to remember that any synthetic models deriving from data can only replicate specific properties of the data, meaning that they’ll ultimately only be able to simulate general trends.
However, synthetic data has several benefits over real data:
- Overcoming real data usage restrictions: Real data may have usage constraints due to privacy rules or other regulations. Synthetic data can replicate all important statistical properties of real data without exposing real data, thereby eliminating the issue.
- Creating data to simulate not yet encounteredconditions: Where real data does not exist, synthetic data is the only solution.
- Immunity to some common statistical problems:These can include item nonresponse, skip patterns, and other logical constraints.
- Focuses on relationships:Synthetic data aims to preserve the multivariate relationships between variables instead of specific statistics alone.
These benefits demonstrate that the creation and usage of synthetic data will only stand to grow as our data becomes more complex and more closely guarded.
Synthetic data generation / creation 101
When determining the best method for creating synthetic data, it is important to first consider what type of synthetic data you aim to have. There are three broad categories to choose from, each with different benefits and drawbacks:
Fully synthetic: This data does not contain any original data. This means that re-identification of any single unit is almost impossible and all variables are still fully available.
Partially synthetic: Only data that is sensitive is replaced with synthetic data. This requires a heavy dependency on the imputation model. This leads to decreased model dependence, but does mean that some disclosure is possible owing to the true values that remain within the dataset.
Hybrid Synthetic: Hybrid synthetic data is derived from both real and synthetic data. While guaranteeing the relationship and integrity between other variables in the dataset, the underlying distribution of original data is investigated and the nearest neighbor of each data point is formed. A near-record in the synthetic data is chosen for each record of real data, and the two are then joined to generate hybrid data.
Two general strategies for building synthetic data include:
Drawing numbers from a distribution: This method works by observing real statistical distributions and reproducing fake data. This can also include the creation of generative models.
Agent-based modeling: To achieve synthetic data in this method, a model is created that explains an observed behavior, and then reproduces random data using the same model. It emphasizes understanding the effects of interactions between agents on a system as a whole.
Deep learning models: Variational autoencoder and generative adversarial network (GAN) models are synthetic data generation techniques that improve data utility by feeding models with more data. Feel free to read in detail how data augmentation andsynthetic data support deep learning.
Challenges of Synthetic Data
Though synthetic data has various benefits that can ease data science projects for organizations, it also has limitations:
- Outliers may be missing: Synthetic data can only mimic the real-world data, it is not an exact replica of it. Therefore, synthetic data may not cover some outliers that original data has. However, outliers in the data can be more important than regular data points as Nassim Nicholas Taleb explains in depth in his book, the Black Swan.
- Quality of the model depends on the data source: The quality of synthetic data is highly correlated with the quality of the input data and the data generation model. Synthetic data may reflect the biases in source data
- User acceptance is more challenging: Synthetic data is an emerging concept and it may not be accepted as valid by users who have not witnessed its benefits before.
- Synthetic data generation requires time and effort: Though easier to create than actual data, synthetic data is also not free.
- Output control is necessary: Especially in complex datasets, the best way to ensure the output is accurate is by comparing synthetic data with authentic data or human-annotated data. this is because there could be inconsistencies in synthetic data when trying to replicate complexities within original datasets.
Machine Learning and Synthetic Data: Building AI
The role of synthetic data in machine learning is increasing rapidly. This is because machine learning algorithms are trained with an incredible amount of data which could be difficult to obtain or generate without synthetic data. It can also play an important role in the creation of algorithms for image recognition and similar tasks that are becoming the baseline for AI.
There are several additional benefits to using synthetic data to aid in the development of machine learning:
- Ease in data production once an initial synthetic model/environment has been established
- Accuracy in labeling that would be expensive or even impossible to obtain by hand
- The flexibility of the synthetic environment to be adjusted as needed to improve the model
- Usability as a substitute for data that contains sensitive information
2 synthetic data use cases that are gaining widespread adoption in their respective machine learning communities are:
Learning by real life experiments is hard in life and hard for algorithms as well.
- It is especially hard for people that end up getting hit by self-driving cars as in Uber’s deadly crash in Arizona. While Uber scales back their Arizona operation, they should probably ramp up their simulations to train their models.
- Real life experiments are expensive: Waymo is building an entire mock city for its self-driving simulations. Only a few companies can afford such expenses
To minimize data generation costs, industry leaders such as Google have been relying on simulations to create millions of hours of synthetic driving data to train their algorithms.
For more, feel free to check our article on synthetic data in computer vision.
