How To Use Synthetic Data To Overcome Data Shortages For Machine Learning Model Training
It takes time and considerable resources to collect, document, and clean data before it can be used. But there is a way to address this challenge – by using synthetic data.
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The essence of artificial intelligence lies in data. If we don’t have sufficient data, we cannot train the models to do what we want, and our expensive and powerful hardware becomes useless.
But procuring relevant, accurate, legitimate, and reliable data is easier said than done. That’s because the data collection process is often just as complex and time-intensive as setting up the actual machine learning models.
Moreover, it also takes time and considerable resources to collect, document, and clean data before it can be used. But there is a way to address this challenge – by using synthetic data.
What Is Synthetic Data?
Unlike actual data, synthetic data isn’t collected from real-world events but is generated artificially. This data is made to resemble a real dataset. You can use this synthetic data to detect inherent patterns, hidden interactions, and correlations between variables.
All you need to do is design a machine learning model that can comprehend how the real data behaves, looks, and interacts. Then, the model can be queried, and millions of additional synthetic records can be generated that act, look, and feel like the real data.
Now, there isn’t anything magical about synthetic data, but if you want to create high-quality synthetic data, you need to follow a few basics. For instance, your synthetic data will be based on the actual data used to make it. So, if you use poor-quality data points, you can’t expect it to deliver a high-quality synthetic dataset.
To expect high-quality data as your output, you need high-quality data as your input. Besides that, it should also be plentiful, so you have enough input to expand your existing dataset with first-rate synthetic data points.
Causes of Data Shortages
To realize how synthetic data can help you overcome data shortage, you need to understand the reasons behind data shortage in the first place. This is one of the two biggest problems with data.
For starters, available data may be insufficient for AI/ML models. Due to data privacy laws, enterprises need explicit permission to use sensitive customer data. And it can be challenging to find enough users, customers, or employees who would agree to let their data be handed over for research purposes.
Besides this, machine learning models cannot integrate with dated data. The reason is that they require new trends or must be able to respond to novel processes, technologies, or product features. So, historical data is of little to no use, limiting the data collection.
Moreover, the sample sizes are usually small due to the nature of the data itself. For instance, you might only get data once a month for a customer price index in a model that measures the sensitivity of stock prices. Even if you go for historical data, you would only obtain 600 records for 50 years of CPI history - clearly, a very small data set.
In some instances, the effort to label data is not cost-effective or timely. For example, to predict customer satisfaction and measure customer sentiment, the ML model needs to manually inspect many text messages, emails, and service calls, but that requires an excessive number of hours, the time you may not have.
However, with the help of synthetic data, ML can easily label data without any issues.
Why Is Synthetic Data Useful?
Consider that you have a data collection with very little info on some of the variables, making it difficult to make a prediction. You are also limited from obtaining more data for this marginal class. In this case, synthetic data can come to the rescue. It will allow you to synthesize more data points for the marginal class and balance your model, increasing performance.
Imagine a task involving a prediction about whether a piece of fruit is an orange or an apple just by understanding the characteristics of both fruits, such as their shape, color, seasonality, etc. For that, you have 500 samples for oranges and 3,000 samples for apples. So, when the ML model tries to analyze the data, its algorithm is expected to be automatically biased towards apples due to the enormous class imbalance. Hence, the model will be inaccurate and deliver you unsatisfactory performance.
The only way you can address this imbalance is by using synthetic data. Generate 2500 more samples for oranges to give the model enough data to not be biased toward either of the fruits. The prediction will be more accurate now. In the same way, you can also effectively use this model to manage the online reputation of your business as you can sort the good online reviews from the bad ones.
Also, be careful when choosing the suitable ML algorithm to ensure that the model works accurately.
Consider that you have a dataset that you want to share. The problem here is that it includes sensitive data such as personally identifiable information (PII), social security numbers, full names, bank account numbers, etc. Since privacy is of utmost importance, this data must be guarded carefully. Now, suppose you wish to analyze this data or build a model. But since you’re dealing with PII, you won’t be able to share this data with a third party without bringing in legal teams, using only non-personally identifiable information, anonymizing data, implementing secure data transfer processes, and much more.
So, there will be months of delay since you won’t be able to share the data immediately. Here, synthetic samples can come in handy again. You may acquire these samples from the real dataset.
This synthetic data can be shared with a third party without any trouble. Moreover, you won’t risk your data privacy being invaded and personal information leaks. It can be especially useful for services that offer HIPAA database hosting and data storage.
New Advances In Synthetic Data
Synthetic data can fill the gaps in your data and make it complete to enable you to produce a large amount of safe data. The generated data helps enterprises stay compliant and maintain the data balance. Moreover, with the recent innovation in technology, you can produce data with improved accuracy. This has made synthetic data even more valuable in delivering the missing data required by the machine learning models.
It has also been successfully used to enhance image quality. Moreover, the generative adversarial network (GAN) models advance the precision of synthesized tabular data.
Another recent advancement in creating synthetic data is Wasserstein GAN or WGAN. The critic neural network tries to find the minimal distance between the distribution observed in produced samples and the distribution of the data observed in the used training set. Then, WGAN trains the generator model to generate more realistic data. To predict the probability of the realness of the generated images, it keeps the score of an images’ realness instead of utilizing a discriminator.
Unlike GANs, WGAN does not pursue stability by looking for an equilibrium between two contrasting models. Instead, the WGAN seeks a junction between the models. As a result, it generates synthetic data with features more similar to real life.
With technological advancement and innovation, synthetic data is becoming richer, more diverse, and closely aligned to real data. Hence, using synthetic data to overcome data shortage will become more likely over time as you can generate and use synthetic data more efficiently. Synthetic data will also help maintain user privacy and keep enterprises compliant while also enhancing the speed and intelligence of ML models.
Nahla Davies is a software developer and tech writer. Before devoting her work full time to technical writing, she managed—among other intriguing things—to serve as a lead programmer at an Inc. 5,000 experiential branding organization whose clients include Samsung, Time Warner, Netflix, and Sony.