The emergence of big data has opened up numerous possibilities for businesses to tap into this vast potential. Data-driven AI (artificial intelligence) and ML (machine learning) initiatives can help companies scale faster and expedite their time to market.
However, training AI models requires access to lots of accurate datasets. Moreover, there are several limitations when accessing real-world data.
These constraints range from the difficulty in obtaining large datasets to the increased emphasis being placed on data protection and privacy laws. Faced with the prospect of violating compliance/privacy laws and racking up massive fines, companies prefer not to leverage the available data and, in the process, lose out on business opportunities.
In order to get around issues of privacy and compliance, organizations have begun using synthetic data.
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What is Synthetic Data?
Synthetic data is data that is artificially generated by ML algorithms to create datasets that closely resemble the original data. It is a cheap and cost-effective method that enables researchers to generate massive datasets effortlessly.
To create synthetic data, specific machine learning frameworks are often used, the most common of which is generative adversarial networks (GANs).
In this deep learning model, two neural networks—the generator and the discriminator—are pitted against each other to produce synthetic data. The generative models learn from real datasets to produce synthetic data similar to real data but have no one-to-one relationship with actual facts.
Other ways to generate synthetic data are using variational autoencoders (VAEs) and autoregressive models. While VAEs use encoding and decoding patterns, the autoregressive model uses a set of input and output patterns to generate synthetic data.
Why Do We Need Synthetic Data?
There are multiple reasons for adopting synthetic data. First, businesses spend heavily on collecting and analyzing real data. With synthetic data, companies can drastically reduce information acquisition costs. Further, synthetic data plays a significant role in training AI models, especially for use cases where actual data is unavailable.
Another reason to use synthetic data is when datasets contain personal information like a person’s name or social security number. While this can be avoided by adopting techniques such as non-personally identifiable information (non-PII) and anonymizing data, there is always a risk of re-identification.
To prevent this from happening, you can derive synthetic data from real-world data with the help of an artificial data generation tool. Doing so will safeguard the properties of the actual data and allow you to use data without the risk of compromising personal information.
Benefits of Synthetic Data
Benefits of using synthetic data include:
- Cost: Collecting real data is time-consuming and prohibitive, while producing data through a synthetic data generation software is far cheaper, quicker, and more efficient.
- System Testing: High-quality test data is crucial for production-worthy data; however, prohibitive regulations can limit comprehensive datasets. An alternative is to generate synthetic data that addresses all your needs in system training.
- Speed: Requesting access to sensitive datasets can be a time-consuming process that is met with a lot of barriers. With synthetic data, companies don’t have to go through the bureaucratic processes of acquiring real data; instead, they can create data whenever required in a much quicker time frame.
- Ensures Data Privacy: One of the most important benefits of synthetic data is that it does not involve the use of any sensitive data. Since all personal info has been removed, there is no way the data can be traced to a real person.
- Easy Access: In certain use cases, real-world data may not be available, while in other cases, there may not be enough data to train models properly. The only way to get data in such scenarios is to generate it synthetically.
Challenges of Synthetic Data
Although synthetic data is beneficial in many ways, it comes with its fair share of limitations.
- Use of Common Trends: While synthetic data can mimic real data, it cannot completely reconstruct the original data. As it concentrates mainly on common trends, there is a possibility that it might miss out on corner cases present in the original data.
- Data Biases: Synthetic data is only as good as the source data from which it has been derived. If biases are present in the source data, then the synthetic data will present you with a biased outcome.
- User Acceptance: Since synthetic data is a new field, many users have concerns about it. So, user acceptance is an issue that organizations often deal with when it comes to synthetic data.
Also read: Top Data Modeling Tools 2022
Use Cases of Synthetic Data
Synthetic data finds use in multiple industries, including finance, healthcare, automotive, and manufacturing.
Autonomous vehicles are one of the notable success stories of synthetic data. Companies need to acquire and label massive amounts of actual data for designing self-driving cars accurately.
However, collating live datasets of real cars in action is expensive and time consuming. With the help of synthetic data, developers can test self-driving cars realistically on simulated roads, safely, quickly, and more efficiently.
When a company manufactures a new product, there may not be enough datasets available for training models. Synthetic training data is the quickest and most effective way to address this problem. By training their models on synthetic datasets that cover all possible scenarios, companies can spot quality problems earlier.
Financial institutions have a wealth of customer data they can use to gain better insights. Yet, they are wary of using the available data because of the harsh compliance laws and privacy requirements. Synthetic data enables these institutions to copy the statistical properties of the original datasets without putting customer privacy at risk.
Synthetic data is commonly used in healthcare, as it lets researchers use record data without worrying about the legal complications of infringing on patient confidentiality. Further, researchers can also extrapolate synthetic data to conduct advanced studies where no real clinical data exists.
Can It Help Improve Bias in AI?
As much as we want a bias-free world, the truth is that AI systems are guilty of propagating unintended biases. Synthetic data offers an opportunity to eradicate prejudices from AI algorithms and move toward fairer AI that accurately represents the world.
However, the problem is not with the AI system but with the data fed into the system. To solve that, researchers would need to study the biases in existing data. Then, they can retrain the system ensuring that assumptions and prejudices do not creep into the newly created synthetic data. The new dataset that is now created will conform to the predetermined definition of fair AI.
Growing Use of Synthetic Data
Gartner estimates that by 2024 60% of the data used by AI projects will be synthetic data. Synthetic data is an emerging field which will grow as privacy policies become more stringent in nature.
Synthetically generated data enables businesses to gain actionable insights, shorten their time to production, and become more agile. With the immense benefits that it offers, it is a matter of time when synthetic data supplants actual data in AI models.
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