Generative Adversarial Networks (GAN)
These networks, also called GAN or Generative adversarial neural networks, were introduced by Ian Goodfellow et al. in 2014. These networks are a recent breakthrough in image recognition. They are composed of one discriminator and one generator network. While the generator network generates synthetic images that are as close to reality as possible, the discriminator network aims to identify real images from synthetic ones. Both networks build new nodes and layers to learn to become better at their tasks.
While this method is popular in neural networks used in image recognition, it has uses beyond neural networks. It can be applied to other machine learning approaches as well. It is generally called Turing learning as a reference to the Turing test. In the Turing test, a human converses with an unseen talker trying to understand whether it is a machine or a human.
The tools related to synthetic data are often developed to meet one of the following needs:
- Test data for software development and similar purposes
- Training data for machine learning models
We prepared a regularly updated, comprehensive sortable/filterable list of leading vendors in synthetic data generation software.Some common vendors that are working in this space include:
|Name||Founded||Status||Number of Employees|
|CA Technologies Datamaker||1976||Public||10,001+|
|Deep Vision Data by Kinetic Vision||1985||Private||51-200|
|Delphix Test Data Management||2008||Private||501-1000|
|Informatica Test Data Management Tool||1993||Private||5,001-10,000|
These tools are just a small representation of a growing market of tools and platforms related to the creation and usage of synthetic data. For the full list, please refer to our comprehensive list.
Synthetic data is a way to enable the processing of sensitive data or to create data for machine learning projects. To learn more about related topics on data, be sure to see our research on data.
If your company has access to sensitive data that could be used in building valuable machine learning models, we can help you identify partners who can build such models by relying on synthetic data:
Find the Right Vendors
If you want to learn more about custom AI solutions, feel free to read our whitepaper on the topic:
Custom AI whitepaper
Also, you can follow our Linkedin page where we share how AI is impacting businesses and individuals or our twitter accountto learn more about the topic.
Cem founded the high tech industry analyst AIMultiple in 2017. AIMultiple informs ~1M businesses (as per similarWeb) including 55% of Fortune 500 every month.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech companies that reached from 0 to 3M annual recurring revenue within 2 years.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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Jun Mon, 2022 at 08:05
Hi I would like you to study our requirements for sythentic datasets. and help us.Reply
Jan Sun, 2022 at 13:31
Would be good to see Mindtech Global in your list, as they are one of the leading suppliers of visual synthetic data, and the only provider with a complete end-end platfrom.Reply
Jan Sun, 2022 at 21:20(Video) How is Synthetic Data used in Healthcare?
Hi Chris, thank you for the heads up! Could you please sign up @ https://grow.aimultiple.com so we can collect data on Mindtech Global and decide whether to feature it.Reply
Dr. Uwe Hilzenbecher
Oct Wed, 2021 at 17:08
An excellent contribution !Reply
Jul Thu, 2021 at 17:46
Very nice article. Simply and intelligibly written. All praise for the author.Reply
Nov Thu, 2020 at 04:40
Check out Simerse (https://www.simerse.com/), I think it’s relevant to this article. Cheers!Reply
Sep Mon, 2020 at 23:46
Hi everyone! I really enjoyed the article and wanted to share here this amazing open-source library for the creation of synthetic images. Flip allows generating thousands of 2D images from a small batch of objects and backgrounds.Reply
Oct Sat, 2019 at 23:50
The folks from https://synthesized.io/ wrote a blog post about these things here as well “Three Common Misconceptions about Synthetic and Anonymised Data”. https://blog.synthesized.io/2018/11/28/three-myths/. Perhaps worth citing.Reply(Video) Synthetic Data in Machine Learning: What, Why, How? | Mind the Data Gap by Synthesized, Ep 8
May Sat, 2020 at 09:39
Thanks for the suggestion!Reply
Synthetic data is information that's artificially manufactured rather than generated by real-world events. Synthetic data is created algorithmically, and it is used as a stand-in for test datasets of production or operational data, to validate mathematical models and, increasingly, to train machine learning models.
What is synthetic test data? Synthetic test data is 'fake/dummy' data that can be used for development and testing of applications. It is not based on real data or existing information: it is artificially created with the help of algorithms.
That's where synthetic training data comes in handy. Synthetic data is fake data that mimics real data. For certain ML applications, it's easier to create synthetic data than to collect and annotate real data.
Synthetic data is artificially annotated information that is generated by computer algorithms or simulations. Often, synthetic data is used as a substitute when suitable real-world data is not available – for instance, to augment a limited machine learning dataset with additional examples.
Synthetic data is considered a secure approach for enabling public release of sensitive data as it goes beyond traditional deidentification methods by generating a fake dataset that does not contain any of the original, identifiable information from which it was generated, while retaining the valid statistical ...
