Synthetic data is a replacement for the real thing when researchers and scientists can't get their hands on it because of confidentiality or privacy concerns. In simple words, synthetic data is a replacement for real data.
With the growing demand for artificial intelligence, there's no way we can produce enough real-world examples on our own. As a result, the need to create synthetic data has never been more pressing than it is now.
What is Synthetic Data?
Synthetic data is artificial data that is created through algorithms. It simulates the statistical properties of real people but without revealing any identifying information. Businesses across
various industries need synthetic data for three major reasons:
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Training machine learning algorithms
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Product testing
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Privacy
Artificial intelligence is becoming more and more advanced, with even the most complicated tasks being handled by these computer systems. For us humans who work in a less sophisticated field than AI development to make sense out of what they're doing sometimes, we need help from "synthetic" data which reflects real-world events mathematically or statistically but does not come directly from any person.
Synthetic data is a bit like diet soda. To be effective, it has to resemble the “real thing” in certain ways. Diet soda should look, taste, and fizz like regular soda. Similarly, a synthetic dataset must have the same mathematical and statistical properties as the real-world dataset it's standing in for. It looks like it, and has formatting like it.”
Kalyan Veeramachaneni, principal investigator of the Data to AI (DAI) Lab and a principal research scientist in MIT’s Laboratory for Information and Decision Systems
What are the Best Synthetic Data Generation Techniques?
Different methods of data synthesis can be used to generate synthetic data. Businesses should choose a method according to the requirements and level desired for specific purposes in generating this type of information, such as decision tree learning versus deep-learning techniques. Let’s learn more about some of the best synthetic data generation techniques.
Variational Autoencoder (VAE) and Generative Adversarial Network (GAN)
Variational Autoencoder (VAE) and Generative Adversarial Network (GAN) are deep generative models. VAE is a method of encoding data so that it can be transmitted more efficiently. The encoder creates an output that represents the original input, and then there's training on how well those two match up to each other through optimization methods.
On the other hand, the GAN model is an artificial intelligence technique that takes data from real-world events and generates synthetic ones. This process happens in two parts: generators create this new information, using something like
machine learning or statistical analysis, and discriminants compare these synthetically created datasets with their original versions based on certain conditions set before training begins.
Monte Carlo Method
It has been said that if you have the data, then gambling on its distribution can be an effective means of generating synthetic samples. This is because businesses can determine what their best-fit model should look like by considering both observed information as well as potential outcomes based on different models or theories about how this particular set might behave; once they know these parameters it becomes possible for them to use Monte Carlo synthetic data generation method.
According to Dataset Distribution
For cases where real data does not exist but an analyst has a comprehensive understanding of how dataset distribution would look like, they can generate a random sample from any given distribution such as Exponential, Lognormal, Uniform, Chi-Square, Normal or t-distributions.
Scikit-Learn
Scikit-Learn is a method of synthetic data generation using Python, one of the most popular languages in the field of data science. Scikit-Learn is one of Python's three libraries that can be used to generate synthetic data. This powerful Python tool can be used for machine learning tasks like regression, classification and clustering. It also allows you to generate synthetic data if there's no real-world example of your researched topic.
Best Tools for Synthetic Data Generation
Generating synthetic data includes two steps: Data preparation and data synthesis. Data preparation is necessary before data synthesis. While several vendors can perform these two steps, we have compiled the three, best synthetic data generation tools for all:
Hazy
Hazy offers unique models capable of generating high-quality synthetic data with a differential privacy mechanism. Data can be tabular, sequential (containing time-dependent events), or dispersed across several tables in relational databases.
Edgecase.AI
Edgecase.AI takes the need for large-scale data annotation and generation of training images/videos to a whole new level with its proprietary platform. It helps solve fundamental problems in multiple industries like security, industry 4.0, healthcare, agriculture and retail.
MOSTLY AI
MOSTLY AI offers the leading synthetic data platform, which enables enterprises to unlock, share and fix their data. Thanks to its advanced artificial intelligence system, MOSTLY AI promises unmatched synthetic data accuracy while maintaining granular-level information.
Frequently Asked Questions
Which business functions can consider using synthetic data for improved overall efficiency?
The benefits of synthetic data are many. Data is an incredibly important component in all areas and can help us continue innovating new products, services or solutions when the necessary information isn't readily available. The following business functions can make the most of synthetic data for the best outcomes:
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Human resources
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DevOps
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Agile Development
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Machine Learning
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Marketing
What are the key benefits of synthetic data?
Synthetic data has many benefits over real data. For example,
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It focuses on multivariate relationships
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It is immune to several, common, statistical problems
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It overcomes restrictions that may arise while using real data
What are the main limitations of synthetic data?
Even though synthetic data has many advantages, there are some challenges around it as well. For example,
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It requires output control
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It is time-consuming
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It is yet to gain significant user acceptance
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Data quality depends heavily on data source