Data has become many organizations’ most valuable resource. Machine learning (ML) is shifting from a fringe technology used exclusively by top innovators to a mainstay of modern business, making data an indispensable tool. However, many data and machine learning projects struggle to reach their full potential.
Businesses grapple with ML projects that deliver inaccurate results, largely because of problems with the data itself. While most businesses today understand the need to capitalize on data through machine learning, fewer realize how their data practices hinder these outcomes. Using synthetic data can help overcome many of these current shortcomings.
What Is Synthetic Data?
As the name suggests, synthetic data is artificially generated information. It follows the same rules and reflects real-world concepts and trends, but it doesn’t come from a real-world source. Like original data, it can also come in various forms, from plain text to tabular information to visual or audio media.
Synthetic data falls into three main categories:
- Fully synthetic data
- Partially synthetic data
- Hybrid data
Fully synthetic data refers to datasets that are 100% artificial. It may be based on original datasets but contains no real-world information or context. Partially synthetic data is original information with some fields replaced with synthetic substitutes, usually to reduce data breach risks. As you might expect, hybrid data refers to datasets that use a mixture of original and synthetic data.
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Benefits of Synthetic Data
Leading tech companies like Google and Amazon use synthetic data in their ML applications, and more organizations are migrating that way. Of course, popularity alone isn’t a sufficient reason to embrace a trend, so here are some advantages of using synthetic data.
While it may seem counterintuitive, sometimes using real-world data produces less accurate results than synthetic information. That comes down to one of ML’s most significant challenges: bias.
Original datasets are prone to human bias. Historical and deep-seated implicit biases can seep their way into real-world information through how people collect, record, and organize it without data scientists realizing. This problem is so pervasive that studies suggest up to 38.6% of the data in popular AI databases is biased.
Synthetic data provides a way around issues like historical misrepresentation that could lead to bias. Consequently, using it in ML models can deliver more accurate results despite the information not necessarily coming from the real world. This can also produce more appropriate and fair customer-facing AI applications.
Synthetic data can also make it easier to attain enough information to train effective ML models. Reliable ML algorithms typically require extensive datasets, but not every company has ready access to enough relevant data. Synthetic data provides a way around that issue, as businesses can generate a lot of it without length collection processes.
This can happen in one of two ways. First, teams could use entirely synthetic datasets. Alternatively, they can use techniques like synthetic minority oversampling, which creates dummy data based on real information to fill in the blanks in that original dataset.
These strategies are particularly useful for businesses or ML applications with limited available real-world data. A lack of information availability no longer has to be a barrier to effective ML implementation.
Similarly, synthetic data can also help teams complete ML projects in less time. According to a 2020 study, a third of enterprises say it takes them between one to three months to implement an ML model. Another 24% take even longer, and these figures don’t even include training and data collection time.
With such lengthy average deployment times, businesses must streamline data collection and training as much as possible to minimize project expenses. Synthetic data is an ideal answer, as it can provide enough information in a fraction of the time.
Synthetic data means teams don’t have to spend nearly as much on data collection and organization. Depending on how they generate it, they can also create it in an already standardized format, streamlining preparation too. This efficiency can make ML projects a more cost-effective investment.
Another advantage of synthetic data in ML is how it mitigates data breach risks. Because ML projects typically store considerable amounts of data in one place, they can carry significant cybersecurity risks. Synthetic data lowers those concerns by replacing sensitive information.
If an ML project uses real-world data, especially personally identifiable information (PII), a breach could be devastating. Enterprises could face lost business and legal damages on top of remediation costs. Conversely, if synthetic data leaks, it’s not as pressing an issue as it doesn’t reveal any real-world PII.
Considering how data breach costs have reached an all-time high of $4.34 million in 2022, this security is a crucial advantage. It’s particularly important for ML applications that deal with sensitive information like PII.
How to Capitalize on Synthetic Data in ML
Synthetic data has many advantages for ML developers. However, like any other resource, its efficacy depends on how teams use it. With that in mind, here are some synthetic data best practices.
Understand When to Use Synthetic Data
The first and arguably most important consideration for synthetic data is determining when it’s the most appropriate choice. While synthetic datasets provide many benefits over original data, it’s not always what teams need.
Enterprises should review their ML goals to see if it’s important to have real-world information. Generally speaking, synthetic data is ideal for testing “what if” scenarios, when real-world data is limited or imbalanced, or privacy is a major concern. Alternatively, original data may be a better fit for digital twins, when outliers are particularly important, or when real-world information is readily available.
In some cases, it may be best to use hybrid datasets. Teams must determine their goals and restraints to understand which strategy is best for their specific ML project.
Clean and Prepare Data Before Generation
It’s also important not to overlook data preparation and cleansing, even with synthetic information. Poor-quality data costs businesses $15 million annually on average, and 60% of companies don’t even know how much bad data costs them. To avoid these costs, teams must prepare their synthetic data before using it.
While synthetic data can generate in already standardized formats, errors can still happen. Teams must review these datasets to ensure they’re clean and organized before using them to make the most of their synthetic information.
Basing synthetic data on high-quality original information can help. The better the source, the better the dummy information will be, reducing cleansing and preparation time.
Determine the Best Generation Method
Businesses should also understand that different data generation methods have varying strengths and weaknesses. Comparing these to find the best option is just as important as deciding between synthetic and original data.
Variational autoencoders (VAEs) can generate complex datasets efficiently and are easy to implement, but they struggle to provide consistent quality across all types with complex original datasets. Alternatively, generative adversarial networks (GAN) work well with unstructured or complex original datasets but are more challenging to train and implement.
Sometimes, it may be best to outsource synthetic dataset generation. These options are expanding, with more than 70 vendors providing synthetic data in 2021. Teams should review their in-house expertise, budgets, and needs to determine the best way forward.
Synthetic Data Can Unlock ML Projects’ Potential
Using ML to its fullest potential requires large, reliable, and secure datasets. In many cases, synthetic data can help provide that while minimizing complications with original information.
ML developers should consider how synthetic data could improve their projects. Capitalizing on this resource could lead to considerable accuracy, efficiency, security, and financial benefits. This, in turn, will make ML a more worthwhile endeavor for many enterprises.
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