Synthetic data carries the ability to create fake patient records and fake medical imaging that is truly non-identifiable because the data does not relate to any real individual.
A synthetic feature is a combination of the econometric measures using arithmetic operations (addition, subtraction, multiplication, division). Each synthetic feature can be seen as a single regression model that is developed in an evolutionary manner.
Data augmentation is useful to improve performance and outcomes of machine learning models by forming new and different examples to train datasets. If the dataset in a machine learning model is rich and sufficient, the model performs better and more accurately.
- pip install Faker. To use the Faker package to generate synthetic data, we need to initiate the Faker class.
- from faker import Faker. fake = Faker() With the class initiated, we could generate various synthetic data. ...
- fake.name() Image by Author.
Synthetic data generation is the process of creating new data while assessing data utility. Explore generation techniques, generating in Python & best practices
Synthetic data is artificial data generated with the purpose of preserving privacy, testing systems or creating training data for machine learning algorithms.. Synthetic data generation is critical since it is an important factor in the quality of synthetic data; for example synthetic data that can be reverse engineered to identify real data would not be useful in privacy enhancement.. Though the utility of synthetic data can be lower than real data in some cases, there are also cases where synthetic data is almost as valuable as real data.. They should choose the method according to synthetic data requirements and the level of data utility that is desired for the specific purpose of data generation.After data synthesis, they should assess the utility of synthetic data by comparing it with real data.. There are three libraries that data scientists can use to generate synthetic data: Scikit-learn is one of the most widely-used Python libraries for machine learning tasks and it can also be used to generate synthetic data.
There are different types of synthetic data: media, text, and tabular. This article presents examples of companies using synthetic data, from Amazon’s Alexa to Google’s Waymo.
This post presents the different synthetic data types that currently exist: text, media (video, image, sound), and tabular synthetic data .. Amazon is using synthetic data to train Alexa's language system Google's Waymo uses synthetic data to train its self driving cars Health insurance company Anthem works with Google Cloud to generate synthetic data American Express & J.P. Morgan are using synthetic financial data to improve fraud detection Roche is using synthetic medical data for clinical research German insurance company Provinzial tests synthetic data for predictive analytics. Or they use fully synthetic data , with datasets that don't contain any of the original data.. Synthetic data can be:. Synthetic text Synthetic media like video, image, or sound Synthetic tabular data. You build and train a model to generate text.. Amazon's Alexa AI team, for instance, uses synthetic data to complete the training data of its natural language understanding (NLU) system.. The data science team modelled tabular synthetic data after real-life customer data.. They trained their machine learning models without compromising on the model performance or on their customer privacy.. Using synthetic data as a data protection method In general, all customer-facing industries can benefit from privacy-preserving synthetic data, as modern data procession laws regulate personal data processing.
Learn about the importance of synthetic data, its main use cases, how it can benefit computer vision projects, and methods for generating synthetic datasets.
Algorithms create synthetic data used in model datasets for testing or training purposes.. Synthetic data offers several important benefits: it minimizes the constraints associated with the use of regulated or sensitive data, it can be used to customize data to match conditions that real data does not allow, and it can be used to generate large training datasets without requiring manual labeling of data.. In this article Developers often require large, accurately labeled datasets when training AI models.. Autonomous vehicle algorithms trained with diverse synthetic data can produce safer computer vision for cars, accounting for a larger variety of rare real world events.. Even if a synthetic dataset is based on real data (for example, images of real people), it can preserve the relevant characteristics of the original data without using any identifiable information, eliminating the compliance risk.. Realism— synthetic data must accurately reflect the original, real-world data.. Data scientists must adjust the ML models to account for bias and ensure the synthetic dataset is more representative.. Privacy —some types of synthetic data are based on real world data.. To generate synthetic data, data scientists need to create a robust model that models a real dataset.. Several neural rendering algorithms are now available that address these challenges.
Compare 19 synthetic data generator products with objective metrics. Find products’ reviews, demand, maturity, satisfaction, customer insights & trends
There are 2 categories of approaches to synthetic data: modelling the observed data or modelling the real world phenomenon that outputs the observed data.. In areas where data is distributed among numerous sources and where data is not deemed as critical by its owners, synthetic data companies can aggregate data, identify its properties and build a synthetic data business where competition will be scarce.. As it aggregates more data, its synthetic data becomes more valuable, helping it bring in more customers, leading to more revenues and data.. Access to data and machine learning talent are key for synthetic data companies.. To achieve this, synthetic data companies aim to work with a large number of customers and get the right to use their learnings from customer data in their models.. Synthetic data companies build machine learning models to identify the important relationships in their customers' data so they can generate synthetic data.. Synthetic data companies need to be able to process data in various formats so they can have input data.. Synthetic data can not be better than observed data since it is derived from a limited set of observed data.. Any biases in observed data will be present in synthetic data and furthermore synthetic data generation process can introduce new biases to the data.. Deep learning is data hungry and data availability is the biggest bottleneck in deep learning today, increasing the importance of synthetic data.. Deep learning relies on large amounts of data and synthetic data enables machine learning where data is not available in the desired amounts and prohibitely expensive to generate by observation.. The only synthetic data specific factor to evaluate for a synthetic data vendor is the quality of the synthetic data.
This article explores synthetic data generation. What's the logic behind it, and how do you generate synthetic data with deep learning models like VAEs or GANs?
Key takeaways: Generating synthetic data comes down to learning the joint probability distribution in an original, real dataset to generate a new dataset with the same distribution.. Deep learning models such as generative adversarial networks (GAN) and variational autoencoders (VAE) are well suited for synthetic data generation.. As previously explained in Types of synthetic data and real-life examples , there are different synthetic data types: structured and unstructured.. To do so, we need to learn an approximated distribution or process compatible with the real data (i.e., a generative model) that can later be used to sample structurally and statistically comparable synthetic data .. With a simple table and very few columns, and none or few dependencies, a very simplistic model can be a fast and easy way to generate synthetic data.. Neural Networks (NNs) are well-fitted to simplify transformation problems because they are good at finding patterns in data.. At first, an encoder network transforms an original complex distribution into a latent distribution.. To generate synthetic dataset, you learn the joint probability distribution from real data by means of a generative model from which you sample new data.
Can AI-driven fitness apps, developed with synthetic data, pump-up your workout?
As more people use fitness apps to train and track their development and performance, fitness apps are increasingly using AI to power their offerings by providing AI-based workout analysis, incorporating technologies including computer vision, human pose estimation, and natural language processing techniques.. The Smart Fitness platform provides 3D-annotated synthetic visual data in the form of video and images.. For example, in cases of pose estimation analysis, an advantage the Smart Fitness platform provides is the capability to quickly simulate different camera types for capturing a variety of differentiated exercise synthetic data.. A trained convolutional neural network then processes these images to predict where the human actor’s joints are located in the image.. AI-based fitness apps generally use the device’s camera, recording videos up to 720p and 60fps to capture more frames during exercise performance.. The problem is, computer vision engineers need vast amounts of visual data to train AI for fitness analysis when using a technique like pose estimation.. While an acceptable level of accuracy in 2D pose estimation has already been reached, 3D pose estimation lacks in terms of generating accurate model data.. In addition, he explained, the Smart Fitness platform optimizes your ability to capture millions of substantial visual exercise data, eliminating the repetitive burden of capturing each element in person.. However, he says it will see growing adoption for use cases for which data must be guaranteed to be anonymous or privacy must be preserved (such as medical data); augmentation of real data, especially where costs of data collection are high; where there is a need to balance class distribution within existing training data (such as with population data), and emerging AI use cases for which limited real data is available.. Several of these use cases are key for Datagen’s value proposition.. “I would say that adding these visual capabilities to these fitness apps, especially as people exercise more in their own home, will definitely improve things significantly.
Build Custom Synthetic Data Generation Pipelines with Omniverse Replicator | NVIDIA Technical Blog ›
Overcome data challenges and build high-quality synthetic data to accelerate the training and accuracy of AI perception networks with Omniverse Replicator, available in beta.
Omniverse Replicator is a highly extensible SDK built on a scalable Omniverse platform for physically accurate 3D synthetic data generation to accelerate training and performance of AI perception networks.. Omniverse Replicator provides an extraordinary platform for developers to build synthetic data generation applications specific to their neural network’s requirements.. Training a DNN for perception tasks often involves manual collection of data from millions of images followed by manually annotating these images and optional augmentations.. Omniverse Replicator for large-scale training data generation with accurate annotations. For example, once synthetic data is generated, developers can train their AI models quickly using the NVIDIA TAO Toolkit .. Omniverse Replicator and TAO toolkit workflow for synthetic data generation and model training. When data required to train deep learning models is unavailable, Omniverse Replicator generates synthetic data, which can be used to augment limited datasets.. Lightning AI (formerly Grid.AI) uses the NVIDIA Omniverse Replicator to generate physically accurate 3D datasets based on Universal Scene Description (USD) that can be used to train these models.. The Omniverse Replicator SDK provides a core set of functionalities for developers to build any domain specific synthetic data generation pipeline using all the benefits offered by the Omniverse platform.. With Omniverse as a development platform for 3D simulation, rendering, and AI development capabilities, Replicator provides a custom synthetic data generation pipeline.